Are companies really using AI to implement strategy?

Very relevant question, Jari.

From what I am seeing, most organizations are still using AI around strategy rather than within the actual system that executes strategy.

AI is increasingly being applied to analysis, reporting, forecasting, knowledge retrieval and operational optimization.
These use cases create value, but they are not the same as embedding AI into the mechanisms that translate strategic intent into coordinated action.

Real strategic execution involves prioritization, resource allocation, decision-making, governance, risk sensing, performance monitoring, learning and adaptation.
These are the capabilities that determine whether strategy becomes reality.

The challenge is that AI adoption is often progressing faster than organizational integration.
Many organizations have AI tools, but far fewer have integrated them into workflows, decision processes, governance structures and learning systems.

As a result, AI frequently improves individual productivity without fundamentally improving organizational execution.

The real question is not whether organizations are using AI.

The real question is whether AI has become part of the organization's operating system for execution, decision-making, governance and learning.

What activities do you include in strategy execution, implementation, and governance, and what are the use cases where you think AI should be included?

I see AI used in researching strategy. It's built into many of the tools and workflows we use for monitoring and execution. It analyzes data, informs decisions (at times, with appropriate validation, of course), and is often used to help create various forms of documentation and reports. These are all activities that can be part of strategy execution, implementation, and governance.

If I've learned anything from past transformations, it's that not every company needs to transform; some just need to adapt, and they don't all need to adapt or transform in the same ways. Let's look at a few ways.

1. Use AI to improve existing work
2. Use AI to redesign the work
3. Re-evaluate whether the work should exist
4. Increase capability/capacity without changing the process

All four approaches should be considered, and some companies may progress through them, for example, starting with 4, then 1, then 2 over time. It wouldn't surprise me if a lot of companies were still figuring out where and how to best use AI for strategy execution, implementation, and governance, and where it's worth the expense.

Sigo viendo el mismo patrón: la IA está omnipresente en capas de soporte, pero casi ausente en capas de ejecución estratégica.

Hoy la mayoría de las implementaciones se concentran en:

• analítica descriptiva/predictiva
• optimización operativa puntual
• automatización de tareas aisladas

Pero muy poco en los componentes duros de la estrategia: orquestación, priorización dinámica, asignación de recursos, gobernanza y control de ejecución.

Desde una perspectiva técnica, lo que observo es que la IA sigue funcionando como módulos periféricos, no como parte del circuito de decisión:

✔No está integrada a los OKRs/KPIs en tiempo real
✔No alimenta modelos de portafolio adaptativo
✔No participa en sistemas de planificación continua (CPM/PPM)
✔No opera sobre constraints reales (capacidad, riesgo, compliance, cashflow)
✔No cierra el loop entre insight → decisión → acción → retroalimentación

Lo que casi no veo aún:

Agentes autónomos que ajusten planes en función de desvíos
Sistemas de gobernanza algorítmica que monitoreen riesgos y cumplimiento
Decision engines que recomienden trade-offs entre iniciativas
Modelos de simulación estratégica integrados al ciclo operativo
IA embebida en PMOs, EPMOs o Strategy Offices

I suspect the question isn't whether AI is being used for strategy execution. The question is whether AI is helping organizations maintain a shared interpretation of strategy.

Most organizations already have AI generating reports, summaries, forecasts, and analyses. What they struggle with is ensuring hundreds of decisions across teams remain aligned with strategic intent as conditions change.

The most interesting future use cases may be less about automation and more about sense-making: identifying priority conflicts, detecting interpretative drift, surfacing resource misalignment, and helping leaders understand when the organization's actions are no longer consistent with its stated strategy.

In that sense, AI may ultimately become more valuable as a coordination and learning capability than as a productivity tool.