AI in 2026: The Year Boring Wins (And Why That’s a Good Thing)

Google's Quiet Infrastructure Win

The launch of Gemini 3 recently was a helpful reality check and possibly a new benchmark for AI deployment - while the performance and benchmarks are impressive, in a similar vein to Nano Banana Pro and Veo 3.1, arguably the most impressive feat Google pulls off is the seamless deployment at scale of these capabilities. Their ability to upgrade their entire infrastructure overnight, for consumers all the way through to developers, is one of the clearest signals that should finally put a nail in the coffin of the long-outdated notion that Google is ‘behind’ OpenAI in some way.

Since the days of ‘Bard’, we’ve been watching brand-recognition continue to favour OpenAI and ChatGPT as the names that brought LLMs to the masses. Within the industry, those actually using the tools professionally have seen the enormous momentum that Google has built, and while the inertia may have meant it took the data titan a while to get off the starting block, it’s been clear that now it’s moving; there are very few companies that can match its dominance in its speciality sectors. The skill-base and expertise in AI at Google has been developing since long before ChatGPT became a household name (Google Brain was a neural net released all the way back in 2011).

This infrastructure and consistency are likely going to play into the wider narrative of 2026 - the year that boring wins.
 

Distribution beats Innovation

The AI Gold Rush has been a repeating story of exciting startups, wildly successful valuations and investment funding. That disparate tech stack and incompatibility starts to show it’s weakness when it comes up against something like the ecosystem that Google offers. In the same way that Apple users tend to stick with the brand once they’re bought in - the convenience, compatibility and the “it just works” approach has real tangible benefits in a fast-paced professional environment.

It really highlights how important it can be to look at the wider scope of a company rather than just its tools in isolation - for example, ChatGPT might be capable of doing everything that Gemini can achieve given the same information, but when Gemini can be integrated into tools like Google Docs, BigQuery, Sheets and more - that native experience makes it far easier to use on a day-to-day basis.

A key prediction for next year is that this “platform effect” will force competitors like OpenAI and Anthropic to strike more and better integration deals with existing tools. Their challenge will be finding companies big enough to offer the market saturation they need - that don’t already have their own AI tools in development or deployment.

The Hype Hangover will Hit

As we’ve mentioned, the story of AI in recent years has been one of unprecedented hype. Whether or not you believe there is an AI bubble, it’s not controversial to state just how enormous the pressure and mouth-frothing interest in deploying AI has been.

In 2026, it’s likely we’ll see a bit of a correction. Many companies have tried to use generative AI to plaster over bad processes, to fake practical tools and to offer things that in practice are far less glossy. The key to successful tech deployment in any industry comes down to making sure it is effective and robust, both of which are incredibly difficult to manage with a technology that is at its core, so random and unpredictable.

The competitive edge is likely to shift to those businesses, platforms and processes that can offer the kind of repeatable, reliable results that most industries tend to prefer. That means that investment in the ‘icing on the cake’ that generative AI offers isn’t going to be as successful, and it may actually fall to more traditional data and automation to fill the gap.

It’s not hard to see why the term “AI” has come to almost exclusively refer to LLMs in popular usage, but they are actually only the latest and most ‘visible’ form of AI that’s available to businesses these days. Machine Learning, Neural Networks and other forms of artificial intelligence are certainly benefiting from the wider interest. However, they are still underpinned by a good foundation of data warehousing and analytics. Automation is another key player in this shift, where general tools like LLMs might fail, it’s likely there are more ‘boring’ solutions that take more effort to deploy - but are far more likely to be successful in the long run.

2026 is almost certainly going to be a year of reckoning for businesses that have over-invested in AI hype, but it remains to be seen whether they will take stock and shift some of that investment into the core practical solutions available elsewhere, or whether they will turn to any number of shiny new AI technologies to solve them.
 

Advertising, uh Finds a Way

You’d be forgiven for thinking that AI-first companies are making an absolute killing in the current climate - but in actual fact most are operating at huge losses. This comes down to the fact that the fundamental value model for something like ChatGPT relies on adoption and scale, with a “we’ll figure it out later” approach to monetisation. With the potential promise of AGI, investors treat the lack of profit as a given, and holding stock in their respective companies almost like a lottery ticket.

The same story held true for Google all those years ago when it came to building a search engine - and while we may all yearn for the days of 10 blue links, the rise of search engine advertising became the foundation of Google’s profitable Ad business. When it comes to their approach to monetisation, then, it should be no surprise that the same holds true for AI Mode.

We’ve started to see the first Ads being shown inside AI mode in recent weeks, and it’s only going to grow from here. The walled garden of Google Search, combined with their dominance in brand recognition, means it’s highly likely to be the largest AI-powered search experience available for a long time to come, and certainly the most available and natural for most users, given their long experience with Google Search.

It has interesting implications for trends we saw start over the last year or so, though, with AI visibility and prompt tracking tools seeing huge growth. Businesses wanting to understand how AI “sees” their brand and understands their content could use these platforms to see how they ‘rank’ or at least how they show up in response to different questions given to AI tools.

Doing this tracking at scale comes at a cost - and for many businesses, getting effective tracking coverage for the multi-platform, multi-prompt and multi-intent landscape their users exist in becomes prohibitively expensive. Not only that, but being able to see how you appear doesn’t often mean that you can meaningfully or measurably impact those results.

That challenge brings us back to advertising - in the same way that Paid Media emerged as a response and an easier alternative to SEO in the Google Search days, it’s likely that a chunk of the market are more than happy to pay to appear in AI results as long as they can get clear, simple ROI metrics and tracking off the back of it. Especially if tracking results and organic efforts can be so expensive, why not simply spend that money on advertising within the platform itself?

It might not be as exciting as trying to game the algorithm, reliving the early days of SEO with keywords in white text on white backgrounds and so on, but in a practical and fast-paced world, we can see 2026 being the year that measurability and clear impact wins out over the organic approach.

Key Takeaways

• Boring Wins Matter: 2026 is shaping up to be the year that practical, reliable AI deployment beats flashy innovation. Companies that focus on consistency, integration, and repeatable results will hold the advantage.

• Distribution > Innovation: Platforms with wide-reaching ecosystems, like Google, are likely to dominate. Seamless integration into existing tools can outweigh individual AI capabilities.

• Hype Hangover Incoming: The AI hype of the past few years is set for a correction. Businesses that over-invested in flashy, unproven AI will need to recalibrate toward more robust, practical solutions.

• Automation and Data Still Core: While LLMs grab headlines, foundational AI, including machine learning, automation, and analytics, remains critical for delivering reliable, measurable outcomes.

• Monetisation and Measurability Win: AI-powered advertising and paid placements will grow as businesses prioritise measurable ROI over attempting to manipulate organic AI outputs.

• Platform Effect Drives Strategy: Competitors like OpenAI and Anthropic will need to pursue strategic partnerships and integrations to match the reach of ecosystem-driven platforms.

If you’d like to explore how these trends could impact your business, or find out how to put practical, reliable AI to work for you in 2026, get in touch, we’d love to help.