The rush to adopt AI is outpacing understanding
According to Gartner’s 2025 CIO Agenda, 78% of enterprises have deployed at least one generative AI tool in the past year — yet fewer than 15% report any measurable impact on performance or profitability.
The reason is simple: most organisations are implementing generic AI tools, not strategic AI capability.
These tools — ChatGPT, Copilot, Gemini and others — are built for breadth, not depth. They are designed for individuals, not enterprises. Sector specific tools only typically address an isolated part of an organisation. They excel at isolated tasks but fail to integrate with business systems, governance models, or strategic objectives. As a result, what many executives describe as “AI adoption” is in reality a series of unconnected experiments — impressive in demos, invisible in results.
The productivity illusion
In surveys conducted by MIT Sloan Management Review (2024), 63% of business leaders admitted they struggle to quantify AI productivity gains beyond anecdotal evidence.
Generic tools create what we call a “productivity illusion”: visible activity without verifiable performance improvement.
Employees become more efficient at producing content, reports, and analysis — but none of this is captured in structured data, integrated workflows, or measurable ROI.
At scale, this breeds inconsistency, fragmented processes, and shadow technology use that erodes governance.
Three fundamental flaws of generic AI in enterprise use
- Lack of contextual intelligence
Large language models are trained on public data. They lack the semantic understanding of internal processes, systems, and performance drivers that shape competitive advantage. Without access to contextual data, AI can only generalise. - No integration or assurance
Generic AI operates outside enterprise architecture. It cannot enforce policies, track lineage, or align outputs to financial or operational KPIs. This leads to duplicated effort, risk exposure, and compliance blind spots. - Zero differentiation
When every competitor uses the same off-the-shelf tool, the output converges toward mediocrity. True differentiation comes from how AI is trained, integrated, and governed — not which vendor is used.
The governance gap
Deloitte’s 2024 State of AI in the Enterprise report highlights that 62% of organisations lack a formal AI governance framework.
This absence of structure has tangible consequences: data leakage, regulatory risk, and inconsistent decision-making.
Unsupervised use of generic AI also introduces IP vulnerability — sensitive project data or client information may enter the public domain. For industries like financial services, construction, or healthcare, that’s an unacceptable exposure.
From tools to capability
At Surtori, we view AI adoption as a strategic capability build, not a technology rollout.
Through our Fusion Framework™, we help organisations move through a proven sequence:
| Product | Phase | Output |
| (Digital & AI Operating Model Review™) | Discover | Identification of your biggest efficiency wins. Target operating model, digital operating model and tech roadmap established. Internal budget alignment to business case – Tech aligned. |
| Digital investment Alignment Plan | Design | Identifying the highest value change initiatives/projects that deliver best ROI. Detailed project scoping and modelling against budget. |
| Development Engineering and Benefits realisation | Deliver | Develop product, Operationalise, Measure Benefits and provide ROI. |
| Support Maintenance & ongoing innovation | Embed | Ensuring your business stays ahead of the competition. |
Each phase ensures that AI initiatives are grounded in business objectives, supported by secure data architecture, and tied directly to measurable outcomes.
This approach transforms AI from a novelty into an operational engine for performance.
The differentiator: intelligent transformation
The next phase of digital transformation will not be defined by who has the most AI tools, but by who achieves governed, contextualised intelligence at scale.
Executives who treat AI as a strategic capability — integrating it into process design, data models, and decision frameworks — will unlock compounding advantages in efficiency, insight, and resilience.
Those who rely on generic tools will automate noise.
In summary
Generic AI creates activity.
Intelligent AI creates advantage.
Businesses that embed AI through structured frameworks, governed data, and measurable impact will define the next era of transformation.
That’s the difference between adopting AI — and becoming intelligent.
78% of companies now use generative AI — but fewer than 15% see measurable impact. Generic tools create activity, not advantage. Here’s why the next era of transformation demands intelligent, contextualised AI capability.