"Digital transformation" had a good run. For the better part of a decade, it was the catch-all term for modernising enterprise technology. But here's the thing: the term has outlived its usefulness, and the playbook that goes with it is actively holding organisations back. The world has moved on. It's time the language did too.
The Problem With "Digital"
Digital transformation was about digitising existing processes. Taking paper forms and putting them online. Moving data from spreadsheets to databases. Replacing manual workflows with automated ones.
That work mattered. But it's table stakes now, not strategy.
The organisations still running the digital transformation playbook in 2025 are investing in the wrong paradigm. They're optimising the old process instead of reimagining it.
$1.3T
spent globally on 'digital transformation' in 2024 - with 70% of projects failing to meet objectives
Source: McKinsey, Digital Transformation Survey 2024
Seventy percent failure rate. After a decade of practice. That's not an execution problem. It's a framing problem.
What Changed
Three things happened between 2022 and 2025 that made the "digital" framing obsolete:
1. AI became accessible. Enterprise-grade AI capabilities went from "build a team of PhDs" to "call an API." The barrier to entry collapsed.
2. The value shifted. Digitising a process saves time. AI-augmenting a process creates new capabilities that didn't exist before. The value ceiling is fundamentally different.
3. The competitive dynamics changed. When everyone has digital processes, digital isn't a differentiator. When a few organisations have AI-augmented processes, the gap becomes a chasm.
AI Transformation Is Not Digital Transformation Plus AI
The instinct is to treat AI as another technology layer added to the digital stack. "We've done cloud, we've done mobile, now we do AI." This is wrong in a specific and important way.
Digital transformation is additive: you add technology to existing processes.
AI transformation is multiplicative: AI changes what's possible. It doesn't just make the process faster; it makes entirely new processes viable.
Digital transformation asks: how do we do what we do, but digitally? AI transformation asks: what could we do that we've never been able to do before? That's the shift. Let the technology do the heavy lifting, and empower your people to focus on the uniquely human work.
Tim Hatherley-Greene
Chief Operating Officer
The Enterprise Examples
Digital transformation of claims processing: Paper forms → online forms → automated data entry → faster processing.
AI transformation of claims processing: AI reads the claim, cross-references policy terms, identifies anomalies, drafts the assessment, and flags the 15% of cases that need human review. The other 85% are processed in minutes, not days.
The digital version made the existing process faster. The AI version made a new process possible.
Digital transformation of procurement: Spreadsheets → e-procurement platform → supplier portal → automated POs.
AI transformation of procurement: AI analyses spending patterns, predicts demand, identifies consolidation opportunities, monitors supplier risk in real time, and negotiates framework terms based on market data. Procurement becomes proactive, not reactive.
What AI Transformation Requires
The playbook is different. Five shifts:
1. From Process Optimisation to Process Reimagination
Stop asking "how do we make this faster?" Start asking "should this process exist in this form at all?"
2. From Technology Projects to Capability Building
Digital transformation is delivered through projects. AI transformation is delivered through building organisational capabilities (shared infrastructure, internal skills, governance frameworks) that enable continuous AI adoption.
3. From Vendor Selection to Partnership Design
The digital playbook buys products. The AI playbook builds partnerships, because AI capabilities are configured and evolved, not installed and maintained.
4. From Annual Planning to Continuous Evolution
Digital projects have end dates. AI capabilities evolve continuously: new models, new data, new use cases. The planning framework needs to be continuous, not annual.
5. From IT-Led to Business-Led
Digital transformation was typically IT-led because the value was in technology modernisation. AI transformation must be business-led because the value is in reimagining business outcomes.
The Litmus Test
If your "AI transformation" roadmap looks like your digital transformation roadmap with "AI" substituted for "digital," you're running the wrong playbook.
The Compound Difference
The most important difference: AI transformation compounds.
A digital process improvement is linear. You invest once, you get the improvement, done.
An AI capability compounds. The first use case builds foundation infrastructure. The second use case is faster and cheaper. The third is faster still. The data generated by the first use case improves the second. The patterns learned by the second inform the third.
After 12-18 months, an organisation with an AI foundation is operating at a fundamentally different speed than one running isolated digital projects.
The "digital transformation" label isn't just outdated. It's actively misleading. It frames AI as an incremental improvement to digital processes, when it's actually a step change in what organisations can do.
Organisations that recognise this shift and update their playbook will compound their advantage while their competitors are still digitising. The gap only gets wider from here.
- Does this mean digital transformation was a waste?
- No. Digital transformation was necessary. You need digital infrastructure (cloud, APIs, data systems) as a foundation for AI transformation. But the organisations that stopped at "digital" and didn't shift to AI are now facing a compounding gap against those that did.
- Can you do AI transformation without completing digital transformation?
- Partially. You need basic digital infrastructure: cloud hosting, APIs, clean data. But you don't need to complete every digital initiative before starting AI work. The best approach is to run them in parallel, with AI transformation informing which digital modernisation to prioritise.