Here's the thing: 2027 is worth the wait.

Our earlier editions of market gap content mostly pointed at big, growing markets: AI, e-learning, renewables, payments. Useful, but broad. This time we're doing it differently. Every gap in this edition is tied to something dated and specific: a legal deadline, a forecast with a horizon, or a measurable change in how buyers behave.

And most of them land on the same group of companies. Too big to ignore the change, too small to have a team for it.

Eurostat's 2025 figures show the split clearly. 55% of large EU enterprises use AI. Among medium-sized firms, it's 30.4%. Among small firms, 17%. Large companies have compliance departments, platform teams and vendor relationships. Firms with 10 to 250 people mostly don't.

That missing middle is where the opportunities sit.

Part 1 covers the four AI-driven gaps. Let's break them down.

1. AI agents that actually work: governance for the mid-market

Plenty of companies have piloted AI agents. Far fewer can monitor them, control costs, decide who's accountable when one gets it wrong, or redesign work around them.

Enterprises can buy "agent control towers." Other companies like small agencies and IT providers that serve them are mostly improvising.

This picks up where our 2025 gaps on friendlier process automation for SMEs left off. The tools got smarter. The management problem got bigger.

Why now? Gartner has put a date on the shake-out. In June 2025, it predicted that over 40% of agentic AI projects will be cancelled by the end of 2027, citing rising costs, unclear business value and weak risk controls. In the same release, it estimated that only about 130 of the thousands of vendors selling "agentic" products are the real thing. Gartner calls the rest "agent washing."

So 2027 is the year a lot of agents get switched off. The question is which ones.

What's the potential? The governance gap is wide. In April 2026, Gartner reported that only 13% of organisations believe they have the right governance for AI agents. The same release projects the average Fortune 500 company will run more than 150,000 agents by 2028, up from fewer than 15 in 2025.

Those are enterprise numbers. But agent sprawl doesn't stay at the top. If the biggest companies can't keep track of their agents, a 300-person firm running agents across n8n, Make, Zapier and Copilot Studio has no chance without help.

What's the key to success? Process, not just software. Microsoft, Salesforce and ServiceNow are already bundling governance features into their platforms. A standalone dashboard can get squeezed. A service that knows how to redesign the workflow around the agent, and can prove what each one is worth, is much harder to replace.

What you could build:

• An agent cost and reliability for SMB automation stacks, tracking cost per task, error rate and how often a human has to step in.
• A two-week "agent audit" that decides, agent by agent, whether to kill, fix or scale it, with the ROI evidence to back each call.
• A human-in-the-loop approval layer for agents that touch customers or money: review queues, escalation rules, clear handoffs.

Risk check: If the platform vendors make governance good enough and free, pure software plays get squeezed. Lead with the service.

2. Being the answer: AI search visibility for SMEs and B2B service firms

This one is new to the series. We've never written about how companies get found. In 2027, that's turning into a market of its own.

Buyers now research inside AI assistants. Enterprise brands have GEO tools and agencies tracking how ChatGPT, Gemini and Perplexity describe them. Local service businesses, B2B consultancies, agencies and niche manufacturers have almost nothing affordable.

Why now? Two numbers tell the story.

First, clicks are thinning out. Pew Research found in July 2025 that when a Google AI summary appears, users click a traditional result on 8% of visits. Without the summary, it's 15%.

Second, AI is now part of almost every B2B purchase. Forrester's Buyers' Journey Survey of roughly 18,000 buyers, published in January 2026, found that 94% used AI or generative AI somewhere in their buying process.

What's the potential? The shift is sharpest in some categories. In a G2 survey fielded in March 2026 (n=1,076), 51% of B2B software buyers said they start research with an AI chatbot more often than with Google. G2 is a vendor with a stake in this, and the sample is software buyers only. Still, it shows where things are heading.

To be clear: none of this means SEO is dead. Gartner's famous prediction that search volume would drop 25% by 2026 hasn't shown up in raw query numbers. We've argued before that AEO runs on SEO fundamentals. The gap isn't "replace SEO." It's "measure and improve what AI says about you," which most small firms can't do today.

What's the key to success? Honest measurement. AI answers change from one run to the next, so any tool that promises a single precise "AI ranking" is overselling. Winners will track trends across many prompts and pair the data with the work that actually moves it: earned media, reviews and content worth citing.

What you could build:

• A low-cost AI visibility tracker for SMEs: weekly prompt panels by location or niche, showing your share of answers against named competitors.
• A "citation-worthy" content and PR service focused on the third-party sources AI engines tend to cite.
• Vertical directories built for answer engines, such as verified B2B suppliers in the Western Balkans, structured so AI systems can read and trust them.

Risk check: The GEO tool space is already crowded, and the measurement is noisy. Services with real content depth will outlast another dashboard.

3. Machine customers: getting small shops ready for AI shopping agents

Back in 2023, we flagged creative e-commerce and m-commerce for SMEs as a gap. The 2027 version has a twist: the customer might not be a person.

AI assistants increasingly research, compare and, through protocols like OpenAI and Stripe's Agentic Commerce Protocol and Google's AP2, complete purchases. Most small merchants' catalogues, policies and checkouts weren't built for a machine to read, let alone buy from.

Why now? AI-referred shopping went from curiosity to measurable channel in a single season. Adobe reported that generative-AI referral traffic to US retail sites grew 693% year on year in the 2025 holiday season, and revenue per AI-referred visit rose 254% (Digital Commerce 360 summary, January 2026).

It's a small base. But it's real traffic with real revenue attached.

What's the potential? Large, and hard to pin down. McKinsey's estimates agentic commerce could orchestrate up to $1 trillion of US B2C retail revenue and $3-5 trillion globally by 2030.

Read that carefully. It covers goods only, and it's a scenario, not a forecast. Narrower definitions, such as sales completed entirely inside AI platforms, produce far smaller numbers. So treat it as a range, not a promise.

What's the key to success? Treat it as a CX problem, not a plugin. An agent "experiences" your shop through structured data: product attributes, compatibility, delivery times, return rules, reviews. Design for that reader as carefully as for a human one.

What you could build:

• A catalogue optimisation tool for agents that enriches product feeds and tests how ChatGPT, Gemini and Perplexity describe and rank each product.
• An agentic checkout integration service for Shopify and WooCommerce merchants, including fraud rules for orders placed by agents.
• A "machine customer" CX audit that reworks returns, pricing transparency and reviews so agents are more likely to recommend you.

Risk check: Protocols are still moving, early conversion data is mixed, and Shopify, OpenAI or Google could make much of this a free built-in feature.

4. AI Act compliance in a box for small firms that use AI

The gap is specific. Small and mid-sized firms that use AI in hiring, credit, customer service or marketing have no affordable way to list their AI systems, classify them, document them and show they're building AI literacy. Enterprise governance tools are priced and designed for enterprises. Non-EU vendors selling AI-enabled software into the EU, including software houses in our own region, face the same problem.

Why now? Because the delay didn't remove the work. It compressed it.

The Digital Omnibus on AI (Regulation (EU) 2026/1744, in force since 27 July 2026) moved the Annex III high-risk obligations from 2 August 2026 to 2 December 2027. Annex III covers uses like employment, creditworthiness, education and access to essential services. Rules for AI inside regulated products moved to August 2028.

Meanwhile, some duties already apply. The Article 50 transparency rules, such as telling people they're talking to an AI and labelling deepfakes, have applied since 2 August 2026. And Article 4 on AI literacy was rewritten from "ensure" to "take measures to support" literacy, but it still applies to every provider and deployer.

What's the potential? Look again at Eurostat's adoption split: 17% of small firms and 30.4% of medium-sized firms use AI. These are the companies now adopting, and the ones with the least compliance capacity. Every one that uses AI in a high-risk area will need documentation by December 2027.

What's the key to success? Automation or sector depth. The market is already full of generic "AI Act checklists." What's missing is software that finds the AI tools a company actually uses (including the SaaS features and browser extensions nobody signed off on) or deep expertise in one high-risk area, like HR tech or lending.

What you could build:

• An AI register and risk triage tool that discovers AI in use, classifies it against Annex III and generates deployer documentation and Article 50 notices.
• AI literacy as a service: short, role-based training with evidence logs, sold per seat to HR.
• A fractional AI governance officer on retainer for firms with 50 to 500 employees.

Risk check: Brussels has already delayed once and could again, and there are no penalty precedents yet, so urgency depends on enforcement signals.

The pattern behind all four

Step back, and the four gaps start to look like one.

AI agents are becoming gatekeepers (deciding what a buyer sees), customers (deciding what gets bought) and coworkers (doing work someone has to answer for). Gaps 1 and 2 are about trusting the agents you run. Gaps 3 and 4 are about being visible and usable to the agents your customers run.

None of these can be solved with a tool alone. Every "what you could build" above works better when someone has done the strategy work first: which agents earn their keep, what your customers actually ask AI, what your shop looks like to a machine. Solutions without strategy are just temporary fixes, and in 2027 a lot of temporary fixes will get cancelled.

One more thing before you build. A watchlist is a starting point, not proof. Run any of these through a proper validation step before you commit budget. 

Want to build on one of these?

If one of these gaps lines up with something your team is already thinking about, we'd like to hear about it.

At Solveo, these four sit close to our daily work. We're happy to think it through with you, from testing whether the gap is real for your market to designing what you'd build first.

So, which of these four is already showing up in your own business?

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