Hands up who’s bored of seeing and reading “SEO is dead”. Whilst I don’t believe that to be the case, it’s easy to see why AI tools and companies are leaning into that narrative to position their products. But in reality, it’s simply not true. Search has always been a resource for solving queries. How to? Can I? Where is? What is the best? From early keyword matching to semantic search and voice, search engines have continuously evolved to better interpret human demand and intent. Since the launch of the internet, technology companies have monetised that demand and intent within what we define at Logixx as the Global Search & Discovery Economy. From the earliest days, users have moved across platforms, sources and environments to find information, validate choices and make decisions. What has changed is not the existence of that economy, but its shape and the expectations placed on it. Discovery has diversified. Search engines are no longer the only entry point. Social platforms, marketplaces, forums, voice assistants and content ecosystems have all expanded how and where users find information, products and services. At the same time, interaction has evolved. Users are no longer limited to entering queries and reviewing results. They can now engage in more fluid, responsive and iterative ways of exploring information. The expectation is shifting, not away from search entirely, but beyond it. It’s similar to a moment in Toy Story where Buzz Lightyear arrives, packed with new features and capabilities, and everything changes. Woody, the familiar and trusted cowboy, is still central, still valuable, but no longer the only way the world is experienced. That is where search engines find themselves today. Not replaced, but recontextualised within a broader discovery ecosystem where new interfaces are redefining how users interact with information. And just like Woody, SEO remains highly relevant. SEO is not dead. But it is now operating within a more complex, more competitive Global Search & Discovery Economy where visibility is no longer defined by search alone, but by how effectively a brand is understood, validated and surfaced across it. From Sifting to Directing Let’s face it, traditional search requires effort. A user enters a query, scans results, navigates paid ads integrated into the organic results, clicks through links, evaluates relevance, and refines their search again. Even at its most efficient, the model relies on users to sift through information. AI is accelerating the evolution of search and discovery. Instead of navigating a fixed set of results, users can ask more naturally, combine multiple questions into a single interaction, and shape the direction of discovery in real time. This is not just a shift in format, but a shift towards more human, conversational discoverability. The user is no longer constrained by the structure of results pages. They can explore, refine and redirect based on what matters to them, rather than what is presented to them. Discovery becomes more fluid, more iterative and more user led. What previously required multiple searches, comparisons and decisions can now evolve within a single interaction, shaped continuously by the user. At present, many of these experiences are also ad free, further contributing to a simpler user centric experience.  The implication is significant. AI does not just compete with search engines, it changes the balance between effort and control. Users move from sifting to directing, from searching to interacting, and from following results to shaping outcomes. As control increases and friction reduces, behaviour naturally follows. The Redistribution of Visibility SEO has never been limited to what sits on a website. Strong practitioners have always understood that visibility is shaped by a wider mix of signals: technical accessibility, content relevance, internal architecture, backlinks, citations, brand authority, reputation, and the broader digital footprint surrounding a business. What is changing is not the existence of those signals, but how they are weighted, interpreted and applied within the Global Search & Discovery Economy. In traditional search, the website remained the primary destination, even when off site signals helped determine whether it earned visibility. External authority supported rankings, but the end point was still usually a page, a click, and a visit. AI changes that dynamic. Discovery is increasingly influenced by systems that do not simply rank destinations. They interpret, compare, synthesise and present information across multiple sources before a user ever reaches a site, if they reach one at all. That includes search engines evolving their own AI layers, voice assistants, smart devices, browsers, marketplaces, social platforms, forums, reviews, editorial coverage, and conversational AI platforms embedded into everyday workflows. The shift, then, is not from on site to off site. It is from ranking driven visibility to interpretation driven visibility. That matters because the digital footprint around a brand now plays a more direct role in how that brand is described, framed and surfaced. Reviews, PR coverage, forum conversations, product feeds, business listings, structured data, and consistent entity signals are not just supporting factors around a ranking model. They are increasingly used as direct inputs into the answer layer itself. This is where the relationship between SEO, PR, content, and reputation management tightens significantly. A strong website still matters enormously. It remains the most controllable environment for defining products, services, expertise, and brand meaning. But it now sits within a broader discovery architecture where external corroboration, entity consistency, and machine readable context influence whether a brand is confidently retrieved and accurately represented. For experienced SEO practitioners, that is the more important shift to understand. AI does not change the fundamentals of visibility. It changes how they influence selection. Why SEO Practitioners Are the Natural Owners of This Shift (For Now) For all the change in interfaces, platforms and behaviour, the mechanics of discoverability have not disappeared. They have become more visible. SEO has always operated across multiple layers. Not just content and keywords, but structure, relationships, authority, consistency and how signals align across the wider ecosystem. What AI changes is not the existence of those layers, but how directly they shape outcomes. Systems are no longer just ranking pages. They are interpreting entities, reconciling sources and selecting information based on confidence, consistency and context. This is already visible in behaviour. According to Mckinsey , over 50% of consumers have used AI-powered search experiences, while traditional search still processes over 14 billion queries per day . The shift is not replacement, but redistribution. AI is absorbing research, comparison and decision-led queries where structured, synthesised answers provide a clear advantage. For practitioners, this reinforces something important. The same disciplines that influenced rankings now influence retrieval. Structured data defines entities and relationships. Internal architecture reinforces how topics connect. External validation through PR, reviews and mentions strengthens how confidently a brand can be resolved across sources. This is why reputation and third-party consistency are becoming more critical. AI systems do not rely on a single source of truth. They reconcile multiple inputs. If those inputs are inconsistent, confidence drops. If they align, retrievability increases. That is the opportunity. But it also introduces a structural risk. As systems become more capable of structuring, interpreting and connecting these signals independently, the mechanics of optimisation become less visible. Not because they disappear, but because they are increasingly handled within the systems themselves. The Agentic Future and the Compression of SEO Practices The next phase of this evolution is not just AI-assisted discovery.It is discovery that reduces the distance between intent and decision. Systems are becoming better at interpreting intent, selecting relevant sources, validating options and presenting outcomes in a way that requires fewer steps from the user. The data already points in this direction. AI driven discovery traffic has grown over 500% YoY and in AI-led environments, up to 90%+ of interactions end without a click . Even within traditional search, more than 50% of queries result in zero-click behaviour . This changes the nature of visibility. In a ranking model, visibility was position-based. In this emerging model, visibility is selection based. Content is not just surfaced, it is interpreted, extracted and used. This is where retrieval becomes critical. Content must be structured so it can be confidently lifted, summarised and cited without distortion. Entity clarity, schema, internal relationships and external corroboration all contribute to whether a system can rely on that content. At the same time, the optimisation process itself becomes more compressed. Many of the tasks that defined SEO execution are increasingly being automated or embedded within platforms. Content structuring, linking, and even aspects of optimisation are being handled continuously rather than manually. The impact is already measurable. Organic CTR can drop by up to 60% when AI summaries are present , even when rankings remain unchanged. This reinforces a shift away from traffic as the primary indicator of success. This does not remove the need for SEO practices, but it changes where value is created. Optimisation is no longer just about influencing rank. It is about influencing how systems interpret, validate and select information within environments where decisions are increasingly shaped before a click occurs. 10 Things SEO Practitioners Must Understand About AI in 2026 Structured Data Increases Your Probability of Being Selected, Not Just Understood AI systems do not read pages in isolation. They construct responses by extracting and reconciling signals across multiple sources, selecting information they can interpret with the highest confidence. Structured data helps define entities, attributes and relationships in a way that makes content easier to select and reuse within those responses. This matters because, as highlighted by McKinsey, a brand’s own website often contributes only around 5–10% of the sources used in AI-generated answers , meaning visibility is increasingly determined by how well your information is structured and aligned across a wider ecosystem, not just how well it ranks. Entity Clarity Determines Whether You Are Retrieved at All AI systems resolve brands, products and topics as entities, not keyword variations. They cross reference multiple sources to validate what something is, how it is described and how it relates to other concepts. If those signals are inconsistent, the system’s confidence drops and so does the likelihood of inclusion. McKinsey’s research shows that different AI platforms return different sources depending on context, reinforcing that consistent entity definition across your site, PR coverage and third party platforms is critical to being retrieved at all. Content Architecture Now Influences How Easily You Can Be Used AI does not just surface pages, it extracts meaning. Content that is modular, well structured and clearly segmented is easier to interpret, summarise and reuse. In contrast, long, unstructured pages create friction for systems trying to assemble answers. This aligns with the growing shift towards AI native content formats, where clarity, hierarchy and extractability determine whether content is included in generated responses, not just whether it ranks. Internal Linking Has Become a Signal of Semantic Understanding Internal linking is no longer just about crawl efficiency. It helps define how topics connect, effectively shaping how your site is interpreted as a knowledge graph. When AI systems attempt to understand a subject area, strong internal relationships reinforce context and improve answer completeness. This is why AI native SEO guidance increasingly emphasises topic clustering and interconnected content rather than isolated pages. Interpretability Has Overtaken Crawlability as the Baseline Being indexed is no longer enough. Content must be structured in a way that can be clearly understood, categorised and confidently reused by AI systems. As AI generated summaries reduce reliance on traditional click based journeys, content that cannot be easily interpreted risks becoming effectively invisible, even if it technically ranks. This reflects a broader shift away from access as the primary constraint, towards understanding as the limiting factor. Third party content is now a source of truth, not just a source of authority In traditional SEO, third party content primarily influenced visibility through backlinks and authority signals that supported rankings. In AI driven discovery, those same sources are used directly to construct answers. Reviews, editorial coverage, forums and independent platforms are interpreted, compared and cited as reference points within AI generated responses. This means backlinks remain valuable for search, but they are not essential for AEO and GEO in the same way. What matters is whether your brand is consistently represented across the sources AI systems trust when forming answers. Research into AI driven search behaviour shows that these systems increasingly rely on a broader mix of third party content, including reviews and user generated sources, to generate responses rather than relying on a single domain. Brand Strength Increases Retrieval Confidence Across Systems Recognisable brands are easier for AI systems to resolve, validate and prioritise. Strong brand presence across multiple trusted sources increases the likelihood of being selected within generated responses. At the same time, McKinsey notes that even well known brands can be absent from AI answers if their presence is not consistently reinforced across the sources these systems rely on, making brand strength necessary but not sufficient on its own. Retrieval Optimisation Now Sits Alongside Ranking Optimisation Ranking determines whether you are visible within a list. Retrieval determines whether you are included within an answer. Content must now be structured so it can be extracted, summarised and cited without losing meaning. This shift is reflected in user behaviour, with AI driven discovery growing rapidly and users increasingly engaging with synthesised responses rather than navigating multiple links. Visibility Must Be Measured Beyond Rankings and Traffic Traditional metrics such as rankings, impressions and clicks no longer capture the full picture of visibility. As more interactions happen within AI generated responses, practitioners need to understand where and how their brand is being referenced, cited or included. Industry guidance now points towards tracking presence across AI outputs and conversational interfaces, rather than relying solely on traffic as a proxy for performance Zero Click Is Now the Default State of Discovery Users are increasingly receiving answers without leaving the platform. With over 50% of traditional searches already ending without a click and AI-driven experiences pushing this significantly higher , visibility is shifting from driving traffic to being included within the response itself. This fundamentally changes the role of SEO, from generating visits to influencing outcomes earlier in the decision-making process Conclusion AI driven discovery will continue to grow, particularly in research and decision led use cases. With over half of consumers already having used AI powered search experiences, this behaviour is becoming embedded rather than experimental. Search engines will evolve rather than decline. Their scale remains unmatched, but their interfaces will continue to shift towards integrated AI experiences where ranking, retrieval and synthesis operate together. Visibility will become increasingly probabilistic. There will be no single position to optimise for. Different systems will return different outputs based on context, sources and interpretation. Success will be defined by the likelihood of being selected, not the certainty of ranking. Reputation will move further into the core of discovery. Reviews, editorial coverage and third party validation will shape how brands are described within AI generated responses, not just whether they are discovered. Backlinks will continue to support rankings, but reference signals will increasingly determine how a brand is interpreted and presented. Measurement will lag behind reality. With the majority of traditional searches and a growing share of AI interactions ending without a click, visibility and influence will become harder to track using traditional metrics. New frameworks will emerge, but they will need to measure presence, inclusion and interpretation, not just traffic. And underlying all of this is a more fundamental shift. Discovery is becoming less about navigating information and more about being presented with interpreted outcomes. SEO is not dead. But it is no longer defined by rankings alone, or even by search engines themselves. The fundamentals of discoverability remain. Structure, authority, consistency and relevance still matter. What has changed is how those signals influence interpretation, retrieval and selection. Visibility is no longer just earned through position. It is earned through being understood, validated and selected across the Global Search and Discovery Economy. The brands that succeed will not simply optimise for where they appear. They will optimise for how they are represented across the sources that shape answers. And the practitioners who succeed will recognise that SEO is no longer just about influencing search engines. It is about influencing how information is structured, trusted and selected within systems that increasingly shape decisions before a click ever happens.

Branding has existed for centuries, long before marketing became a formal discipline. Early brands were simple identifiers. Marks used by craftsmen, farmers and traders to signal origin, ownership and quality. These marks of origin and brand identity helped buyers navigate markets where information was scarce and trust was essential. The period of industrialisation introduced the birth of print media, marking the first major expansion in marketing capability. Newspapers, posters and printed advertising enabled brands to communicate with larger audiences for the first time. As products became more widely available and increasingly similar, brands helped signal reliability, reputation and value. Print media also created the first real commercial opportunity for entrepreneurs to invest in promotion, diversify their reach and compete more aggressively in emerging markets. As brands began to advertise more widely, marketing quickly became an exercise in understanding supply, demand and human behaviour. Early advertising campaigns often reflected the cultural stereotypes of the time. Men were portrayed driving Ford cars or smoking cigarettes, while women were frequently depicted as homemakers responsible for purchasing household goods. While these portrayals feel dated today, they represented some of the earliest attempts to connect products with real world identities, aspirations and problems. Marketers relied heavily on intuition and creative instinct, aligning brands with the lifestyles and needs of their audiences. The twentieth century accelerated this shift with the globalisation of brands and media. Radio and television expanded the reach of brands beyond local markets and across national borders. Brands were no longer simply identifiers of origin. They became cultural signals capable of shaping how consumers perceived products, services and entire industries. Advertising moved beyond simple product promotion and into the creation of identity, aspiration and belonging. As millions began to be spent on advertising and marketing, the natural evolution was for marketing to become more scientific. Marketers began analysing what worked and what did not. They studied the impact of advertising presence versus absence, the role of promotions and sales, and the behavioural patterns behind purchasing decisions. Psychology increasingly became central to marketing strategy as businesses sought to understand why consumers choose certain brands and remain loyal to them over time. These developments marked some of the earliest foundations of what we now recognise today as customer lifetime value thinking. In 1993, Kevin Lane Keller introduced one of the most influential frameworks for understanding brand value: the Customer Based Brand Equity Model. Keller’s model explained how strong brands build equity in the minds of consumers through a progression of awareness, meaning and emotional connection. Logical evaluations such as quality and performance combine with emotional associations such as identity, aspiration and trust. Together these forces create perceived value, ultimately leading to brand resonance. For decades, Keller’s framework has provided a powerful lens for understanding how brands grow. Brands succeed when they become mentally available, emotionally meaningful and consistently present in the lives of consumers. But the environment in which brands compete has continued to evolve. The birth of the internet dramatically expanded the reach of brands and the power of consumers. Borders and continents were no longer barriers. Just as people could send electronic mail to each other across the world, they also had the opportunity to discover, engage with and purchase goods and services from businesses in entirely different markets. Even in the early days of online commerce, transactions were possible. Consumers might complete an order form online and send payment by cheque through the post, or register their details to confirm an order. It was imperfect, but it signalled a profound shift in how commerce could operate. The rapid development of ecommerce platforms and digital payment gateways soon made online transactions faster, simpler and more secure. Goods could be purchased instantly and delivered across regions and continents. The internet had fundamentally expanded both audience reach and commercial opportunity for brands. As internet adoption accelerated, a new generation of digital platforms began to reshape how consumers discovered and interacted with brands. Early internet portals such as AOL and Yahoo introduced millions of people to the online world, acting as gateways to information, communication and commerce. Soon after, search engines, most notably Google, transformed the mechanics of brand discovery. For the first time, consumers could actively search for information, compare products and evaluate brands before making purchasing decisions. Brand discovery was no longer controlled solely by advertising exposure. It increasingly became driven by consumer intent. At the same time, social platforms such as Myspace, Facebook and later YouTube introduced an entirely new dimension to brand communication. Brands were no longer simply broadcasting messages to passive audiences. They were participating in digital communities where consumers could share opinions, recommend products and influence each other’s purchasing behaviour. Branding and marketing once again had to step into the lives of people, much like they had done during the birth of print media, but this time it was within people’s digital society where brands needed to reach and engage consumers. This shift marked a fundamental change in the balance of power between brands and consumers. Discovery became search driven, while validation increasingly came from peer networks and online communities rather than from advertising alone. However, even with ecommerce and digital payments enabling global transactions, media placement itself was still largely manual. Advertisers negotiated placements, managed campaigns and optimised performance through labour intensive processes. It was not long before this too evolved. Media buying and distribution became digitised, giving rise to programmatic advertising and machine learning driven optimisation. This era of media digitisation and machine learning transformed how brands reached audiences. Marketing shifted from broadcast communication towards algorithmic discovery, where platforms could analyse behaviour, optimise placement and deliver content to the most relevant audiences at scale. Today we are experiencing another shift in branding and marketing not seen since the birth of print and the internet. While each previous transformation took decades to fully unfold, the evolution of artificial intelligence is accelerating far more rapidly. The adoption of AI across industries is happening at a pace never seen before in the history of marketing. But the latest wave of technological change has also reshaped how loyalty forms and how demand converts into action. The fascinating reality is that much of the world is now digitised and connected through the internet. In terms of audience reach, brands are approaching a natural ceiling. The global population is largely accessible through digital channels, meaning growth can no longer rely solely on reaching more people. The question therefore becomes different. How can brands maximise this global potential? What shifts in branding, marketing capability and digital enablement need to take place so that relevant audiences around the world can resonate with a brand? This is the question we are going to explore. The Rise of Brand Equity As branding evolved through print, global media and the early internet, marketers increasingly focused on understanding how brands create value in the minds of consumers. While advertising expanded the reach of brands, it also raised a more complex question. Why do consumers choose one brand over another, even when products appear similar? Marketing theory began to focus on the concept of brand equity, the idea that brands themselves hold measurable value beyond the functional characteristics of the products or services they represent. Keller’s Brand Equity model became one of the most influential frameworks in modern branding, widely used across marketing academia and brand consulting to understand how brands build meaning and loyalty over time. At its core, the framework explains how strong brands build value through a progression of awareness, meaning and emotional connection. Consumers first recognise and recall a brand. Over time they begin to associate that brand with specific attributes, experiences and values. These associations shape how the brand is perceived and ultimately influence purchasing decisions. Logical factors such as quality, reliability and performance combine with emotional associations such as identity, aspiration and trust. Together these elements form perceived value in the mind of the consumer. When this perceived value is strong and consistent, it leads to brand resonance. This is the stage where consumers develop a deep psychological connection with a brand. They choose it repeatedly, recommend it to others and remain loyal even when alternatives exist. In a world where media exposure was relatively controlled and product choice was limited, this approach proved highly effective, enabling strong brands to build preference, loyalty and long term commercial advantage. However, the forces of globalisation and digital commerce dramatically changed the competitive landscape. Manufacturing capabilities expanded across the world, enabling products to be produced faster, cheaper and at greater scale. New brands could enter markets quickly, while established products were often replicated, imitated or positioned as lower cost alternatives. Copycat products and counterfeit goods became increasingly visible across global markets. While the importation of counterfeit goods remains illegal in many countries, culturally the presence of imitation and lookalike products has become normalised in many categories. Consumers could now compare prices instantly, research alternatives and discover competing brands from anywhere in the world. In many cases consumers may feel emotionally connected to a particular brand, logo or product, yet still choose a lower priced alternative that delivers similar tangible benefits. This tension between emotional attachment and practical value is one of the clearest examples of perceived value competing directly with tangible value in modern purchasing behaviour. In other words, consumers may admire the brand, but still choose the product that delivers the best combination of price, availability or convenience at the moment of purchase. This shift does not mean brand equity has become less important. Strong brands still command trust, recognition and emotional attachment. But it does mean that perceived value alone is not always enough to convert demand into action. Consumers today operate in an environment where discovery, validation and fulfilment happen simultaneously across digital platforms.This is where the traditional understanding of brand equity begins to meet the realities of modern consumer behaviour. And it is at this intersection that the next evolution of branding begins. The Brand Equity Pivot To succeed in today’s globally accessible environment, brands must balance the traditional principles of brand equity and perceived value with a new set of brand loyalty dynamics. They must maintain emotional connection and trust whilst navigating increasing levels of competition, the rise of counterfeit and copycat products and the broader impacts of globalisation, digital commerce and shifting geopolitical conditions. For businesses to grow in this environment, branding itself must evolve. Brands must find an equilibrium between perceived brand equity and tangible brand value. This requires a strategic pivot. It is for this reason that we introduce the Brand Equity Pivot. Before exploring this shift, it is useful to briefly recap how traditional brand equity has historically explained the creation of perceived value and loyalty. Brand Equity Principle What It Builds Outcome for Brands Brand Awareness Recognition and recall Mental availability when demand appears Brand Meaning Associations around quality, identity and values Differentiation from competitors Perceived Value Logical and emotional evaluation of the brand Trust and preference Brand Resonance Deep psychological connection Loyalty, advocacy and repeat purchasing For decades this model provided the foundation for brand strategy. The logic was simple: build awareness, create meaning and strengthen perceived value and loyalty would follow. However, modern purchasing environments introduce additional dynamics that influence whether perceived value can translate into real commercial behaviour. Consumers may recognise a brand, trust it and feel emotionally connected to it, yet still choose an alternative if the product cannot be easily discovered, validated, interacted with or obtained when demand arises. This is where tangible value signals become critical. Tangible Brand Capability What It Enables Commercial Outcome Discoverability Ability for consumers to find the brand through search and digital platforms Visibility Social Proof Reviews, ratings, recommendations and community signals Validation Accessibility Ease of interacting with the brand across digital and physical environments Interaction Availability Ability to obtain the product or service when demand appears Conversion In practical terms: Discoverability drives visibility Social proof drives validation Accessibility drives interaction Availability drives conversion Tangible Brand Capability Examples of How Brands Deliver It Discoverability SEO, AEO, GEO, marketplace optimisation, structured data Social Proof Customer reviews, ratings, testimonials, influencer advocacy, community engagement Accessibility Mobile apps, responsive websites, chat and voice agents, seamless digital experiences Availability Ecommerce platforms, fast delivery, local distribution, multiple payment options Together these mechanisms allow perceived brand value to translate into real purchasing behaviour. In other words, perceived value creates brand desire, but tangible value enables that desire to convert into action. This relationship between perceived value and tangible value is illustrated in the Brand Equity Pivot framework below. How AI Enhances the Core Principles of Branding Artificial intelligence is not a branding principle in its own right. It is an accelerant. It enhances the mechanisms through which brands build perceived value and deliver tangible value. AI helps brands identify audiences, create and distribute content, surface trust signals, improve interaction and optimise conversion. This means AI strengthens both sides of the Brand Equity Pivot. It can amplify perceived value by improving awareness, meaning and resonance, and it can amplify tangible value by improving discoverability, validation, accessibility and availability. Brand Enhancement Category What AI Improves Examples from the AI landscape Brand Impact Audience Intelligence Identifies new audiences, lookalike groups and high value segments Aidaptive, Anyword, Neurons, KeywordSearch, Semrush, Markopolo Improves targeting, reach and relevance Content Creation Produces written, visual, audio and video content at speed and scale Adobe Firefly, Jasper, Copy.ai , Writesonic, Runway, Midjourney, Synthesia, DeepBrain, Fliki, Lumen5 Strengthens brand salience, frequency and resonance Creative Optimisation Tests, predicts and improves creative effectiveness before or during launch AdCreative.ai , Anyword, Neurons, Simplified, MarketingBlocks Improves message relevance and performance Discoverability Improves visibility across search, AI search and digital platforms Frase, Surfer, SEO.ai , Scalenut, LinkWhisper, Semrush, Morningscore Strengthens discoverability and visibility Social Proof and Validation Surfaces reviews, monitors sentiment and manages brand conversation BrandBastion, ContentStudio, Flock Social, CommentReply, Engage AI Improves validation, trust and community influence Accessibility and Interaction Enables seamless interaction through chat, voice and conversational interfaces Chatbase, Chatfuel, QuickChat, Alan, Watermelon, Cody, Arsturn, Chat Thing Improves accessibility and interaction Knowledge Architecture Structures internal and external knowledge so AI can respond accurately and consistently Glean, Weaviate, ChatPDF, AskYourPDF, Cyte, Mem, Taskade, Cody Improves consistency, responsiveness and brand intelligence Personalisation Tailors messaging, offers and experiences in real time Aidaptive, Contlo, ActiveCampaign, HighLevel, Begin AI, Tavus, Windsor Strengthens resonance, loyalty and relevance Conversion Enablement Reduces friction in the path to purchase through automation and optimisation ActiveCampaign, Contlo, HighLevel, Make, Zapier, Systeme, Reetail Improves conversion and commercial efficiency Availability and Fulfilment Signals Helps brands align stock, delivery and fulfilment with demand Aidaptive, Make, Zapier, HighLevel Improves availability and readiness to convert demand Brand Governance and Consistency Maintains consistent tone, message and visual identity at scale Grammarly, Jasper, Copy.ai , Adobe Firefly, Looka, LogoAI Protects perceived value and brand coherence What this means in practice is simple. AI can now support the full brand journey. It can identify the right audiences It can create and launch content It can optimise that content for discoverability It can surface reviews and community signals for validation It can power interactions through chat, voice and assistants It can support conversion through automation, personalisation and operational readiness This is why AI should not be seen purely as a content tool. Its real value lies in its ability to strengthen the full system of modern brand building. How AI Maps to the Brand Equity Pivot The most important point is that AI does not sit outside the framework. It strengthens the mechanisms inside it. Brand Equity Pivot Layer AI Contribution Example Tool Types Perceived Value Builds awareness, meaning, emotional connection and resonance Jasper, Adobe Firefly, Runway, Synthesia, Copy.ai , Midjourney Discoverability Helps the brand get found in search, AI search and digital environments Frase, Surfer, SEO.ai , Semrush, Scalenut Validation Surfaces trust signals, reviews, sentiment and social engagement BrandBastion, ContentStudio, Flock Social Accessibility Enables interaction through digital assistants and knowledge based systems Chatbase, Alan, QuickChat, Cody, Watermelon Availability Supports conversion readiness through automation, personalisation and fulfilment signals ActiveCampaign, Contlo, HighLevel, Make, Zapier If traditional branding built desire, AI helps brands operationalise that desire. It gives marketers the ability to scale content, improve visibility, reinforce trust, enable interaction and reduce friction at the point of conversion. That is what makes AI strategically important to branding. Not because it replaces the principles of brand building, but because it improves a brand’s ability to execute them. Generative AI and Agentic AI in Modern Brand Systems Artificial intelligence is often discussed as though it is one thing, but in practice two distinct forms of AI are beginning to shape modern marketing systems: Generative AI and Agentic AI. Understanding the difference is critical because they enhance branding in different ways. AI Type Primary Function Role in Branding Generative AI Produces content, assets and creative outputs Accelerates brand communication Agentic AI Executes actions and pursues goals within defined systems Optimises brand systems and operations Generative AI has already transformed how brands create. Copy, visuals, videos, voice, presentations and campaign assets can now be produced, adapted and personalised at a speed and scale that would previously have required large teams and long lead times. This dramatically increases a brand’s ability to reinforce perceived value. But the same opportunity is available to competitors hustling for attention. Brands can communicate more frequently, tailor messages to more segments, localise creative faster and keep pace with culture and market signals more effectively. But generative AI alone does not solve the operational side of modern brand loyalty. Producing more content does not automatically make a brand easier to find, easier to trust, easier to interact with or easier to buy. That is where agentic AI becomes important. Agentic AI systems operate with goals and the ability to take action within structured environments. Rather than simply generating outputs, they can analyse data, monitor conditions, trigger workflows and improve performance across the brand system. Examples include: systems that identify search trends and recommend content actions agents that respond to customer questions using a structured knowledge base systems that optimise messaging flows based on behaviour tools that automate follow up, recommendations or conversion journeys workflows that improve fulfilment readiness based on demand signals Generative AI strengthens brand communication, while agentic AI strengthens brand capability. When these two work together, brands move beyond campaigns and towards intelligent ecosystems. Together they allow brands to strengthen both perceived value and tangible value simultaneously. Branding Capability Generative AI Role Agentic AI Role Audience Reach Creates more content variations for more segments and channels Identifies high value audiences and triggers actions based on behaviour Brand Meaning Produces stories, visuals and messaging that reinforce positioning Learns which messages and moments drive stronger response Social Proof Summarises reviews, generates case study formats and surfaces proof points Monitors sentiment, flags issues and routes actions Discoverability Creates SEO, AEO and GEO aligned content at scale Tracks trends, optimises workflows and adjusts discovery tactics Accessibility Builds branded assistants, guided experiences and conversational interfaces Automates responses, routes enquiries and maintains service continuity Availability Supports product messaging, stock communication and offer clarity Responds to demand signals, automates journeys and improves fulfilment readiness This is where branding starts to look less like a campaign schedule and more like a living system. But … Infrastructure Matters! This is also the point where a lot of businesses get AI wrong. They buy tools before they build the conditions that allow those tools to create value. As explored in our previous article featuring The Data Intelligent Marketing Enablement Framework DIMEF Model , AI only becomes commercially useful when it sits on top of structured infrastructure. That means AI performance depends on: clean and connected data structured knowledge architecture integrated systems clear governance workflows capable of acting on insight Without these foundations, AI can produce outputs, but it cannot reliably strengthen the brand system. In the context of branding, DIMEF matters because it creates the conditions that allow AI to enhance both perceived and tangible value. For example: Without a structured data layer, audience intelligence and personalisation remain weak Without a knowledge architecture, conversational AI becomes inconsistent or inaccurate Without integrated execution systems, AI cannot improve discoverability, interaction or conversion in a meaningful way Without governance, brands risk inconsistency, misinformation and erosion of trust AI can only strengthen branding when the brand ecosystem itself is structured to support intelligence. Conclusion For businesses and brand leaders, the implication is clear. This is precisely why many organisations are currently shoehorning AI solutions into their marketing stacks without a clear strategic approach. The result is often rising technology costs, increased security risks and fragmented data silos rather than genuine marketing intelligence. The opportunity is not simply to adopt AI. The real opportunity is to build a brand system that AI can genuinely enhance. Because those that don’t will feel the impact on their bottom line.  That means asking a different set of questions. Is our brand easy to discover across search, AI search and digital platforms? Are trust signals visible and credible at the moment consumers research us? Can customers interact with us seamlessly across channels? Can we convert demand efficiently once it appears? Do we have the data, knowledge and execution infrastructure required to support AI in these areas? The brands that succeed will not be the ones using the most AI tools. They will be the ones using AI to strengthen the right brand mechanisms. The fundamentals of branding have not changed. Brands still grow by creating awareness, meaning, trust and emotional connection. Perceived value still matters. Brand resonance still matters. But the context in which those principles operate has changed dramatically. Today brands must do more than create desire. They must also be ready to capture it.That is the essence of the Brand Equity Pivot. Modern brand loyalty depends not only on how consumers feel about a brand, but on how easily they can discover it, validate it, interact with it and obtain it when demand appears. Generative AI and Agentic AI do not replace these principles. They enhance a brand’s ability to execute them with greater speed, scale and intelligence. The brands that win will be those that balance perceived value with tangible value and pair that balance with the infrastructure required to make AI meaningful. Because in a globally connected and digitally saturated market, branding is no longer just about being remembered. It is about being discoverable, trusted, accessible and ready the moment demand appears.

Is it just me, or does every time you open Facebook, Instagram or LinkedIn feel like stepping into an AI arms race? Every scroll seems to introduce a new tool promising to revolutionise marketing, AI copywriters, AI designers, AI video generators, AI research assistants, AI outreach tools, AI agents. The list grows by the day. I have to admit, it can feel overwhelming. At this stage of the AI cycle it is easy for marketing teams to fall into the trap of chasing tools rather than solving real problems. Very quickly the subscriptions start stacking up. A design tool here, a writing assistant there, an AI research platform, an automation agent, a video generator. Before long finance teams are looking at expense reports trying to understand why the business is suddenly paying for dozens of AI subscriptions. The bigger question is whether anyone really knows if they are making a difference. Are they improving productivity? Is company information being shared securely? Is there any governance around how these tools are being used? But it’s ok, at least you can say your company is embracing AI.  This is why it is worth taking a step back. Not to wait and miss the AI boat, not to let your competitors steal the march, but simply to ensure that the energy and cost being invested into AI is structured and aligned. When you step back from the constant bombardment of new AI tools and return to the reality of running a business, the question becomes much simpler. How should AI actually be rolled out in a way that drives growth, improves efficiency and remains manageable across the organisation? AI adoption should not be about collecting subscriptions. It should be about building a system that allows marketing teams to work faster and smarter. Otherwise organisations risk spending hundreds of pounds per month on premium AI tools simply so employees can use them to check emails. The objective is not simply to remove all humans from the marketing operation. This is not Terminator territory. But it would also be unrealistic to pretend that AI will not change how marketing teams are structured. It is hard to ignore the fact that during business restructures the first casualties are often internal marketers and recruiters. Recent research from PwC's Global AI Jobs Barometer  highlights how quickly this shift is happening. The report shows that organisations adopting AI technologies are seeing measurable productivity improvements across knowledge based roles, with many businesses using AI to automate tasks that previously required dedicated human resource. AI excels at analysing large volumes of data, identifying patterns and executing repetitive processes. Humans remain essential for strategic thinking, creativity, partnerships, commercial negotiations (e.g. affiliate marketing) brand judgement and understanding customers. This is where structure becomes important. The Data Intelligent Marketing Enablement Framework (DIMEF) by Logixx Consulting is designed to bring clarity to how AI should be integrated into the marketing operating model. Rather than focusing on individual tools, it breaks AI down into the core capabilities required to drive marketing growth and operational efficiency. The Governance Layer: Setting the Scene for an AI Future At the top of the model sits the Marketing and Communications AI Infrastructure, which defines the strategy, governance and operating procedures that guide how AI is used across the organisation. This layer is essential because AI introduces new considerations around data governance, privacy, model monitoring and ethical use. Without clear policies and oversight organisations risk inconsistent decision making, exposure of sensitive information or teams using tools that have never been properly approved. Yes, it is the boring part. It is not remotely exciting, especially for those working in marketing. But before investing heavily in AI it is worth stepping back and answering a few fundamental questions. What is the strategy? What do you actually want AI to achieve or enable within the business? Is it about productivity, insight generation, customer experience or operational efficiency? How will it be governed? What level of risk is associated with AI and how does this impact compliance, intellectual property, data sensitivity, customers and employees? Who is responsible for approving tools and monitoring how they are used? What are the human implications? How will roles evolve as AI becomes embedded into workflows? What new skills will teams need and where should human oversight remain essential? These questions may not feel as exciting as experimenting with new AI tools, but they are the foundation for sustainable adoption. The governance layer ensures that AI operates within clearly defined boundaries. It also ensures that leadership and finance remain aligned on the cost, risk and security of AI investments. Standards can then be established around data usage, automation rules, human approval processes and compliance requirements. In short, this layer sets the rules for how intelligence is generated, monitored and applied across the organisation. Building the Data Foundation Every AI driven system should start with the data rather than the technology. Many organisations begin their AI journey by experimenting with tools, but without a structured data foundation those tools rarely deliver meaningful value. At its simplest level the infrastructure relies on two critical data inputs. While this may sound deceptively simple, these two categories contain the vast majority of the information required to power modern marketing intelligence. First party data and compliance This includes customer records, preferences, consent information and identity data collected directly by the organisation. However, this category is not just about the data itself. It must also consider the legal and operational frameworks around storing, managing and utilising that data responsibly. Understanding how customer data is governed, secured and accessed is just as important as the data itself. Measurement frameworks and signals Marketing activity generates a significant amount of behavioural data. Engagement signals such as impressions, clicks, interactions and conversions all feed into the measurement framework and provide an understanding of how marketing activity performs. These signals help organisations move beyond assumptions and measure what is actually happening across channels and campaigns. At Logixx Consulting we combine these sources to create what we call a Single Human View (SHV). The SHV represents a unified understanding of each individual person, whether they are an existing customer, a prospect or simply someone who has interacted with the brand. It combines identity data, behavioural history, preferences and outcomes to create a much richer picture of the individual. This view provides the contextual foundation that enables AI systems to generate meaningful insight rather than isolated data points. The Knowledge Architecture: Giving AI Context While data provides the signals that power marketing intelligence, AI systems also require access to structured knowledge. Marketing teams generate an enormous amount of institutional knowledge over time. Documents, research, website content, campaign history, product information and brand guidelines all represent valuable sources of context. However, this information is often scattered across shared drives, cloud folders, internal tools and email threads. A centralised knowledge architecture ensures that this information can be ingested, structured, indexed and retrieved when needed. The first benefit is internal. Rather than employees searching through folders or relying on tribal knowledge, both staff and AI systems can access a single, structured source of truth. This makes information easier to find, reduces duplication and ensures teams are working from consistent guidance. The second benefit is operational. The knowledge architecture becomes the foundation that powers automations, AI agents and real time customer interactions. This architecture typically ingests information from sources such as documents, internal research, PDFs, forms, website content and other digital assets. Once structured, the information becomes searchable and accessible to both humans and AI systems. For example, tone of voice guidelines, brand messaging frameworks, product documentation and service processes can all be embedded into the knowledge layer. This allows AI agents and automated systems to respond to customers in a way that reflects the organisation's brand, policies and expertise. Without a structured knowledge layer, AI systems rely heavily on general training data or incomplete context. With one, they can retrieve accurate internal information and apply it to real business situations, whether that is supporting staff decisions, generating content or handling customer conversations in real time. The result is that institutional knowledge no longer sits dormant in folders or forgotten documents. Instead it becomes an active intelligence layer that informs staff, powers automation and supports customer engagement across the organisation. The Data Lake: The Core Intelligence Engine At the centre of the architecture sits the Data Lake, which stores and connects all customer, product and operational data. This centralised environment enables the organisation to move beyond fragmented reporting systems and siloed data sets and move towards a unified intelligence engine capable of powering insight generation, automation and AI driven decision making. Without a centralised data environment, AI systems are effectively guessing. They may produce confident answers, but those answers can be based on incomplete or inaccurate information unless clear guardrails and data handling protocols are in place. This is often the difference between a structured chatbot and a true AI enabled conversational agent. A chatbot follows predefined rules and decision trees. An AI agent can interpret context, retrieve relevant information and respond intelligently, but only if it has access to accurate and well structured data. By consolidating information from marketing platforms, operational systems and knowledge repositories, the Data Lake becomes the foundation for analysis, data informed decision making and automation. Operational customer events, such as account changes, support interactions or purchases, are also captured here. These events provide valuable signals that help organisations understand customer behaviour beyond digital marketing channels alone. They also unlock automation opportunities, trigger communications and support service interactions. The result is a continuously updated intelligence environment that reflects both marketing activity and real world customer interactions. Turning Data into Intelligence Raw data alone does not drive growth. It must be interpreted and harnessed. This is the role of the AI Business and Customer Intelligence layer. At this stage AI models analyse the meaningful signals flowing through the Data Lake and measurement systems, identifying patterns, trends and opportunities. The emphasis on meaningful signals is important. Conversion and value data should not be confused with softer channel engagement metrics such as impressions or clicks. Engagement data has its place, but intelligence should ultimately be grounded in outcomes. The outputs of this layer can take many forms. Dashboards, automated reporting, forecasts and conversational insights that allow users to query the data using text or voice interfaces are increasingly common. This is where practitioners begin to see the real benefit of AI. Media buyers, marketers and commercial teams gain faster access to insight without needing to manually interrogate multiple reporting systems. For business analysts the shift is more profound. Once the infrastructure is in place, the traditional role of preparing reports and summarising performance becomes largely automated. AI can generate commentary, highlight anomalies and answer questions directly. Rather than acting as data workhorses, analysts increasingly become data and AI custodians, responsible for ensuring the integrity of the data environment and the quality of the intelligence being generated. From Insight to Action Insights alone do not drive results. They must translate into action. The AI Decision Engine performs this role by converting intelligence into execution strategies. Based on the signals flowing through the data environment, the system can recommend campaign adjustments, audience targeting strategies and budget reallocations. It is worth noting that a significant amount of optimisation already takes place within advertising platforms themselves. Systems such as Meta and Google Ads rely heavily on conversion signals to train their algorithms. This means the quality of the underlying measurement framework becomes critical. Approaches such as server side tracking and methodologies that connect Google session identifiers with conversion frequency and value can significantly improve the accuracy of these optimisation signals. When platforms receive stronger data, their algorithms become more effective. While AI can recommend actions, human oversight remains essential. Marketing leaders still guide strategic direction, approve major shifts in spend and ensure decisions remain aligned with commercial objectives. Which leads naturally into the execution layer. AI and Marketing Execution: From Insight to Action This is the part of the conversation where AI discussions often become exaggerated. Spend a few minutes on LinkedIn and you will likely see posts claiming that AI has already replaced media buyers. Tools are showcased that connect directly to advertising platforms and promise to analyse performance, generate creatives, optimise budgets and produce reports automatically. Some of these capabilities are very real. AI can already analyse large volumes of performance data far faster than human teams. It can identify patterns in campaign performance, detect anomalies, generate creative variations and recommend budget reallocations in seconds. However execution inside a real marketing environment still requires structure. Campaign structures must follow defined naming conventions. UTM frameworks need to remain consistent so performance can be measured accurately. Creative assets still pass through brand and legal approval processes. Budget decisions must align with broader commercial objectives. In many ways media buyers have already lived through a far more radical transformation. The introduction of DSPs and programmatic media buying fundamentally changed how digital media was purchased. Manual placements quickly disappeared as automation and algorithmic buying became the standard. AI represents the next stage of that evolution rather than its replacement. Rather than removing media buyers entirely, AI is improving the role. Much of the manual work around data analysis, reporting and optimisation can now be automated, allowing practitioners to focus more on strategy, experimentation and performance interpretation. Within the Data Intelligent Marketing Enablement Framework (DIMEF)  by Logixx Consulting, AI becomes part of the execution layer rather than replacing it. It accelerates analysis and improves decision quality while still operating within the governance, measurement and operational structures that ensure marketing activity remains measurable and strategically aligned. Executing Campaigns with AI Once decisions are made, execution occurs through the AI and Communications Execution layer. This layer connects directly to marketing platforms such as CRM systems, messaging platforms and advertising channels including Google, Meta and WhatsApp. Campaigns can be deployed, optimised and monitored automatically based on insights from the decision engine. In many cases this is already happening through structured automation rather than pure AI, using decision trees and rule based workflows that trigger campaigns, messaging or bidding adjustments. Creative production is one of the areas seeing the fastest acceleration through AI. The major advertising platforms are rapidly embedding creative generation directly into their ecosystems. Google is expanding its AI tools to generate and optimise creative assets within its advertising environment,  allowing marketers to automatically produce multiple variations of headlines, descriptions and imagery. You can also find more about creative and generative AI from Google here .  Meta is building generative AI capabilities designed to create, test and optimise multiple ad variations automatically,  enabling campaigns to scale creative testing far beyond what manual production would allow. If Meta is of interest then it is worth looking at this brand consistency article from Ad Week  or their announcement of 11 AI ad tools from Cannes in 2025 .  TikTok is introducing AI assisted creative tools  that help brands generate video assets that match the platform's native style and format, reducing the barrier to producing high volumes of short form content. Good article here from Social Media Today  which goes into more detail about their solution.  Alongside the platform ecosystems, traditional creative technology providers are also evolving rapidly. Adobe, for example, has introduced Firefly , its generative AI engine designed to support image generation, asset editing and scalable creative production while maintaining brand safe training data. You can keep up to date with their roadmap here .  The direction of travel is clear. Creative production is moving from a manual process into an AI assisted workflow where concepts, variations and adaptations can be generated in minutes rather than days. Meanwhile AI agents can interact directly with customers through chat or voice interfaces, handling enquiries, guiding users through journeys and collecting new signals that feed back into the marketing intelligence environment. These interactions can be informed by the organisation's knowledge architecture, which stores brand guidelines, tone of voice documentation, product information and operational policies. By embedding this information into the knowledge layer, AI systems can respond in a way that reflects the organisation's voice, expertise and communication standards. Free text prompts and prompt tuning can further refine responses, ensuring that automated communications remain consistent with the brand's tone and messaging. The result is a marketing ecosystem capable of responding dynamically to human behaviour whether the individual is a customer, a prospect or someone who has simply signed up to receive communications. The Competitive Advantage of AI Infrastructure The real opportunity with AI is not found in individual tools or subscriptions. It lies in building the infrastructure that allows data, intelligence and execution to work together. Organisations that approach AI in a structured way gain a significant advantage over those experimenting with disconnected tools. When data foundations, knowledge architecture, intelligence layers and execution systems are connected, marketing teams move from reactive reporting to proactive decision making. Within the Data Intelligent Marketing Enablement Framework (DIMEF)  by Logixx Consulting, AI is not treated as a collection of tools. It becomes an integrated system that connects governance, data, intelligence and marketing execution. This approach allows organisations to extract insight faster, improve decision quality and scale marketing activity with greater efficiency while still maintaining the governance and operational controls that modern businesses require. Ultimately the advantage does not come from replacing human expertise. It comes from combining AI capability with human judgement. When AI handles the heavy lifting of data processing, reporting and operational optimisation, marketing teams can focus on what they do best: strategy, creativity and building meaningful relationships with customers. We hope this framework has helped demystify how AI can be implemented within marketing in a structured and practical way. DIMEF is not a technology stack. It is an operating model for how marketing organisations structure AI. If your organisation is exploring how AI and marketing can unlock growth opportunities or deliver operational efficiencies, you can schedule a conversation with Logixx Consulting.

Storytelling and content creation have been on an exciting journey in recent years. From apps that make editing and content production accessible to anyone, to platforms that allow storytelling at scale, we’ve seen a democratisation of design. What was once the domain of trained professionals is now open to anyone with a smartphone and an idea. This shift has given rise to the era of the influencer. Individuals without formal design training are now building personal brands by creating content that resonates with social audiences and online communities. The ability to produce, edit, and publish at speed has blurred the lines between creative enthusiasts, influencers, and design professionals, reshaping how audiences consume and connect with content and, in turn, challenging the role of traditional designers. Whilst the gap between creative enthusiasts, influencers, and design professionals has narrowed, those with a design background are uniquely positioned to embrace this shift. By adopting AI, they can widen the gap once more, combining attention to detail and originality with faster delivery and the ability to test ideas at scale. What might appear to be a threat is instead becoming a powerful extension of their creativity. Let's explore the different areas of the design process... Ideation and AI For those naive enough to rely solely on AI, be prepared to be challenged — and even called out for laziness. But for those who embrace AI as a creative partner, the opportunities are endless. AI can critique ideas, test narratives against an ICP (ideal customer profile) or audience groups, and suggest new angles based on audience data. Beyond text generation, AI is now stepping into the concepting and storyboarding stage, giving marketers and designers faster ways to visualise ideas. By simply uploading a partial or completed script and adding prompts to guide the storyboard, users can edit and refine until the concept feels right. A few examples worth exploring: Storyboarder AI  offers straightforward upload or text prompts can be enough to create professional looking storyboards. Storyboarder AI has an impressive range of interactive examples. StoryboardHero  is backed by a corporate video agency, though access to demos is limited and the positioning feels more like a lead generation tool than a design solution. Storyboard from StoryboardHero Runway  super powerful text-to-video concepting tool with more advanced options. Working with leading film studios including Lionsgate. If you are looking for something special then look no further than MidJourney   It's more expensive but for good reason, it excels at creating high quality concept art and mood boards. Finally is Leonardo AI , one of the best freemium tools, offering strong outputs for rapid ideation without heavy costs. Might be worth mentioning Boords  which looks like it has some potential but, if i'm totally honest, their website seriously lacks the examples that showcase what is possible with their technology. The Creative Process and AI Who else remembers when AI first crept into design and the phrase “that’s AI”  was casually thrown around in the creative industries? A few years on, some of those early, sloppy executions are still out there, but the overall quality has improved with every development cycle. AI is no longer just a tool for inspiration, it has become part of the hands-on creative process, an enabler that frees up time by taking on repetitive or time-consuming tasks, leaving more room for strategic and imaginative work. AI tools now allow designers to rapidly prototype, generate multiple design variations, and keep assets brand-consistent without starting from scratch. What might once have required several rounds of manual iterations can now be achieved in minutes, giving professionals the agility to test, adapt, and evolve campaigns faster than ever before. Examples include: Canva AI , part of Canva, seems to be creeping into every business - a development that's no surprise given decades of using PowerPoint. Not only does it offer a superior experience to slides, but it also integrates AI solutions including copy, image generation, and instant layouts for quick campaign mock ups. Khroma  is a little bit niche compared to some of the more well rounded tools, but it learns your colour preferences by selecting a range of shades or importing documents. It can speed up early branding and campaign processes, though at present (September 2025) it is still in beta and lacks the ability to enter hex codes or filter palettes efficiently. An interesting concept at a the start of the creative process. For copy I would look no further than Jasper   or Copy.ai . I was first introduced to Jasper by a PR agency in Amsterdam. If you are still relying on ChatGPT for copywriting, Jasper is worth a look. It is pricier, but its speed, output quality, and style preferences make it a true marketer’s platform. Copy.ai   is a cheaper alternative to Jasper, better suited to smaller tasks. Where Jasper excels at bigger challenges like brochure copy, SEO, and large-scale comms, Copy.ai offers broad integrations that make content creation and implementation much simpler for smaller teams. Uizard recently launched Autodesigner 2.0 , ideal for product marketers and UX practitioners. It takes text ideas, sketches, or screenshots and converts them into editable design prototypes, perfect for sparking inspiration, testing UX improvements, or even analysing competitor creative. Something recent in September 2025 is the new release of Figma AI Copilot. Figma has closed much of the gap between Adobe tools and design enthusiasts. Copilot accelerates design by generating components, automating workflows, and supporting prototyping, all without the steep costs and training often required with Adobe. These tools do not remove the need for a professional eye, if anything, they demand it. But they also contribute to a very real fear: that designers, copywriters, UX practitioners, and product marketers could be replaced if they fail to incorporate AI into their skill set. At the same time, they show how clients, SMEs, and leaner marketing teams can be empowered by AI rather than overcharged by agencies working on traditional time and tools models. AI means fewer manual hours, which creates a cost saving for businesses, but also a dilemma for traditional agencies whose business models are built around billable time. AI in Post Production Post production has traditionally been one of the most time intensive stages of the creative process. Editing, audio tuning, voiceovers, and final polish could take days or even weeks, more often than not requiring multiple specialists. AI is (again) reshaping this another stage of the design process. AI in post production is making it faster, more cost effective, and more scalable to get the job done, while still delivering professional quality. AI driven tools can fine tune audio, clean up video, generate voiceovers, and even replicate voices with astonishing accuracy. They allow marketers and creators to repurpose content across formats and platforms, producing multiple variations from a single asset. For businesses under pressure to deliver more with less, this shift is a game changer. IF, you know where to look and hopefully this helps:  Descript  an AI powered audio and video editor with features such as overdubbing, filler word removal, multi track editing, and automatic captioning. It’s one of the tools I am considering so I will keep you posted on how it goes. Murf AI generates natural sounding voiceovers for ads, explainer videos, and e-learning, with a wide library of voices. Similar voice AI solutions to consider include Resemble AI  which offers advanced voice cloning and localisation for creating consistent brand voices or adapting content across languages. Some other interesting post production AI include: Aiva  used to compose original music tracks tailored to video projects, presentations, and campaigns. Runway  is a very powerful video editing solution, including background removal, motion tracking, and style transfer. Pictory automatically generates short, shareable videos from long form content, perfect for social media. These tools bring undeniable efficiencies, but they also raise important questions. If AI can tune voices, edit audio, and generate content at scale, where does the line fall between efficiency and authenticity? For professionals, the opportunity lies in combining the speed of AI with the nuance of human judgement, ensuring that creative work retains originality and emotional impact. AI in post production is not about replacing editors, sound engineers, or designers — it is about enabling them to focus on higher value creative direction while delivering projects with greater speed and reach. The Rising Concern of AI All the opportunities AI brings also come with risks — and these should be considered at every stage of the design process. Nowhere is this more visible than in the movie and independent creative sectors, as highlighted by Moviemaker . Concerns range from whether AI-generated content undermines original artistry to whether deepfake technology could be misused to replicate actors without consent. The main challenges include: Copyright and intellectual property : who owns AI generated work, the user, the platform, or the dataset? Ethical concerns : how do we safeguard against misuse and protect original artistry? Ownership challenges : when AI contributes to creative outputs, who takes credit and accountability? Community and job market frustrations : while businesses are under constant pressure to cut costs and drive efficiencies, AI offers significant potential. Yet a human custodian is still required to ensure legal compliance, brand consistency, and quality assurance. At the same time, another tension is emerging. Many creatives and communities feel that design enabled through AI is inauthentic because it was not created by human hands, and because it denies opportunities to those who rely on creative work for their livelihoods. There is an expectation in parts of society that businesses should uphold the creative industries by supporting local talent. However, this is an increasingly unrealistic expectation. Rising business rates, utility costs, marketing spend, wages, and broader operational expenses continue to place pressure on EBITDA. The result is a complex landscape. AI is not simply a creative tool, it is a cultural and economic disruptor .  Designers, businesses, and communities will need to navigate not just the opportunities but also the ethical, legal, and social implications that come with adopting AI at scale.

Product-Led Growth (PLG) is a hot topic, the latest buzzword doing the rounds amongst SaaS start-ups and scale-ups. From the release of books, podcasts and more, SaaS influencers have gone into overdrive positioning it as the next big thing in driving scalable growth. But wait, is putting the product at the heart of lead generation, sales, and retention a new thing? Is it really as revolutionary as it seems? In my experience, while the acronym may be new, many of the principles behind PLG have been in play for quite some time. We will explore the types of PLG with the Logixx Consulting's Product-Led Growth Quadrants . One thing is for sure, bottling up PLG and selling it as the best practice for SaaS businesses is good for start-ups and scale-ups, good for company culture and collaboration and good for businesses seeking to test SaaS products. What is Product-Led Growth? At its core, PLG puts the product at the centre of customer acquisition, conversion, and retention. Instead of being over-reliant on demo-centric sales processes, the PLG approach focuses on providing access and information for users to experience the product directly – the product essentially sells itself, and users have the information and confidence to make an informed purchase decision. This is achieved through tactics like freemium models, free trials, and seamless onboarding, all designed to show value upfront with as little friction as possible. The end goal? To build such a great user experience that customers are more likely to stay, upgrade, and advocate for the product without a salesperson guiding them through the process. At Logixx Consulting, we use our marketing and commercial experience consulting across the DESK Economies and PLG. We will explore the four key aspects that drive PLG success. Demand Creation: Offering Value First PLG starts with creating demand through value, typically delivered via freemium models or free trials. This lets users engage with the product on their own terms. However, knowing your Ideal Customer Profile (ICP) and aligning your product with the needs of that audience is critical. This is where product-market fit becomes essential. Part of that ICP alignment requires product teams to go beyond guessing what the needs of their customers are, to knowing what they want. Using communications and research tools like Jobs To Be Done (JTBD) is a great approach for this. What is Freemium? Freemium is a business model in which SaaS companies offer a basic version of their product or service for free, allowing users to access key features without any initial payment. The objective is to attract a large user base by removing the financial barrier to entry, letting users experience the product and its value. If users engage positively and develop a dependency, then there is a better chance of upgrading them to a paid plan that unlocks additional features or benefits. How are they different from free trials? While both freemium and free trials offer users access to a product without upfront payment, the key difference is the time limitation. A free trial provides full access to all features for a limited period, after which the user must subscribe to continue using the product. In contrast, freemium offers indefinite access to a basic version of the product, but with certain premium features locked behind a paywall. Freemium models focus on long-term user engagement, while free trials aim to convert users quickly after they’ve experienced the full product's value. What is Ideal Customer Profile (ICP)? The Ideal Customer Profile (ICP) is a detailed description of the type of customer that would benefit most from a product or service and bring the most value to the business. It includes demographic, firmographic, and behavioural characteristics such as company size, industry, budget, and specific challenges the customer faces. Defining an ICP helps businesses tailor their marketing, sales, and product efforts to target the right audience, ensuring better conversion rates and more efficient resource allocation. In SaaS, a well-defined ICP leads to higher customer satisfaction and retention. What is Jobs To Be Done (JTBD) and how does this dovetail into the ICP approach? Jobs To Be Done (JTBD) is a framework for understanding customer needs by focusing on the “job” or task they are trying to accomplish with a product. Instead of concentrating solely on demographics or product features, JTBD asks, “What problem is the customer trying to solve?” This helps businesses design products that address specific needs or pain points. JTBD dovetails with the ICP approach by aligning customer motivations and objectives with the product’s value proposition. When both ICP and JTBD are used together, companies can build products that are precisely targeted to help ideal customers achieve their goals efficiently. Throughout this post we will highlight the market leaders making a success of PLG. A prime example is Slack, which disrupted internal team communication. Its freemium model gives teams a taste of the core product, offering just enough value to keep users engaged while prompting them toward paid plans which include advanced features. This strategy has been highly successful because Slack allows users to experience the product’s value independently before any sales intervention. The key to their success is being able to create simple and effective usability offering value first and then showcasing what is possible within the paid version. PLG focuses on users onboarding themselves. Signing up and getting started must be seamless and without barriers. This ease of access, often accompanied by interactive demos or guided tours, shortens the time from discovery to adoption. Community provides a human and informative aspect of the brand going beyond great usability, template and demoing tools. Showing users that there are others like them, engaging, asking similar questions, and collectively sharing experiences that others can relate to. For PLG teams this is a gold mine for product insights. Lead Maximisation: Engaging Users Throughout the Journey Getting users into your product is just the beginning; maximising leads is where PLG strategies really shine. While this sounds simple, compared to traditional sales processes, getting leads to actively use the product was once a huge milestone in itself. Now, PLG transforms that starting point into a continuous opportunity for engagement and growth. Progressive Disclosure plays a critical role in driving more effective and data-driven PLG insights by personalising user experiences and gradually revealing value in a way that suits their needs, giving customers what they need without human sales intervention. We see progressive disclosure tactics being deployed at two key stages across registration and activation. Progressive Disclosure within the PLG Registration Process When registering for freemium or free trials, capturing data related to a user’s ICP and their Jobs To Be Done (JTBD) is crucial. Progressive disclosure, a method where information is requested based on previous answers, is an excellent approach for this. Monday.com   does this brilliantly, asking users targeted questions during the registration process. This ensures they gather meaningful data about the user’s ICP and JTBD before the user even starts engaging with the product. By tailoring this early stage of the user journey, Monday.com  maximises lead data and increases the chances of conversion. Progressive Disclosure Post-Activation Keeping users engaged post-activation by gradually unveiling various aspects of product’s full value is another key element of PLG. During onboarding, features and benefits are introduced in small, digestible steps, keeping the experience smooth and frictionless.  Monday.com , again, excels here by asking ICP and JTBD-related questions not just at registration, but throughout onboarding. This maximises data collection, allowing the platform to customise the user experience as they explore the product. This phase of PLG uses tactics such as: In-app tutorials  that guide users through specific tasks. Live chat , knowledge bases, or community forums to offer timely support. Tooltips  that provide contextual help based on real-time usage. Another example is Notion, another early PLG adopter, that relies on progressive disclosure to help users discover its deeper functionality over time. They also incorporate product gamification into their onboarding, offering badges or milestones as users complete tasks. This turns casual users into power users by incentivising deeper exploration of the product. The sense of reward and accomplishment strengthens the bond between user and product, encouraging word of mouth, reviews, and even user-generated case studies. To truly maximise leads, businesses should track product analytics to understand user behaviour. These insights enable personalised, timely interactions that can significantly improve engagement and drive conversions. Activation Rate: Converting Leads Into Engaged Users The activation rate is one of the most critical metrics in PLG. It reflects how effectively the product is converting free users into active users – those who have experienced enough value to continue using it. This is where the most actionable product insights can be captured and tracked. The ability of product and IT teams to mine these insights, and the effectiveness with which marketing and sales teams use them is key to maximising growth. Activation doesn’t happen by chance. It requires constant testing and refinement of onboarding flows through A/B testing and user segmentation. For example, Canva , a graphic design tool, measures activation by how quickly users create and download their first design. Through experimentation, Canva discovered that showing users template suggestions during onboarding drastically improved conversion rates. This is a classic example of how reducing friction and providing immediate value can enhance product adoption. User feedback is essential to understanding where friction might exist. A mix of surveys, sentiment analysis, and in-app prompts can reveal pain points that hinder activation. Those who don’t convert are often segmented into re-engagement funnels, with personalised messaging and targeted campaigns designed to bring them back into the product. Sales Enablement Opportunities: Blending Product Data with Traditional Sales While PLG focuses on letting the product do the heavy lifting, sales teams still play a vital role, especially as leads move toward higher-value, enterprise-level solutions. The relationship between PLG and traditional sales isn’t either/or; it’s about maximising the product’s value to drive revenue growth. While the product can handle much of the user’s journey, there are some things that only salespeople and personalised conversations can achieve. In fact, PLG makes sales enablement even more powerful by using product usage data to qualify and prioritise leads. Thanks to progressive disclosure and product analytics, sales teams already have valuable insights into how prospects are engaging with the product and where their pain points lie by the time they step in. For example, sales teams at Atlassian use insights such as which features a user has adopted and how frequently they log in to tailor their outreach. This allows sales reps to provide highly relevant solutions rather than delivering generic sales pitches. This phase of PLG is also about creating a seamless integration between in-app messaging, email campaigns, and traditional sales efforts. Automated communication strategies deliver personalised messages when users hit specific milestones or show signs of churn, ensuring timely engagement. Sales teams then step in to focus on upselling, cross-selling, and supporting users who require more complex or customised solutions. Sales Enablement Opportunities: Blending Product Data with Traditional Sales While PLG focuses on letting the product drive much of the growth, sales teams still play a vital role, especially for complex or enterprise-level deals. The relationship between PLG and traditional sales is not an either/or scenario; it's about leveraging the product to maximise revenue. Some things only human interaction—sales calls, demos, and deeper conversations—can achieve. PLG, however, makes sales enablement even more powerful by using product usage data to qualify and prioritise leads. By the time the sales team steps in, they already have a wealth of data on how the prospect has engaged with the product and where their pain points lie. For example, sales teams at Atlassian use insights like which features a user has adopted and how often they log in to tailor their outreach. This approach enables sales reps to offer highly relevant, personalised solutions, rather than relying on general pitches. This phase of PLG also requires creating a seamless integration between in-app messaging, email campaigns, and traditional sales efforts. Automated communication strategies deliver personalised messages when users hit specific milestones or show signs of churn. Sales teams can then focus on upselling, cross-selling, and supporting users with more complex needs, ensuring long-term growth and customer retention. Why Does Product-Led Growth Seem Like a New Approach? With PLG being such a hot topic in SaaS, it's easy to see why people perceive it as a radically new concept. In reality, PLG is more about rethinking how we use existing tools and data than inventing something brand new—a concept familiar to digital marketers and conversion specialists. So, why does it seem new? Here’s why: From Sales-Led to Product-Led In traditional models, SaaS companies were sales-led, with outbound strategies like cold calls and demos guiding the process. The product often wasn't experienced by users until late in the sales cycle. PLG flips this approach by putting the product in the user’s hands from the start, allowing them to experience value immediately. Sales teams still play a crucial role, but they now step in at key points in the user journey—such as post-activation—instead of driving the process from the outset. Data-Driven Decision Making What feels new about PLG is the real-time access to data. Companies can now track every interaction within their product, enabling data-driven decisions on product improvements, user engagement, and lead qualification. In the past, teams relied on feedback, surveys, and reports that took weeks to gather. Today, tools like Mixpanel and Heap offer instant insights into user behaviour, allowing businesses to pivot quickly when needed. Cross-Department Collaboration It's not unfamiliar that SaaS businesses experience silos between marketing, sales, product and customer success teams. The silos would be marketing generating leads, sales converting the leads, and customer success handled retention. PLG fosters cross-department collaboration by uniting these teams around the product. The garden walls are broken down by a culture of collaboration. Marketing focuses on driving product adoption, sales step in post-activation leading on the more complex sales that require a human relationship, and customer success ensures long-term engagement. This unified approach creates a more holistic customer journey. Important Note:  While freemium and free trials work for most businesses, they may not be suitable for enterprise clients or heavily regulated industries, where new vendor processes can be complex. Here, sales teams are indispensable, navigating stakeholder management and the myriad of compliance requirements, like ISO certifications, information security, SSO, and integrations. Real-World Examples of Product-Led Growth in Action To understand how PLG works in practice, let's look at a few standout examples : Monday.com , Canva , and Wix . Each has leveraged PLG to achieve significant growth by offering a seamless, product-first experience. Monday.com : Collaborative Work Management Monday.com  uses a freemium model that allows small teams to sign up, onboard themselves, and start managing projects immediately. Tailored onboarding ensures users experience immediate value, boosting the likelihood of conversion to paid plans. Canva: Democratising Design Canva  offers a free version that lets users design without paying upfront. Activation is tracked by how quickly users create and download their first design. In-app prompts introduce premium features when users are most likely to benefit, creating a natural upsell process. Wix: Customisable Website Creation Wix  offers both a freemium experience and a guided process using Wix ADI. Its customisable onboarding flow caters to different user personas, from novices to experienced users. In-app tutorials guide users through features, and premium offerings are introduced based on user engagement. How PLG Differs from Traditional Approaches PLG may feel new, but it’s more of an evolution than a radical departure from traditional SaaS models. Here’s how PLG differs: Sales-Led Growth vs Product-Led Growth In sales-led models, companies relied on high-touch, outbound sales efforts. Sales controlled the funnel, and users engaged with the product later in the cycle. PLG flips this dynamic, allowing the product to drive much of the acquisition and engagement, with sales stepping in later. Marketing’s New Role In traditional models, marketing focused on lead generation. In PLG, marketing takes on a broader role, focusing on driving adoption through education and tailored onboarding. The Funnel vs The Flywheel Traditional sales models view the customer journey as a funnel with a distinct end. PLG adopts a flywheel approach where engaged users return to the product, refer others, and generate organic growth, creating a self-sustaining cycle. Conclusion: PLG is Here to Stay PLG isn't just a buzzword—it’s a strategic shift that puts the product at the heart of revenue generation and lead acquisition. It’s built on a framework of continuous testing, learning, and evolving, making it particularly suited to SaaS environments. For companies like Monday.com , Canva , and Wix , PLG has been a driving force behind rapid, scalable growth. Their success proves that putting the product at the centre isn’t just a trend—it’s a sustainable, long-term strategy that aligns sales , marketing , and product teams around a shared goal: maximising user value and driving growth.

The growth potential of the majority of businesses is determined by their decisions, operating model, and relevance to their target audience. However, there are various external factors such as the DESK economies, which include Digital, Experience, Service, and Knowledge economies. Companies that can maximise their presence and competitiveness across this quadrant will achieve commercial and brand success. Digital Economy The digital economy involves economic activities that connect people, communities, and businesses with their devices, data, and technology. This economy is built on synergistic connectivity—realised through machines and technologies, benefiting both audiences and businesses. Experience Economy Consumers increasingly value unique and engaging experiences. Digital transformation allows businesses to create immersive and personalised customer experiences through technologies such as virtual reality (VR), augmented reality (AR), and interactive platforms. Service Economy The service economy focuses on revenue generated through offerings that add value to businesses, people, governments, and communities. These offerings help organisations fulfil their commitments and enable consumers to benefit from knowledge, skills, expertise, and various professional solutions. Knowledge Economy With digital transformation, businesses can better manage and utilise knowledge assets. This includes leveraging data analytics for better decision-making, enhancing intellectual capital, and fostering innovation through collaboration tools and platforms. The Impact of the DESK Economies on Growth Hacking and Digital Transformation Growth hacking and digital transformation are pivotal in navigating the complexities of the DESK economies. Effective growth hacking and digital transformation projects should factor in the challenges presented by the DESK economies. Growth hacking leverages innovative strategies to rapidly scale businesses, while digital transformation integrates technology into all aspects of operations to stay relevant to new and existing audiences. This translates into greater product and service demand, driving optimum revenue potential. Powered by data, enriched with engagement, and delivering reach, its success ultimately hinges on complete business-wide adoption, technology selection, and data opportunities. Growth hacking complements this by identifying new ways to market and new service offerings. Together, they form a powerful combination that can guide businesses through the changing landscape towards growth. In the Digital Economy Consultants thrive by navigating businesses through the dynamic world of e-commerce, digital marketing, and data analytics. They can evaluate existing digital ecosystems to provide a vision for a digitally enabled future. This includes embracing AI services and how it can accelerate data insights, strategies and automation, machine learning, content creation, and digital media and marketing trends. Within the Service Economy Consultants embrace the challenge of improving efficiency and personalisation. They utilise advanced automation and AI tools to ensure organisations meet their commitments and deliver exceptional value to customers. In the Knowledge Economy Consultants excel at enabling businesses to leverage intellectual capital effectively. They facilitate the adoption of collaboration tools and platforms, helping organisations utilise data analytics for strategic decision-making and cultivating a culture of continuous innovation. Consultants also use marketing techniques to generate interest and intrigue in their knowledge, which translates into commercial growth. In the Experience Economy Consultants design strategies to create unique and engaging customer experiences. They focus on adapting to the channels and communication methods that audiences are utilising, incorporating technologies like VR, AR, and interactive platforms to build strong emotional connections with customers, enhancing brand loyalty and market differentiation. By integrating growth hacking techniques with digital transformation strategies, and leveraging the expertise of consultants adept at navigating the challenges of the DESK economies, businesses can achieve remarkable growth and maintain a competitive edge in today’s rapidly evolving market.

Considering a Marketing Consultant to Grow Your Business? If you’ve been on the fence about whether to bring a marketing consultant on board, let me tell you why it’s one of the smartest moves you can make for your business. This isn't about fluffy benefits or generic AI-generated advantages. We’re diving into the real, impactful reasons that hiring a marketing consultant can stimulate your growth, creativity, and data-driven opportunities. Logixx Consulted was founded on the experience of seeing the direct impact a marketing consultant can have on a range of marketing teams and businesses. Here are some of my personal experiences of how I have made a difference to businesses. Unbiased Expertise One of the biggest advantages of a marketing consultant is their objectivity. Internal marketing teams, while valuable, can sometimes develop tunnel vision. Heavily influenced by bonus-inspired KPIs, over-reliance on agencies, and being entrenched in the company culture, they can struggle to see beyond the existing frameworks and strategies. A marketing consultant, however, is an outsider. They bring fresh eyes and can identify opportunities and challenges that might be invisible (consciously or unconsciously) to those on the inside. With those fresh eyes comes the licence to ask tough questions relating to the ideal customer profile (ICP), attribution, creative execution, sales strategies, product marketing, and much more. Focused on Growth  Consultants are results-driven, hired on a promise to deliver direction and growth. A task that has various routes to success, such as increasing market share, evaluating martech (marketing technology), sales tech, and CRM (Customer Relationship Management), improving customer retention, or boosting brand awareness, to name a few. The larger projects include digital and business transformation . They’re not bogged down and influenced by internal politics, relationships, or other distractions. Their sole focus is on creating and executing strategies that lead to measurable improvements. This means they’ll do whatever it takes to get the job done, whether that involves working with your product teams to influence the product roadmap, creating improvements within the sales experience, or devising new marketing campaigns to reach untapped audiences. Versatility Across Stages and Maturity Levels  Whether you’re a startup trying to make your mark or a mature company looking to reinvigorate your brand, a marketing consultant can tailor their approach to fit your needs—something that Logixx Consulting takes huge pride in. They’re adept at navigating different stages of business growth and can adapt their strategies accordingly. For startups, this might mean creating a go-to-market strategy from scratch, with limited resources, best practices, and budget. For established businesses, it could involve refining existing strategies or even integrating marketing efforts during a merger or acquisition. Some established marketing practitioners prefer to work within their respective preferred environments. Some like big brands, with big budgets. Some might like slugging it out as part of a startup. But marketing consultants do not have a preference. The only preference is the most efficient way of tackling growth and change. Strategic Planning and Execution  A well-rounded, experienced marketing consultant doesn’t just come up with ideas; they have the experience of implementing them. They can develop a comprehensive marketing strategy that includes everything from market research and competitor analysis to branding and digital marketing. But it doesn’t stop there. They’ll help you build a team to execute the strategy, ensuring that the right people are in place to carry out the plan. This might involve hiring new talent, training existing employees, or even outsourcing certain tasks. Measuring and Evolving Strategies  The best marketing consultants know that a strategy is only part of the story. They’re committed to implementing, measuring the success of their initiatives, and adapting to trends, performance, and feedback. Having a track record of leading the end-to-end marketing process as part of a growth hacking process is where the best consultants make a lasting impact on businesses. This breadth of experience allows the consultant to tap into years of experience dealing with different business models, sales processes, revenue operations, lead generation, customer and client services, performance metrics, and reporting. Cost-Effective Expertise  Hiring a full-time marketing VP/CMO can be expensive, especially for small to medium-sized businesses. A marketing consultant offers a more flexible and cost-effective solution, but just as much (or in many cases) more experience of driving business change and fresh thinking. You get access to high-level expertise without the overhead costs associated with a permanent hire. This flexibility means you can scale your marketing efforts up or down as needed, without the long-term commitment. The interesting added benefit here is that it suits both parties. Consultants don’t want a permanent position; instead, they want the permanent opportunity to go into businesses, make a difference, and understand their impact (if they are there long enough). Fresh Thinking and (sometimes) Innovation  Staying ahead of the competition requires fresh thinking and sometimes an innovative approach. No business can be innovative all the time and most of the time innovation isn’t the answer. Sometimes, doing the simple things better, better aligned and well executed is enough to move the dial and to stay ahead of the competitors. Usually, innovation requires multiple departments to bring it to life. But whether it's fresh thinking or an innovative approach, marketing consultants can rely on their ability to understand the latest trends, tools, and technologies. Enhanced Collaboration  Good marketing consultants know that collaboration is key and getting the best out of people and stakeholders is essential. Sometimes working in isolation is required, but generally, they need to partner closely with your internal teams, getting a real honest perspective of the inner workings of the organisation from every level of worker. This includes working with product teams to align marketing efforts with product development, research or BETA community engagement. Collaborating with sales teams to ensure marketing strategies drive sales and that sales materials are providing a meaningful sales experience. Working with customer service teams to understand consumer sentiment and insights, delivering customer research or NPS (Net Promoter Score). This holistic approach ensures that all aspects of your business are working together towards the common goal, growth. Navigating Mergers and Acquisitions  During mergers and acquisitions, marketing can often be an afterthought. Surprising given that in Spring 2024 research from Deloitte highlighted that marketing budgets are a consistent 13.6% of total revenue. Marketing is a huge cost to a business and a marketing consultant can help navigate the complexities surrounding branding, communication, and customer engagement. They’ll develop strategies to merge marketing teams, evaluate technologies and agencies, unify brand messaging, and maintain customer loyalty and retention during the transition. Building a Sustainable Marketing Infrastructure   Up to this point, the majority of the points have been on business growth. However, something that can’t be overlooked is the impact a marketing consultant can have to not only tackle growth challenges but simultaneously build a sustainable marketing infrastructure. This includes setting up marketing automation, running PPC campaigns, creating content, developing and maintaining content calendars, establishing KPIs, and implementing reporting mechanisms. These elements are crucial for ongoing success as they are the BAU (Business as usual) foundations that campaigns and branding are built upon. Cross-Departmental Integration  A marketing consultant understands that marketing doesn’t operate in a vacuum. However, it's common to hear from businesses that cross-department relationships are fractured and marketing is an effective walled garden. For a strategy to be truly effective, it needs to be integrated across all departments, by people, for the people and enabled through technology and the spirit of collaboration. This means working closely with sales, product development, customer service, and even HR to ensure that marketing efforts are aligned with the company’s overall objectives. A consultant ensures that everyone is working towards the same goals and that marketing strategies are supported across the board. Marketing is there to enable the brand, its product, and people to flourish with the knowing that everyone can get behind the plans. Customer-Centric Approach  A marketing consultant's addition to growth can only be achieved in the long run if the foundations and execution are centred around the audience. A marketing consultant brings a customer-centric mindset to the business because this is the lifeblood of a successful consultant. Everything a marketing consultant offers boils down to how a product or service is marketed and how that investment can demonstrate incremental revenue. This is why they prioritise understanding the customer’s needs, preferences, and behaviours. This insight is used to tailor marketing efforts to attract and retain customers more effectively. By aligning marketing strategies with customer expectations, consultants help businesses create more personalised and impactful marketing campaigns. Data-Driven Decision Making  In marketing, we’ve seen trends come and go, with each year often dubbed “the year of” something new—be it mobile, social media, video, or personalisation. Recently, the focus has shifted to data, making it arguably “the year of data.” Marketing consultants excel at leveraging data to make informed decisions. They use analytics to track the performance of marketing campaigns, understand customer behaviour, and measure impact and ROI using a range of attribution models. This data-driven approach ensures that marketing strategies are continuously optimised for better results. Building a Marketing Culture  At times, the role of a marketing consultant is to become the cheerleader. Leading the merry dance in an attempt to build a culture that is excited by what marketing can do for them and the company they work for. Creating an environment where everyone understands the importance of marketing and how it contributes to the business’s success. It involves training and educating employees, new starters, and across departments about the marketing principles and practices, ensuring that marketing becomes a shared responsibility. Crisis Management and PR  In times of crisis, having a marketing consultant can be invaluable. They can develop and implement crisis communication strategies that protect the brand’s reputation and maintain customer trust. This includes managing public relations, crafting appropriate messaging, and ensuring that all communication channels are aligned and responsive. Conclusion Bringing a marketing consultant on board is not just a smart move; it’s an investment in the future of your business. With their unbiased expertise, they can see the opportunities and challenges that may be invisible to your internal team. Their focus on growth ensures they are always striving for measurable improvements, that are just as much data-led and customer-influenced, and their versatility means they can adapt to the specific needs of your business, no matter its stage or maturity. A marketing consultant doesn’t just develop strategies; they execute them. Their ability to strategically plan and implement ensures that the ideas are not only innovative but also actionable. There is no point having a Rolls-Royce on the driveway if you don’t know how to drive it, and it's the same with a marketing strategy—there is no point having one if you can’t or won’t action it. By measuring and evolving strategies, they make sure your marketing efforts are always optimised for better results. Their presence is a cost-effective alternative to a full-time executive, providing high-level expertise without the overhead. Marketing consultants are champions of fresh thinking and innovation, always staying ahead of the curve with the latest trends and technologies. Their ability to ask the tough questions whilst being able to enhance collaboration across departments ensures that marketing efforts are aligned with the company’s overall goals and growth potential, enabling the brand, its products, and its people to flourish with confidence. In times of mergers and acquisitions, their skills in navigating complex transitions are invaluable, ensuring a seamless integration of marketing efforts and consolidating costs, data, and processes. They also build a sustainable marketing infrastructure, setting up systems and processes that ensure long-term success. A truly effective marketing strategy requires cross-departmental integration, and consultants excel in breaking down silos and embracing collaboration. Their customer-centric approach ensures that all efforts are aligned with customer needs and expectations, leading to more personalised and impactful campaigns. In today’s data-driven world, their expertise in leveraging analytics ensures that decisions are informed and strategies are continuously optimised. By building a marketing culture within your organisation, they create an environment where everyone understands the importance of marketing. And in times of crisis, their ability to manage communications and PR protects your brand’s reputation and maintains customer trust. In conclusion, a marketing consultant is not just a temporary solution; they are a catalyst for sustainable growth, innovation, and success. So, if you’re still on the fence, remember that the right marketing consultant can transform your business in ways you never thought possible. Ready to talk? Let’s make it happen.