“…AI engines are very, very good at doing web search and discovery for you. They present you with an answer to your shopping questions — price and features and reviews — and save you time.” – Adam Brotman, former Starbucks exec and Forum3 AI co-founder. 

This quote sums up the pace of change in how we’re all discovering and evaluating products and services. Markets are increasingly competitive and more dynamic. Buyer journeys are less visible, and the way people discover, evaluate and choose products or services no longer follows the patterns many businesses are used to.

Despite this, many teams are still working from a familiar playbook, scaling what worked before, increasing spend in established channels and relying on agency partners to drive more output. 

This chimes with our own recent experience here at OV – never before have we been asked to evaluate incumbent agency partner performance as much as we have in the past 12 months, with brands looking to squeeze every last drop from their existing approach. However, this is increasingly less effective as both markets and buyer behaviour shift.

What once felt like a reliable path to growth now produces weaker returns. Channels begin to plateau, performance is harder to interpret and activity increases without the same level of impact. We have explored this shift in more detail here.

At the centre of this is a gap between how buyers now behave and how most marketing and growth strategies are still set up to operate.

What is changing: AI, LLMs and the end of siloed discovery

AI is changing how buyers approach decisions, from how they search through to how they evaluate and shortlist options.

LLMs are increasingly acting as a single interface for discovery, evaluation and shortlisting. Instead of moving between search engines, websites, comparison platforms and peer recommendations, buyers are consolidating these steps into fewer interactions. 

As an example, one of our clients is a global travel company, with a large proportion of its customers in the US but the product delivered across several continents (mainly Europe). The power of LLMs, and AI agents, to act as a single source of discovery is having a significant impact on behaviour – a survey recently found travel planning as the most trusted use of Gen AI. It’s enabling people to identify quieter times to travel, less touristy spots off the beaten track, or reducing the amount of time needed to build itineraries. 

This is impacting the way people are booking their flights, hotels and tours, with people increasingly better informed and able to be more precise with what they do and when they do it. This is also giving rise to people having greater confidence to wait until they’re at their destination to book – no longer are time zones, foreign languages or the lack of human support the limitations they once were. So much so that in the past 12 months alone, research found that as many as 40% of travelers globally have used AI to help plan their trip.

This in turn has significant implications for the businesses selling these travel products. And, when you consider the combined power of these behavioural shifts, with the social integration of apps like WhatsApp and Instagram and the cumulative source of online reviews, the customer is not just better informed, the entire experience of booking travel is changing. 

Search engines remain dominant and other channels still play an important role, but the path forward is becoming clear as these behaviours continue to evolve.

Alongside this, a new pattern is emerging. Early-stage research is increasingly being delegated to AI tools and AI co-pilots, often described as System 0 thinking. Initial exploration, filtering and comparison happen before a buyer engages directly with a brand.

This shift has several implications.

• Visibility is changing. If your proposition is not clearly understood and represented in the sources AI draws from, you are less likely to appear in early consideration.
• Influence is shifting. The inputs that shape AI-generated responses carry more weight than individual channel performance in isolation.
• Differentiation becomes harder to signal. Where multiple providers appear similar at a surface level, AI will tend to group them into comparable options.

Other marketing channels still matter, but their role in discovery and decision making is changing as these behaviours evolve.

The activity trap

One of the most common responses to declining performance is to increase investment in existing channels, adding more paid media, more content and more campaigns in an attempt to regain momentum.

This can feel like a sensible response, particularly when competition appears to be intensifying, but it often misses the underlying shift in how buyers are behaving.

If discovery is changing, simply increasing visibility within the old model is unlikely to restore growth and can instead reinforce the problem.

The result is a familiar pattern where marketing activity increases, costs rise and teams become busier, but growth doesn’t follow at the same rate. Again, we’ve written about this – the Orchestration Gap – here. 

At the same time, expectations are rising. Core channels are becoming less effective on their own, and the level of customer understanding required to compete is increasing. Buyers expect relevance, clarity and confidence throughout their decision making, placing greater emphasis on understanding real customer needs and behaviour.

The cost of getting this wrong is high, as it becomes harder to build a sustainable growth system. Lead generation becomes less predictable, conversion is more difficult to influence and investment is harder to justify.

What often looks like a channel problem is usually a reflection of how the growth strategy is structured.

How to respond to changing buyer behaviour

Adapting to this shift doesn’t require abandoning everything that has worked before, but it does require a more deliberate approach to how growth is structured and prioritised.

Challenge

The starting point is to challenge existing assumptions. Many businesses are still investing in channels, budgets and activity patterns that reflect how buyers behaved in the past, rather than how they make decisions today. It becomes important to assess which channels are genuinely influencing decisions, how budget is being allocated against current behaviour and where visibility is strengthening or starting to decline. The aim is to move beyond activity and build a clearer view of what is actually driving growth.

Clarify

Clarity comes from aligning your proposition with how buyers now navigate the market. That means being explicit about what you offer, who it is for and why it matters in a commercial context. It also means ensuring that value is clearly represented in the signals external systems rely on, strengthening how it is expressed across the sources that inform AI-driven discovery and expanding your approach beyond traditional SEO to include Generative Engine Optimisation (GEO). The goal is to make your relevance easier to recognise and harder to overlook.

Grow

Growth then becomes a question of structure and alignment. Fragmented execution is replaced with a more deliberate system, where teams, channels and investment are aligned around how buyers are actually influenced. This includes prioritising the levers that reflect System 0 thinking and building a model that can adapt as behaviour continues to evolve, rather than relying on patterns that are becoming less reliable over time. 

At OV, we have a fashion-based retail client and recently carried out a research project to learn more about their customers’ evolving digital habits. We had over 2,000 responses and the results were striking. Of the three core target segments the brand is trying to reach, 2 have yet to adopt System 0 thinking; in this context, the use of AI to scout preferred fashion websites looking for certain items, outfits, or discounts. However, one target segment definitely has with the use of ChatGPT having emerged from almost nowhere 12 months ago to now being listed as one of the top 4 or 5 sources used to identify what to buy next. 

The result has been honest conversations about the amount of effort that’s now required to be discoverable through ChatGPT, and similar LLMs, with resource and budget conversations now fully informed by this rapidly changing behaviour. 

One fact remains clear amongst the evolution in discovery and search behaviour – understanding what your target customer(s) or buyers want is fundamental to making the right calls. 

Start with a conversation

For leadership teams facing these challenges, our free growth clinic can be a good place to start. Its offer is simple – there’s no pitch, just expert perspective on these (and other) growth challenges. 

If you want to explore this in more detail, you can book a session with one of our senior partners here.

Definitions

LLM vs generative AI
Generative AI is the broader category of systems that can create content, including text, images and code. Large language models are a specific type of generative AI focused on understanding and generating human language.

What is product discovery
Product discovery is the process by which potential buyers become aware of, explore and evaluate solutions that could meet their needs.

AI co-pilots
AI co-pilots are tools that assist users by generating insights, recommendations or outputs, often taking on early-stage research and analysis tasks.

Q&A

What is a marketing playbook?
A marketing playbook is a set of established tactics, channels and approaches that a business uses to drive growth, often based on what has worked historically.

What is System 0z thinking?
System 0 thinking refers to the use of AI tools to carry out initial research, filtering and synthesis before a human engages more deeply in a decision. It builds on Daniel Kahneman’s award winning work in defining System 1 and System 2 thinking (thinking fast, thinking slow) as a way to understand how people make buying decisions. 

How can you incorporate changing buyer behaviour and use of AI into your marketing and growth strategy?
Start by understanding how your customers are using AI in their decision making. Then align your proposition, visibility and content with the sources and signals these systems rely on. Finally, ensure your overall growth model reflects how influence now occurs, rather than how it worked in the past.