Only 26% of organisations have the capability to move past POC and generate value
BCG (Oct 24)
You bought the AI, the dashboards are live, the Head of Data is in seat. But the momentum’s flat.
Some teams never moved. Some moved and stalled. Some got wins — but not the kind that shift the needle. It’s not a tech issue — it’s a structure problem. Most orgs aren’t built to absorb intelligent systems; they’re designed for control, rigidity, and hierarchy.
AI investments often under-deliver — not because the tech fails, but because the structure underneath wasn’t built to support it. AI investments often under-deliver — not because the tech fails, but because the structure underneath wasn’t built to support it. Most organisations are still configured for control, predictability, and hierarchical decision-making — traits that served them well, but now sit uneasily alongside the demands of intelligent systems: adaptability, cross-functional execution, and outcome-aligned incentives.
Whether progress stalls, misfires, or falls short of expectations, the root issue is usually the same: trying to run next-gen capability on a last-gen chassis.
No platform, partner, or dashboard can overcome a structurally misaligned operating model. Without foundational redesign — across teams, processes, and systems — intelligent systems quietly stall, or worse, create performance theatre that hides failure until it’s systemic.
Most AI deployments don’t stall because the tech underdelivers — they stall because the business wasn’t built to support it.
Operating models are becoming visibly outdated. AI demands adaptability, lateral collaboration, and incentives aligned to outcomes – but most orgs are still hardwired for predictability, vertical control, and fixed responsibilities. New systems are being layered onto structures that were never designed to move with them.
Data remains siloed, and governance is reactive and defensive heavy – MVG. Platforms don’t connect, and even best-in-class tools get stranded without the infrastructure to carry value across the business.
AI initiatives often sit in innovation bubbles — adjacent to core operations, instead of embedded within the mechanics of how the organisation actually runs. That fosters performance theatre, and reinforces ancillary initiative mindsets.
Structural Realignment is the Enabler
People : Loosen Functional Boundaries, Align to Outcomes
You don’t need to dismantle functions — but you do need to loosen their grip. Start by realigning teams around shared outcomes, reducing the friction between insight, decision, and action. Critically, embed AI literacy at every layer. If teams can’t interpret, apply, and communicate with intelligent systems, collaboration becomes chaos.
Process : Shift from SOPs to Adaptive Workflows
Not every process should be fluid, some functions depend on rigour, stability, and auditability. But where work intersects with AI, static SOPs can become blockers. Intelligent systems require dynamic inputs, real-time feedback, and fast-turn iteration. In these zones, replace rigid playbooks with adaptive workflows governed by intent, not sequence. Codify outcomes and constraints and then let the process respond to data, not bureaucracy.
Technology : Architect for Movement, Not Just Stability
Modernisation isn’t enough – integration is what unlocks. Systems must be designed to move information across domains, not just within them. That means APIs over silos, data infrastructure over dashboards, and architectural choices that optimise for interoperability over perfection.
You can’t just bolt intelligence onto a system that’s not designed to act on it.
Don’t audit your AI stack, audit your structure.
