I speak to a lot of marketing managers who tell me the same thing in slightly different words. They know something has changed in search, they can feel it in their reporting, but nobody’s given them a clear, practical plan for what to actually do differently on a Monday morning.
I completely understand that feeling, because search has genuinely fragmented faster than most internal processes have been able to keep up with.
I run Essheo, a search marketing agency working across the UK and US, and this is the exact conversation I have on most of my discovery calls. What I want to do here is set out, plainly and practically, what I think marketing managers actually need to prepare for, based on where the research and the data genuinely point, rather than vague warnings about “the future of AI.”
What Is Actually Changing About How People Search?
The starting point has to be an honest look at consumer behaviour, because that’s what everything else follows from. McKinsey’s research found that half of all consumers now use AI when searching the internet, and for many, discovery through AI genuinely carries through into the actual purchase decision, not just early research.
Separate research from the 2026 AI and Search Behaviour Study found more than one in three consumers now start their search journey with an AI tool rather than a traditional search engine, with 60% saying AI delivers clearer, more helpful answers than a standard results page.
Deloitte’s research into agentic commerce found that nine in ten retail executives expect AI to be used more than traditional search engines within the near term, and half expect today’s multi-step shopping journey, browse, compare, decide, buy, to collapse into a single AI-mediated interaction by 2027.
I think that collapse point is worth sitting with for a moment, because it means the businesses winning in a category won’t necessarily be the ones with the best website. They’ll be the ones an AI agent trusts enough to recommend directly.
How Big a Shift Is Agentic Commerce Specifically?
This is the part of the future that I think most marketing teams still underestimate. Shopify reported that AI-driven traffic to its stores grew eight times year over year in Q1 2026, orders originating from AI-powered searches rose nearly thirteen times over the same period, and new buyers placed orders through AI channels at nearly twice the rate of other channels.
Industry forecasting suggests that by the end of 2027, a measurable share of ecommerce checkouts, plausibly 8 to 15% of direct-to-consumer transactions, will happen entirely inside a chat interface, with no visit to a merchant website at all.
I’d stress that this isn’t a distant, speculative scenario. It’s already measurably underway, and the gap between merchants who’ve prepared their product data properly and those who haven’t is set to widen further.
Analysis of this shift found that by 2027, merchants whose product content lives purely in unstructured description text will lose ground to merchants shipping proper structured Product, FAQPage, HowTo and variant-complete schema markup that AI agents can actually parse and compare.
Will AI Agents Genuinely Act on a Customer’s Behalf?
Increasingly, yes, and the direction of travel is fairly clear even if the exact timeline isn’t. Research into this shift describes AI agents evolving to remember previous conversations, tailor recommendations to a stated budget or context, and by 2027, potentially evaluate products, compare documentation and features, and even negotiate pricing without direct human involvement in business contexts.
For consumer retail specifically, the same research expects AI systems to increasingly browse, compare and transact on a shopper’s behalf with little to no direct human involvement.
I don’t think marketing managers need to solve for every one of these scenarios today. But I do think it’s worth genuinely understanding that the target of your marketing activity is quietly shifting from “the person searching” to “the AI system that person has delegated the search to.” That’s a different audience with different requirements, and preparing your content and data for it now is considerably easier than retrofitting it under pressure later.
Is Your Marketing Team Actually Ready for This Shift?
I think the honest answer, based on the research, is that most teams aren’t yet, and that’s not a criticism, it’s simply where the industry currently sits.
What Does the Data Say About the Current Skills Gap?
It’s substantial. Research from Scrunch surveying more than 600 marketers found that 58% say their training isn’t keeping up with the pace of change in AI search, 57% of teams aren’t planning to upskill employees on this specifically, and 56% aren’t planning to develop or formalise any AI visibility policy at all.
Separate research into B2B marketing found 60% of marketers cite training as the primary barrier to deeper AI adoption, with 47% citing a lack of internal expertise, well ahead of budget constraints at just 25%.
Search Engine Land’s research, surveying 150 marketers alongside over 1,000 consumers, found the single biggest barrier to deeper AI integration in marketing is team training and skill gaps at 26%, ahead of tool fragmentation at 20% and budget constraints at 19%.
I think that ordering is genuinely revealing. Most marketing teams aren’t short of tools or even budget right now. They’re short of people who properly understand how to use what’s available.
Why Does This Gap Matter So Much Right Now?
Because confidence and competence have quietly diverged. The same Scrunch research described this gap plainly, finding that marketers’ confidence in their AI search capability is currently outpacing their actual competence.
That’s a genuinely risky position for a marketing manager to be in when reporting up to a board, because it means teams may believe they’re covering AI visibility adequately when the data suggests otherwise.
Digiday’s research into this same trend found that while marketers’ adoption of AI tools has risen sharply, training on how to properly use those tools has consistently lagged behind. I see this constantly in client audits. A team is using an AI content tool or a citation tracker, but nobody has actually built the underlying strategy or measurement discipline around it, so the tool produces activity without producing a clear, defensible result.
How Should Marketing Teams Actually Be Restructured for This?
This is where I think practical guidance genuinely helps, because the good news is that most businesses don’t need to hire an entirely new department to get this right.
Do You Need to Hire a Whole New Team?
Not necessarily, and I’d actively push back on any agency telling you otherwise purely to sell headcount. Guidance from Search Engine Journal argues convincingly that most businesses don’t need new headcount at all, but do need clear scope changes and a definite answer to who owns this. Their recommendation is straightforward: your existing SEO lead becomes your AI search lead, usually the same person, with their scope expanding from “where do we rank” to “where do we get cited”.
In smaller teams, ownership typically sits with whoever already owns demand generation. In larger organisations, it’s worth having a senior marketing leader hold this personally until the approach is proven, because a function this new gets orphaned quickly if it’s buried three layers down in the reporting structure.
Separate governance research recommends every team have one person who is unambiguously “definitely responsible” for AI visibility outcomes, owning the monitoring cadence, coordinating input across content, PR, brand and development, and reporting results up to leadership.
In small companies this typically falls to the SEO lead or head of content with expanded scope. In mid-market businesses it’s often a senior digital marketer taking on a dedicated remit. In larger enterprises, it increasingly justifies a dedicated AI Search Director or Head of GEO role.
What Should the First 90 Days Actually Look Like?
I’d recommend a structured, measured approach rather than scattered activity, and this closely mirrors how we run new client engagements at Essheo. A sensible first month should focus purely on establishing your baseline: testing your top 15 to 20 buyer queries across ChatGPT, Gemini, Perplexity and Google’s AI Overviews, and recording every answer, every citation, and every competitor named.
Only once that baseline exists should you move into structured experimentation, testing specific content and structural changes against measurable citation and visibility outcomes, before scaling whatever demonstrably works.
I’d genuinely caution against skipping straight to content production without this baseline step. I’ve reviewed too many internal AI visibility efforts that produced plenty of new content with no way of proving whether any of it actually moved the needle, simply because nobody measured the starting position first.
How Should Success Actually Be Measured Going Forward?
Differently to how most teams currently measure organic performance. Current guidance strongly recommends tracking AI visibility across ChatGPT, Gemini, Perplexity and Google’s AI Overviews as genuinely separate, distinct platforms requiring their own monitoring, because your Google rankings simply don’t transfer automatically into AI search visibility on other platforms.
This is precisely the multi-platform view we build into every client reporting structure at Essheo, treating search everywhere as a connected system rather than one dashboard measuring one channel.
What Practical Steps Should a Marketing Manager Take Now?
Bringing all of this together, I’d set out a genuinely practical, ordered set of priorities rather than an overwhelming list of everything at once.
Where Should You Start This Quarter?
Audit how your brand is currently represented across ChatGPT, Perplexity, Gemini, Claude and Google’s AI Overviews for your most important category queries, and be honest about what you find, since this baseline is what makes every following decision evidence-based rather than guesswork.
Alongside that, review whether your product and service content is actually structured for machine parsing, with clean entity data, proper schema markup, FAQ content, and an llms.txt file, rather than relying purely on persuasive marketing copy a human might respond to but a model can’t easily extract.
I’d also recommend investing in third-party presence deliberately rather than hoping it accumulates naturally, since earned mentions on platforms like YouTube, LinkedIn and relevant industry publications consistently carry more weight in AI citations than content on your own website alone.
This is exactly the search everywhere approach we take with every client at Essheo, because I’ve seen firsthand how much a strong third-party footprint changes citation outcomes compared with an owned-content-only strategy.
Should You Build This Capability In-House or Bring in Outside Expertise?
Given that skills have roughly an 18 to 24 month half-life in this space right now, continuous upskilling genuinely isn’t optional if you’re building this purely in-house.
I’d be realistic with any marketing manager about what’s genuinely achievable to build internally versus what’s better brought in as dedicated, fractional external capability, particularly while your own team is still developing its foundational understanding.
This is exactly why I structure Essheo’s engagements to work either hands-off, where we run the strategy and report back weekly or monthly, or hands-on, working directly alongside your existing team to build capability as we go.
I don’t think every business needs to choose between doing this entirely in-house or handing it over entirely to an agency. The right answer is usually a genuine partnership, particularly in the first 12 to 18 months while the internal skills gap the research consistently points to gets closed properly.
Ready to Build a Search Strategy That’s Actually Future-Proof?
If you’re a marketing manager who suspects your current strategy isn’t quite ready for where search is heading, I’d say that instinct is almost certainly correct, and you’re in good company. The research is consistent: most teams know change is happening, fewer have a genuinely structured plan to respond to it, and the gap between the two is exactly where competitive advantage currently sits.
At Essheo, every practitioner on our team carries 8 plus years of experience navigating exactly this kind of structural shift in genuinely hard, competitive sectors, and we build strategies across Google, LLMs, YouTube and social platforms because no single channel is a safe bet any more. Our clients have generated over £45 million in combined revenue over the last two years through the systems we’ve designed and implemented, built on real operational experience rather than theory.
We’ll start with an honest baseline audit of exactly where your brand currently stands across AI platforms and traditional search, then build you a tailored roadmap with quick wins in the first few months and genuine long-term authority built over 12 to 18 months. Whether you want us hands-off with regular reporting or working hands-on to build capability within your own team, that choice is entirely yours.
Book a strategy call with me, and let’s make sure your search strategy is genuinely ready for what’s coming next, not just for what’s already here.