Decidr

But success in this new era isn’t about hype or experimentation. It’s about maturity.

AI maturity means building the systems, culture and leadership discipline to use AI meaningfully across your business. Not just in labs or side projects, but in decisions, workflows and customer experiences. And the stakes are already high.

According to Accenture, only 12% of companies have achieved advanced AI maturity. These “AI Achievers” are pulling ahead, experiencing 50% faster revenue growth and embedding AI across their entire organisation.

What’s more, companies that mention AI on earnings calls are more likely to see share price jumps. But maturity isn’t about bandying around AI-speak or paying for flashy pilot programs. It’s about weaving AI into every layer of the business as a decision engine, not a bolt-on tool.

AI maturity is the new strategic moat

McKinsey estimates that AI could unlock $2.6 to $4.4 trillion in annual value across sectors from banking to science, retail to manufacturing. But the gap between aspiration and execution is stark.

Many organisations remain trapped in pilot purgatory, launching isolated AI experiments that never scale, rarely integrate and often fail to deliver lasting impact. This isn’t due to lack of interest, but lack of infrastructure, coordination and executive alignment.

Meanwhile, the few who are moving beyond pilots, the AI Achievers, are already capturing outsized value, embedding AI into their strategy, operations and culture.

This is the new strategic moat. It’s not about having more data or better models. It’s about making AI a core competency of your business from end to end.

The shift begins at the top. You don’t need to master the algorithms, but you do need to champion integration, embedding AI into core strategy, not relegating it to side projects. Because at this stage, annual planning without AI is less a process and more a shot in the dark.

Lay the groundwork before you automate

It’s easy to get caught up in the excitement of deploying AI, but without a solid data and cloud infrastructure, even the most sophisticated models will fail to take root. A strong foundation doesn’t just support AI, it simplifies implementation, accelerates outcomes and prevents very expensive rework down the line.

The AWS Cloud Adoption Framework underscores the criticality of robust data infrastructure.

Without the right data architecture and scalable cloud systems, AI initiatives collapse under their own weight.

At this critical stage, AI is 20% tech, 80% culture

As organisations race to adopt AI, many find the real stumbling blocks aren’t technical, they’re human. Legacy mindsets, siloed teams and resistance to change can quietly sabotage even the most well-funded AI initiatives. I’ve said it before, and I’ll say it again: sustainable AI transformation isn’t driven by code, but by culture.

IBM’s CEO Guide to Generative AI reinforces this, warning that AI success hinges more on mindset and talent than on tech stacks. Without upskilling and a cultural shift, even the best models stall. AI needs to be woven into the cultural DNA of the business or brand — not as a tool, but as a new way of thinking and working.

Responsible AI is no longer optional

A significant part of AI maturity is ethics. As AI systems take on more decision making power, trust becomes the defining competitive edge. Customers, employees, regulators — they all want to know: Can your AI be trusted?

Too often, responsible AI is treated as an afterthought, something to deal with after the tech is live. But this mindset is increasingly risky. In reality, ethical and transparent AI isn’t just about compliance. It’s about building systems that reflect your values, earn user trust and stand the test of scrutiny.

Responsible AI isn’t a sideshow, it’s your trust currency. And without it, your AI efforts risk undermining your brand as fast as they’re meant to advance it.

Annual planning: From paper trail to intelligent foresight

For years, annual planning has meant long, manual cycles, all riddled with spreadsheets, static assumptions and top-down directives. But that model is reaching its limits. The pace of change is too fast, and the cost of reactive decision making too high.

This is the opportunity, not for a complete reinvention overnight, but for a more intelligent, adaptive approach. AI can now support strategic planning with live insights, predictive modelling and operational clarity. Even if your AI operating model is still evolving, this is the time to start asking better questions:

• Where can AI deliver early, measurable value?
• What does a 12–24 month AI roadmap look like in your organisation?
• How can AI amplify existing business models, not replace them?
• Which roles can shift from administration to real strategic contribution?

We’re not suggesting theory, we’re seeing this play out in real organisations, right now. Mature AI doesn’t just automate. It reframes how businesses think, decide and adapt.

The future of business has a Decidr operating system

The next generation of business won’t run on disconnected tools and static dashboards. It will be fuelled by real time data, intelligent agents and a decision system that learns, adapts and aligns with your goals.

Decidr is that system. Decidr is your pathway to AI maturity. It acts as an intelligent parallel of your enterprise, a unified AI database infrastructure layered with thousands of autonomous agents that accelerate decision making across every function: sales, finance, HR, marketing, procurement and everything in between.

It connects siloed systems, interprets structured and unstructured data and integrates with your existing tech stack. Whether you're managing a single business or an entire group, Decidr brings it all into one cohesive, agentic platform.

Think of it as the “Shopify of AI” but for how your whole organisation thinks, plans and acts.

This is not just how AI will support business. This is how the businesses of the future will operate.