Too many are chasing AI trends without a clear process. Chris Burgess, CEO of CTI Digital, explains why business leaders need to overcome the fear factor and begin by focusing on backend tasks.
Right now, someone in your organisation is pitching an AI project that will ‘transform everything’.
Six months later, it will probably sit gathering dust in a SharePoint folder, one of the 95% of AI innovation initiatives that achieve zero measurable ROI.
This seems like a paradox. Everyone knows AI matters, everyone’s investing, and yet almost everyone’s failing.
Why is it that so few AI pilots are delivering productivity or P&L impact?
The answer is the same reason that so many projects died in previous technological revolutions, from Web 1.0 to mobile. Too many CEOs and CMOs are chasing trends without a clear process.
Leaders are chasing trends without a clear process
It’s easy to see why the C-suite is acting in haste.
It’s not incompetence, it’s collision. Three forces are converging:
- growth targets are rising;
- budgets are shrinking;
- and AI hype is creating urgency to act before you’re ‘left behind’.
This pressure triangle creates an impossible situation – deliver more with less while simultaneously transforming how you work.
It’s no surprise then that teams are defaulting to hope-based planning. They pilot without proving. They scale without measuring. And they treat innovation as a special project rather than organisational capability.
Remedying these mistakes doesn’t mean adding more bureaucracy or creating dashboards that sit ignored. It’s about understanding whether AI is making its way into the daily rhythm of your business.
Businesses must acknowledge the fear factor
Gauging AI readiness is an important first step, before defining where the tech might be able to help. The fear factor around AI naturally creates detractors and overcoming this 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.
‘Anything is possible’ is a daunting prospect
Though AI is a tech full of possibility, starting with a blank page and vaulting ambition isn’t productive.
Instead, every team must ask themselves how they intend to ‘eat the elephant’. The answer, as the old joke goes, is one bite at a time.
In the context of AI, this means focusing on backend tasks – where are people spending their time, and where can they save a few hours? Working together, teams can experiment with AI tools. This might begin with prompting for solutions using a RACE framework (role, action, context, expectation) before eventually moving on to other tools such as AI-powered workflow automation or web and app builders (e.g. N8N, Lovable, Replit).
The important point is to make the possibilities digestible.
Spot, pilot, scale (the ‘5% framework’)
Successfully delivering AI initiatives (becoming part of the 5% that are realising ROI) can be tackled using a 3-stage framework – spot, pilot, scale.
In stage 1, spotting opportunities is about detecting your baseline. What does your current state of productivity look like (time spent on tasks, error rates, customer response times etc.), what are your confidence levels and skills gaps.
Stage 2 is the pilot (with measurement baked in). Track the metrics that matter, including usage rates, workflows in production, number of experiments launched, and the completion of training. These measures will give an understanding of momentum, adoption and lasting value.
Stage 3 is scaling (only when ROI is proven). Businesses must define what ‘proven’ means before they pilot e.g. X hours saved, Y% error reduction, £Z revenue increase.
Understanding metrics before and after a pilot, and the scaling decision point is key.
Remember, if you can’t quantify it, you’re not ready to scale it
Building organisational muscle for continuous innovation
This framework isn’t just about AI – it’s about building organisational muscle for continuous innovation. The companies that are in the 5% are the ones who’ve embedded experimentation and measurement into their DNA.
The competitive advantage goes to those who can learn, measure, and iterate faster than the market moves.
Before launching your next AI initiative, ask yourself: What’s my baseline? How will I measure success? What’s my decision point to scale or kill?
If you can’t answer all three, you’re already in the 95%.
I’ll be exploring this framework in detail at Prolific North Live on 6th November. Join me to see how Northern businesses can turn innovation from expensive theatre into measurable competitive advantage.
