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In 2026, the enterprise landscape is shifting. AI experiments that don’t integrate with core business capability will increasingly be decommissioned or sidelined.
In some cases we've seen pilots that attempt to bolt a modern facade over an organisation's crumbling challenges (much like the team on the left). They ignore the crucial, messy work of building a solid structural foundation, as seen on the right, which is required for lasting integration.
That said, over the last year or two, even more for some, many of our clients proved they could build AI pilots. We more recently hear that they’re past the “can we build something?” phase, and are now confronting the more uncomfortable question:
“Can we integrate what we’ve built into how our business actually operates?”
That’s not a technology problem alone.
It’s an architectural one.
Its a business one...
This is why your AI strategy needs a Business Capability Model (BCM)!
Standalone tools, disconnected agents and tactical automation won’t deliver measurable value without a cohesive architectural foundation.
What we’re experiencing is an Integration Chasm: the gap between isolated AI initiatives and strategic business impact.
Organisations that outperform will be those that treat AI as part of the enterprise fabric, not as an add-on. They will do this by anchoring AI to business capabilities: the stable, outcome-oriented building blocks of the enterprise that define what the organisation must be able to do to execute strategy, rather than how tasks are mechanically performed. This is where the BCM excels at supporting and delivering value mapping alongs with focus.
The Fragile World of AI Pilots
Recent industry analysis makes a consistent point:
- Pilots are multiplying, but enterprise control and value realisation lag. Simply, pilot proliferation outpacing control and maturity. When looking at technology trends that define enterprise AI performance, research from ADAPT explicitly states that "AI pilots multiplied in 2025, but enterprise control lagged." It goes on to note that many organisations can build AI capabilities, but "far fewer can run them with consistent governance, trusted data, and architecture that supports scale." https://adapt.com.au/resources/articles/data-strategy/5-tech-trends-defining-enterprise-ai-performance-in-2026/
- Organisations are building AI tools and agents, but many are failing to tie these to measurable business outcomes. According to a recent Forrester prediction, Enterprises are deferring approximately 25% of planned AI investment into 2027 because expected business value isn't materialising. https://www.forbes.com/sites/forrester/2025/10/17/the-next-phase-of-ai-moving-beyond-hype-to-meaningful-application/
2026 is the year of value execution.
Leaders, analysts at MIT, McKinsey, Gartner and others emphasise that AI must move from interesting prototypes to production-grade systems that deliver measurable outcomes.
Pilots that are isolated must be rationalised.
This movement isn’t about removing innovation; it’s about disciplined execution.
i.e. AI without architectural discipline will be perceived by the business as “fancy automation” rather than strategic capability.
Enterprise Architecture for an Agentic World
Traditional enterprise architecture (EA) practices focus on mapping systems, data and infrastructure. In 2026, the focus must broaden to include Collaborative Intelligence, that is, how people, systems, and AI agents work together toward strategic outcomes. That is at the core of how Fragile to Agile approaches Business Capability Mapping.
One practical evolution is the use of Agent Catalogs within EA tooling. These registries record which AI agents exist, what business capability they touch, what data they use, and what permissions and logic they embody. This prevents redundancy, clarifies ownership and ensures that every agent contributes to a known enterprise capability.
This practice reflects a broader shift:
- From technology-driven projects
- To capability-driven execution.
There’s growing empirical evidence that enterprise value is defined not by what tools are deployed, but by how tightly they align with strategy, governance and measurable business outcomes.
The Business Capability Model as the Source of Truth
At the core of this architectural shift sits the Business Capability Model (BCM), a neutral, stable representation of what the organisation does regardless of process, people or technology. A capability is an outcome-oriented expression of business function: it’s what must be delivered, not how it happens.
In practice, a business capability model serves as the source of truth for AI deployment because it:
- Aligns AI investments to strategic priorities: By linking AI initiatives directly to capabilities, leaders can prioritise where AI agents should focus based on value, risk, and business urgency.
- Clarifies governance and risk control: Capability boundaries define where agents may operate and where guardrails are mandatory. This materially strengthens enterprise risk posture.
- Reduces redundancy and fragmentation: When multiple teams build similar agents, a capability registry reveals duplication and enables reuse or consolidation.
- Supports cross-domain collaboration: A capability-centric view allows business and technology leaders to speak the same language about outcomes, not tools.
Think of a Business Capability Model as the connective tissue between tools and outcomes. Without it, individual AI agents remain tactical: potentially valuable in isolation but strategically disconnected.
The Agentic Operating Model
Architecting AI for strategic value means thinking in terms of an Agentic Operating Model, not just systems and workflows. This model reframes architecture in the enterprise as follows:
Business capabilities drive where AI operates
Every agent must be mapped to one or more capabilities; these mappings inform permissions, data access, governance and performance KPIs.
Agents collaborate to deliver composite outcomes
Task-specific agents should not exist in silos. They need defined protocols for interoperability, access to shared data services, and role definitions within enterprise processes.
Governance is operational, not theoretical
Instead of policy-only governance, enterprises need runtime controls that enforce compliance, ethical constraints, auditability and risk mitigation across agents.
Consider the alternative: a piecemeal portfolio of AI tools, each performing isolated tasks without clear line of sight to business impact. That is what happens when AI strategy is reduced to automation projects rather than capability investments.
From Fragmentation to Integrated Value
Leaders are beginning to recognise 2026 as a pivot point. Analyst reports highlight that while AI experimentations were necessary to build familiarity, they are insufficient to sustain strategic value without integration into enterprise systems of record, workflows and decision loops.
Investments that modernise data pipelines, governance frameworks and capability maps are not overhead. They are value enablers: the foundation on which AI delivers measurable impact.
Integration is no longer a silent infrastructure issue; it is enterprise strategy.
What This Means for you
Realigning AI around business capabilities will require a shift in how leaders think about:
- Roadmaps: from technology feature lists to capability-driven milestones.
- Governance: from policy statements to autonomous controls.
- Ownership: from functional siloes to shared accountability mapped to capabilities.
- Investment discipline: from tools first to outcomes first.
When grounded in business capabilities, AI becomes strategic, measurable and sustainable.
Ultimately, your AI strategy must be anchored in business capabilities if it is to scale beyond pilots into enterprise impact. A capability-centred architecture ensures AI agents are authorised, governed, and measured against real business outcomes, moving the organisation from experiment to execution.
As we said at the beginning:
AI without architecture is just expensive automation.
