4 strategic drivers for 2026 AI Strategies 👇

Yesterday I was with the CIO and his AI top dogs at a fast-moving Australian organisation, discussing how we could help them run an evidence-backed pilot to bring frontier agentic-coding results into their environment.

Over the last few months, I’ve had the opportunity to read a lot of 2026 AI Strategy decks and roadmaps. The details differ, but the underlying opportunities are surprisingly consistent.

Here’s my abstraction with four strategic drivers every company should have in their AI Strategy next year.

1️⃣ AI Power Users & Agentic Workflows at Scale

This is about turning everyday staff into “AI power users” and giving them agentic tools embedded in their workflows so they can safely delegate meaningful work to AI (drafting, analysis, coordination) rather than just using chatbots on the side.

In 2026, serious strategies will be building AI power user capability and redesigning workflows so people can hand off meaningful tasks to agents under clear guardrails, tracking impact at task level and doubling down where the gains are real.

2️⃣ AI Engineering & Agentic Operating Models

This is about reimagining the entire product development lifecycle for a new reality where organisations suddenly have order-of-magnitude multipliers in productivity (22.5× more net integrated code), speed to value (115× faster), and quality.

Today, many teams treat AI as a bolt-on. In 2026, we can expect leaders to be maturing AI platforms, agent frameworks and LLMOps as shared infrastructure, and shifting from scattered pilots to durable, cross-functional pods with clear ownership of value streams and risk.

3️⃣ GenAI‑Powered Products & Customer Experiences

This is about embedding GenAI directly into your products, services, and customer journeys, turning static, rule-based interactions into adaptive, conversational, and personalised experiences.

In 2026, progressive organisations will expand GenAI features where data shows improvements in customer experience and outcomes, treating this as ongoing design and experimentation rather than a one-off “GenAI release”.

4️⃣ Agentic Automation in Operations & Back Office

This is about using agents to automate end-to-end processes, not just single tasks. Agents can read, reason and act across multiple systems and loop in humans when needed. It goes beyond traditional RPA by handling unstructured data, judgment-heavy steps and cross-system orchestration.

Structural cost reduction, faster cycle times, fewer errors and rework, and more human time freed up for genuinely complex or relationship-driven work.

By 2026, front-runners will be selectively redesigning high-value processes so agents handle more of the routine, end-to-end work, using process and performance data to decide where to deepen automation and where to deliberately keep things human.

️ If your 2026 AI Strategy doesn’t name these four explicitly, you’re probably leaving a lot of upside on the table.