Going into 2025, we must have the understanding that AI is no longer a futuristic concept. Yes, it is here, and of course is here to stay, whether you like it or not. It will ultimately be involved in reshaping industries and creating entirely new ways of doing business.

But, while the headlines are dominated by stories of OpenAI talking about AGI being around the corner 10 minutes away, the reality in many boardrooms is far less impressive. Well, okay maybe, OpenAI are being a bit over the top with their predictions as the current AI offerings are less AGI more sounding board with no little to no emotion.

I think organisations are failing to realise AI’s transformative potential, and I know that is a bold statement, but it’s not because the technology is lacking (well in some ways it might be), but because leadership thinking is stuck in the past. Yep, there I said it! I’m not the only one saying that though as colleagues like Bill Schmarzo, Mark Stouse, Eddie Short and Malcolm Hawker have said the same over the last few years.

Let’s face it, if we are looking at AI today, it’s a tool for incremental improvement. But, in the long run it won’t be that at all. It will offer the opportunity to fundamentally rethink your business strategy, redefine how value is created, and establish a competitive advantage that is unassailable.

However, that requires bold leadership, clear strategy, and a willingness to challenge everything you know about how your organisation operates.

As I see it, it’s not BAU any longer, it’s about thinking about your business as never before.

Yeah, it’s a fact, that many are hypnotised by the allure of AI. The promises of improved efficiency, personalised customer experiences, and new revenue streams have CEOs and boards investing billions. According to McKinsey in their “State of AI reports”, less than 20% of companies report significant financial benefits from their AI initiatives.

Why?

Because my belief is that most organisations approach AI with the wrong mindset. Here are the key challenges:

1. AI as a technology project, not a business strategy

Most companies see AI as an IT initiative rather than a strategic lever. This leads to projects focused on technology deployment, chatbots in the main or automation, with little to no alignment to business outcomes. For example, a retailer might implement pricing models without addressing whether they truly differentiate the customer experience or increase profitability.

2. Fragmented efforts

We are seeing the ownership question about who owns AI in a business, and this has led to many AI initiatives being scattered across departments, with no central vision or coordination. Customer service work on chatbots or agents, operations experiment with automation, IT focuses on infrastructure. The result? Disconnected efforts that fail to create measurable impact.

3. Legacy mindsets and structures

Many C-Suites are still operating under 20th Century assumptions. Some of these that continue to persist are, growth comes from scaling products, efficiency comes from cutting costs, and the organisation is best managed through rigid hierarchies. I think we need to think like the six million dollar man, “better, faster, stronger” I’ve been waiting to get that reference in to one of my articles! These legacy models are incompatible with the speed, agility, and experimentation AI requires.

4. The data conundrum

This old chestnut! Yep, as many have said before data is the lifeblood of AI, but most organisations suffer from poor data quality, silos, and fragmented ownership. Without clean, accessible, and relevant data, even the most advanced AI models are useless. But we must be pragmatic here as most organisations have suffered this for a millennia! Yes, many of the current chatbots or agents are often using unstructured data, but, we have so much data that has been used to drive other machine learning algorithms, that we need to start thinking about structured data in the same vein. Maybe, using AI to clean up the crappy data and automate this as much as possible without human intervention.

5. Fear of failure

All new concepts suffer with this and at the moment AI is inherently experimental. Not every project will succeed, and that’s a risk many leaders struggle to accept. Yet, without a culture that embraces failure as a learning opportunity, organisations will never unlock AI’s full potential.

Where we are is at an exciting crossroads, and while the challenges are significant, the opportunities for organisations that get AI right will inevitably be extraordinary. We have to come from a mindset that AI can do more than optimise what you already do, it can unlock entirely new possibilities, here are my thoughts:

1. Reinventing business models

AI allows companies to create new revenue streams and reshape markets. For example, Rolls-Royce transformed its business model by using AI to monitor jet engine performance and maintenance schedules, moving from selling engines to selling “power by the hour” as a service.

2. Delivering personalised customer experiences at scale

Companies like Spotify have built empires by using AI to deliver hyper-personalised recommendations, creating a level of customer engagement that competitors struggle to match. Although sometimes, they do get it wrong when my daughter listens to something that then creates an anomaly! But most of the time they do get it right for me.

3. Enhancing decision-making

This is part of the nuggets of joy for many humans! The fact that AI can process and analyse vast amounts of data in more or less real time, provides leaders with insights that were previously impossible to obtain. For instance, a financial services firm who previously used ML to assess credit risk can now use Neural Nets to do the same and more accurately, perhaps uncovering new lending opportunities while minimising risk.

4. Improving operational resilience

From predictive maintenance in manufacturing to optimised logistics in retail, AI can significantly reduce costs and improve efficiency. Companies heavily reliant on their supply chains can use AI and automation to streamline their supply chains, enabling rapid delivery while keeping costs low and in some areas enhancing revenue through better demand forecasting.

5. Creating competitive advantage

AI’s ability to learn and adapt means the organisations that invest in it now can establish advantages that are difficult to replicate. For example, Tesla’s use of AI in autonomous driving is not just about cars, it’s about building an unrivalled data ecosystem that competitors cannot easily match. Ford, Toyota and others are running around to create this ecosystem and are still trailing.

The shift to AI requires a new type of leadership, one that is not satisfied with incremental change but is willing to challenge assumptions, take risks, and embrace discomfort. Here is what it takes:

Visionary thinking

Leaders must see AI not as a tool for optimisation but to reinvent their companies. This means asking big questions: What markets can we enter? How can we redefine the customer experience? Where can we create entirely new value propositions?

1. The courage to disrupt

We have heard it many times before and yes true leadership means being willing to disrupt your own business before someone else does. Uber, Amazon, Airbnb have done this, and it’s put  dent in many established businesses. Many will need to question current revenue streams or abandon long-held practices.

2. Collaborative mindset

This is a BIG one for me. I think companies need to rethink the way they collaborate as AI success will require cross-functional collaboration. Leaders must break down these silos, foster collaboration between business and technical teams, and ensure that everyone is aligned to the same strategic goals.

3. Focus on outcomes, not outputs

Yep, I know I say this a lot, but it’s true! Leaders must measure success by business impact, not technical achievement. Every AI initiative should be tied to a clear business outcome, whether it’s revenue growth, cost reduction, or risk mitigation. You need to set the tone and for everyone to be able to use the same rule of thumb.

4. Comfort with ambiguity

We are currently in a state of Volatility, Uncertainty, Complexity, and Ambiguity (VUCA), AI is not a straightforward journey. Leaders must be comfortable making decisions with incomplete information, experimenting, and learning as they go.

If you have read many of my articles or posts, then you will know that I specialise in helping organisations create effective strategies to use data and AI that drive meaningful business transformation. This framework focuses on aligning data and AI with your strategic goals and embedding it into the fabric of your organisation:

– Define your vision What does success look like in three to five years? This should not be a vague aspiration, but a concrete vision tied to business outcomes.

– Identify transformational use cases Which challenges, if solved, would transform your organisation? Prioritise use cases that deliver measurable impact and align with your strategic priorities.

– Fix your data foundations Do you have the data you need to fuel AI? If not, addressing data quality, accessibility, and governance must be your first priority, but, must be integrated with point 2.

– Build the right operating model Who is accountable for AI success? Do you have the right structure, culture, and processes to scale AI across the organisation?

– Commit to value-driven metrics Measure success by the business value created, not the sophistication of the technology. Every AI project should deliver a clear return on investment.

The Questions You Must Answer

• Are we ready to rethink our business strategy in this new AI world?
• Have we challenged our assumptions about what customers want, how markets are evolving, and where value lies?
• Do we have the courage to disrupt ourselves before the competition does?
• Are we creating a culture that encourages experimentation and collaboration?

AI is not a technology strategy, it is a business strategy. It’s no longer a choice for businesses; it is an imperative. But to truly harness its transformative potential, organisations must go beyond merely implementing technology.

They need to adopt a different mindset shift that redefines AI as an enabler of business reinvention. This requires bold leadership that is not afraid to challenge the status quo, a vision that aligns AI with long-term goals, and the courage to question entrenched practices that no longer serve a fast evolving world.

Those that thrive will be thinking about reimagining how decisions are made, how value is created, and how teams are structured to compete. It is not enough to dabble in pilot projects or focus narrowly on isolated use cases. Companies must embed AI into the very fabric of their operations, with a relentless focus on outcomes that matter.

Will the future belong to those with different mindsets? Yes, for sure and those who do choose to think differently, act decisively, and embrace AI as a cornerstone of business strategy will ultimately win. As they say, “Carpe diem!”.