As 2026 begins, B2B content marketing sits at an inflection point. AI-driven search and discovery are undeniably changing how audiences find, evaluate and engage with information. Yet while the pace of change is clear, the practical implications are far less so. For many content teams, the challenge is no longer keeping up with new tools but understanding which shifts genuinely matter. And which are simply noise.
I recently attended Spotlight marketing conference by SEMrush, where AI search was the thread connecting every presentation, roundtable and networking conversation. In parallel, our team at BDB Global hosted a webinar exploring AI discoverability in depth (which you can watch on-demand here), examining how these shifts are beginning to play out in practice. In the months since, through close collaboration with the BDB Global team and our clients, it has become clear that several misconceptions are circulating about the new search landscape.
Moving into a new year, it’s worth pausing to bring clarity to the conversation and unpack the most persistent myths shaping current thinking. In this first of two parts, we’ll examine some foundational myths about strategy and optimisation and examine why the reality is more nuanced than it seems.
Myth 1: Traditional SEO is dead
The myth: AI-driven search summaries are now appearing everywhere and people are using ChatGPT, Perplexity and other Large Language Models (LLMs) instead of conventional search engines. This has led many B2B marketers to believe traditional SEO is no longer worthwhile and that the future belongs entirely to Generative Engine Optimisation (GEO) — the practice of structuring and signalling content so it can be accurately interpreted, selected and synthesised by AI-driven search and answer engines. The implication, for some, is that GEO demands entirely new tools and a fundamentally different approach.
Why it matters: This myth creates real uncertainty about where to focus your efforts. Should you keep investing in traditional SEO or pivot entirely to GEO?
The reality: AI discoverability should absolutely be part of marketing strategies. But this is built on the foundation of SEO, not separate from it. Many AI models draw heavily on established search engines such as Google and Bing, often sourcing information from the first one or two pages of results. Crucially, the core principles that determine whether content ranks traditionally — authority, quality, credible citations, clear structure — are the same principles that determine whether AI models reference your content at all.
Rather than abandoning SEO, the smart approach is to layer GEO on top of what’s already working. Resetting isn’t the answer — intelligent expansion is.
Myth 2: You can easily optimise for AI prompts
The myth: Within the new search landscape, there is a growing belief that AI-driven discovery can be systematically reverse-engineered. The idea is that, by analysing patterns in audience prompts, marketers can predict how users will phrase questions and then optimise content to match those prompts precisely. This thinking is often reinforced by platforms promoting proprietary methodologies or paid tools, positioned as shortcuts to visibility in AI-powered search. The implication is that success depends less on understanding audiences and more on cracking a technical code — one that can be decoded, packaged and applied at scale.
Why it matters: This creates false confidence and leads to over-investment in tools that promise easy answers that don’t necessarily deliver.
The reality: AI prompts are typically far longer and more descriptive than traditional keyword queries. Insights shared at the Spotlight event suggested they can be around five times longer, revealing much more context about what users want. The opportunity is real but reverse-engineering prompts isn’t as straightforward as some might claim. LLMs differ in how they retrieve, weight and interpret information. And audiences use them in highly varied ways. As a result, there is no single formula that guarantees visibility across AI-driven search environments.
That’s not to say optimisation is impossible. Tools exist to track when LLMs surface or reference your pages (“reasoning crawls”) and analyse how AI engines interpret page structure and meaning. But these signals are only part of the picture. What matters just as much — if not more — is a deep understanding of target audiences. By focusing on the human behind the prompt, for example through direct customer conversations, marketers can identify the actual questions buyers are asking and the intent attached to them. The process requires experimentation and effort but it helps teams to channel time, energy and investment into what genuinely influences decision-making.
Myth 3: There are shortcuts to AI search visibility
The myth: As pressure mounts for marketing teams to demonstrate AI search visibility, some might be tempted to turn to shortcuts. The appeal is understandable. While traditional SEO takes time to build authority, AI-driven search moves much faster and leadership teams want evidence that AI strategies are delivering results now. This urgency has created a market for so-called quick wins: paid media saturation designed to floods LLMs with brand mentions, aggressive keyword tactics intended to trigger AI responses or other approaches that prioritise speed over substance. The promise is simple — instant visibility without long-term investment.
Why it matters: Chasing shortcuts can push B2B marketers to pursue shorter-term visibility at the expense of sustainable credibility, risking brand reputation and long-term trust.
The reality: Today’s LLM ecosystems are still maturing, which makes them more susceptible to volume-led or signal-gaming tactics than they should be. But this window is closing. As models evolve, they increasingly prioritise genuine authority, context and consistent validation over sheer frequency of mentions. At the same time, audiences are experiencing higher levels of ad fatigue than ever before. A brand that appears everywhere through paid channels yet nowhere in trusted settings or organic discourse can read as inauthentic and irrelevant.
The smarter approach is customer-first content, with AI optimisation treated as a secondary consideration rather than a starting point. Focus on creating genuinely valuable content that earns credible citations and meaningful mentions through digital PR and user advocacy. This is how authority is built through quality and trust — signals that strengthen both algorithmic visibility and long-term business success.
Final thoughts: Building on solid ground
These first three myths share a common thread. Each offers an oversimplified answer to a complex challenge — or promises a kind of magic-bullet solution for the new landscape. Whether it’s the urge to rewrite the rules entirely or crack the code on quick wins, all stem from the same anxiety: the sense that everything has changed and what worked before no longer matters.
The reality is more nuanced — and more encouraging. AI search hasn’t made traditional marketing fundamentals obsolete. In fact, it has made them more critical. The brands succeeding today aren’t starting from scratch or trying to game the system. They’re building on solid foundations — genuine authority, quality, credibility, audience understanding — and then layering new capabilities on top with intent and discipline. Get these fundamentals right and the rest becomes manageable and meaningful.
But strategy is only half the story. In Part 2, we’ll tackle the final two myths, examining how success is measured in this new landscape and why the human element has become more critical than ever. Because ultimately, success in AI-driven search comes from understanding people, not just the technology that connects them.
