Today is the last day of the financial year, and a traditionally day of stock taking for many businesses. So what do we make of AI Adoption so far?
90% of enterprises have adopted AI. Enterprise AI spend has
grown from $1.7B to $37B in two years. Worker access to AI rose 50% last year.
Productivity gains are real and measurable. All of that is quite impressive.
Two-thirds of organizations are still stuck in pilot
purgatory, unable to scale what they started. Only 8.6% have AI agents running
in production (Recon 2026). And just 34% of leaders say they're truly
reimagining how their business works (Deloitte, 2026).
What we know: The technology is rarely the problem.
The blockers are almost always human: underdeveloped decision architecture,
governance delegated to tech teams, and leaders who champion AI in town halls
but don't model it in their own workflows.
So how do we drive depth and business outcomes with AI?
From having worked with a few dozen clients on their AI
strategies and plans, and seeing what is being implemented and scaled, here
are 5 key steps that I believe are key to driving real outcomes.
(1) Find data rich environments: not every area or
problem has the underlying data ready. But some do. For example, your
technology stack, or your HR or Finance functions are usually flush with
ready-to-use data, that is structured, and ready to process. Most processes in
these environments are also semi-automated, so there is a level of process
maturity. Conversely you might think about CRM or loyalty data, but often these
are managed through 3rd parties, or held in multiple systems, and not easily
converted into AI ready data. A high percentage of the AI initiatives we see
moving forward to value are in these 3 areas.
(2) Find areas with effort headroom: AI adoption may
be curtailed by the lack of participation of colleagues who fear for their
jobs. Picking areas where even with automation, there is a significant backlog
of work, means that AI will simply enable more work to get done rather than
create redundancies. One of our AI programs has recently saved a government
department thousands of hours, but this is just enabling them to catch up with
the significant backlog of work, so there is no question of job loss.
(3) Solve a specific problem or address a specific
opportunity: a lot of AI projects start with applying AI to a broad area,
but the specific problem is not well defined, and nor is the specific outcome
being sought. One of our most viable projects involved bringing down mean time
to respond for critical incidents by a very clear target. You could look to
speed up your recruitment or onboarding, or enable more straight through
processing in your finance function. Whatever it is, it should be (a)
measurable and (b) have a clear line to your topline/ bottom line goals i.e. why
it matters. And you need to ensure that the focus always stays on delivering
the outcome. It’s likely to require more than just the AI component.
(4) Deliver to sharp 3-month timelines for velocity:
any project that takes more than 3 months in today’s AI environment risks
obsolescence. No matter how complex the problem or how significant your goal,
it’s critical that delivery is chunked into 3 month deliverables, with each
period delivering tangible value by way of features, benefits, and outcomes.
And here’s a tip: the actual AI component of this may take only a month or less
of that time. The rest is systems access, testing, implementation, and change
management.
(5) Work to a roadmap that is revisited regularly: your
3 month deliverable needs to be backed by a product roadmap. Every development
needs to be seen as a product - which means accepting that the solution will
constantly evolve and add new features and ideas, and respond to the needs and
available AI capabilities. The project mindset is a dangerous one in this
world. It’s too rigid, too complex, and far too dependent on predictability. At
any point, you may have a roadmap that is sharply defined for the next quarter,
and increasingly indicative for future periods.
Ensuring AI adoption across the organization is a good foundation but delivering outcomes is where value lies. Over the next 12 months, the differentiator for enterprise AI will be the ability to move quickly and deliver specific value. And to make this a repeatable and institutionalized approach.