2026 Trends: AI pilots start with people

AI rollouts must confront the fear factor

In our CTI 2026 trends report, we talk a lot about the role of humans alongside AI.

And in many of the chapters, we somewhat dance around the topic of company culture, particularly when we broach the AI skills gap in the mid-market and the need for strategic planning.

So, let’s get into it and tackle culture head on. It’s the one factor that can single-handedly put the kibosh on an AI rollout, just as it did with many digital transformation projects of years gone by.

Top-down meets bottom-up

Cultural issues within any organisation's approach to AI can be viewed top down, bottom up, or a mix of both. They include:

• anxiety about the impact of AI on job security;
• lack of trust (Does the tool make errors and will I be blamed? Is it fair and does it respect privacy?);
• lack of training;
• and poor communication from the C-suite.

These issues can all contribute to the presence of ‘detractors’, inadequate governance and a strategic vacuum, ultimately leading to poor AI implementation.

Detractors are easy to understand with any tech that brings new workflows, especially when spot tools are introduced first and not integrated with existing systems.

Users need clear direction, examples of what ‘good’ looks like, and engaging training. What they don’t need is a lack of clarity from leadership, who may not be entirely sure of the best use cases.

Gauging AI readiness

Leaders must acknowledge the AI fear factor from day one. Overcoming it requires education. “Training teams together, starting with fun examples and exercises designed to acclimatise people to AI (think prompt engineering applied to your hobbies, for example), can then lead on to identifying opportunities in the workplace,” writes CTI Digital CEO Chris Burgess.

This gauging of AI readiness is an important first step before teams can work together to identify backend tasks that are good candidates for augmentation. “This might begin with prompting for solutions using a RACE framework (role, action, context, expectation),” says Burgess, “before eventually moving on to other tools such as AI-powered workflow automation or web and app builders (e.g. n8n, Lovable, Replit).”

A human-centric approach

It’s this collaboration in the education/experimentation phase that helps to bring the workforce along with leadership, and can be led by ‘tiger teams’ of early adopters from across the business.

McKinsey highlights the value of incorporating diverse perspectives early and often in what it calls a human-centric approach. However, the consultancy found that less than half (48%) of C-suite employees say they would involve non-technical employees in the first stages of AI development. Yet it's precisely these non-techie end users who can add valuable perspective and endorsement early doors.

Governance should also be accessible to all internal audiences, not written in legalese, and will evolve over time in order to encourage the right behaviours in the business.

Given that many use cases of generative AI have recently travelled through Gartner's 'peak of inflated expectations', there's no better time for businesses to ensure their staff are on board with new initiatives and have their own reasonable expectations. The research firm has variously predicted that both 40% of agentic AI projects will be cancelled by end of 2027 and that (perhaps related) 40% of enterprise apps will feature task-specific AI agents this year.

Whether AI functionality is built-in to SaaS software or created in-house, successful uptake is a top-down and bottom-up affair.

This is an excerpt from CTI Digital's report, 2026 Trends: Marketing in the Age of AI. If you'd like to chat about AI and innovation with our expert team, get in touch.