For many in the trenches, terms like ‘digital transformation’ and the latest buzzword ‘AI-powered [insert noun]’ are the utterances of consultants and people in boardrooms. Over-usage of these more often than not result in a project not hitting the mark. 

The reality is that most organisational change fails in one way or another. The difference now is that failure has become AI powered. 

The current AI boom, with its eye-watering valuations and even more obscene infrastructure spending, fuels scepticism around whether organisations will really manage to adopt it and realise value from it.  

Historically, very few change efforts achieve lasting results*. The common team-up of poor strategy, insufficient communication and cultural resistance have simply found a new, more expensive arena to have their wicked ways. 

But, despite the high failure rate, taking steps towards AI adoption – look at me using the buzzwords – in your business is a positive move and there’s plenty of evidence to suggest this.  

Let’s look at an example. 

A business’ financial performance, market share, competitiveness, customer satisfaction and growth are all strongly influenced by the degree to which it is digitally mature**. This is borne out by the impact of tools like GPT on human labour revealing a 40% increase in productivity, an 18% increase in the quality of output and decreased inequality between highest and lowest performing workers, effectively lifting the median performance***.  

So, what can learn from the common failures? 

I won’t bore you with a deep dive, but there are three main common failure themes. 

The first theme is the process/strategy. This is the ‘how’. Often projects begin as technical solutions looking for a problem rather than a problem which requires a technical solution. Failure comes because of a lack of success criteria. Clearly identify your success criteria and you make the problem you’re trying to solve smaller. Small is good and increases your chance of positive results.  

The second theme is about your data. For any initiative, consider data your currency. AI is just the statistical model which enables the insight. This category is where you find issues like regulated data types not being accounted for along with the usual variability, format, silo and quantity issues. Understanding and taking control of your data is key. 

The third is about your people. Do you have the right players in the right roles? Do they understand where the business needs to go and how to get there? If you don’t prepare your people and bring them onboard you could face a backlash or resistance. Needless to say, they could of course also help you tackle the problems you’re facing, confirm if you’re cutting deep enough or whether your solution proposed is going to work with your skill sets. 

So, who’s got on top of these problems and succeeded? 

Manufacturer Caterpillar Inc. is a good example. 

To improve the business, Caterpillar management realised they needed to undergo a transformation that took them from being a heavy machinery seller to a heavy machinery seller offering integrated services. This successful transformation set them up to generate recurring revenue while also improving customer service through proactive maintenance. 

What they did was deploy the Cat MineStar Command suite and the Cat Helios cloud platform.  These connected 1.5 million assets globally, effectively turning large equipment into data-generating service assets all under a single enterprise-level data solution. 

The outcomes were impressive. Services revenue should reach $28 billion in 2026 and a refreshed loyal customer base is expected to spend 33% more on aftermarket services. 

So, what are some of the key takeaways from their transformation? 

Well, for starters, they gradually spent into their technology infrastructure for more than a decade. Three more learnings from them are: 

• They created a new team called Cat Digital to lead their transformation. This new division worked across the entire enterprise and had Cat MineStar Command suite buy-in and representation meaning the team had the firepower to crash through any human-created barriers and resistance. 

• The company had a really solid data foundation with Cat Helios, giving them access to the analytics and insights needed to implement proactive machine maintenance for their customers. 

• The project focused on generating services-oriented revenue by helping reduce customer machine downtime through proactive maintenance. 

The main insight: take a discovery-oriented approach 

The key to managing transformation successfully comes down to the approach the business takes once the drivers and problems are known and communicated to all the teams. It’s then a question of bringing plan and execution together before the change process starts.  

So, what about you and your transformation? 

Here’s a tip. With the approach and process so critical, why not think about your transformation from the perspective of a data science workflow? Data is the added complexity in anything AI related and it’s the common currency so picking a workflow centred around data makes sense. 

So, what’s the first step in kicking off your data science workflow? 

It’s discovery. This phase consists of two elements: the business aim, problem or challenge, and data acquisition.  

If we reframe this and add some oomph, we reposition this first step as covering two key areas. 

Understanding and preaching the problem  

This focuses on what we’ve already discussed around understanding the problem and the key issues you’re looking to solve. For example, if customer retention is a goal, are you planning to drive this through less complaints? This might mean something like achieving more face time with their representatives. 

When you understand your problem, you need to be able to preach about it. And to clearly get the message across to your people, you need a charismatic, authoritative leader who can make some noise. 

Another tip: we suggest that having technical knowledge is less important for this leader. His role is to push through the barriers and motivate your people to make sure your business culture is accepting and ready for change. 

Understanding your data 

If data is your currency, you want to know who has it, how it’s used, if there’s anything shady happening and how much of it’s moving around. Data drives statistics and AI is just statistics. There’s also some data like PII, special category or PCI data which can give you insights that get you in trouble, especially if accidentally exposed. 

Questions to answer before you undertake any transformation 

Answering these questions will help you identify what needs to be done in your business by way of a pre-prep before any transformation. 

Understand and preach the problem 

• What is your single highest priority goal? 

• How would this change support your overall strategy? 

• What metric are you driving and do I know through which variables you’re doing so? 

• Who else in the business might be feeling this pain and how are they experiencing it? 

• What resistance will you face and who would be the right leader to push through this? 

Understand your data 

• Where does your data reside and how can it be made accessible for your project? 

• Who are your owners and stewards to maintain the data going forward? 

• Are there any governance or compliance rules you need to be mindful of? 

• How is your business’ data maturity and how can technology meet them where they are? 

• How do your people use data at the moment? 

• What model – and therefore transformations – might I need to be applied to your data for your goals? 

Last but not least, take a step back and think, ‘what are we doing here?’. These kinds of projects are often complex but perhaps answers to these two final questions can help your understanding. 

Why should we do anything in the first place? 

Why should we do this now? 

Good luck!

We can help you understand your data to transform your business. Do get in touch with the team to find out more.

Sources

* Kotter, J. P. (1995). Why Transformation Efforts Fail. Harvard Business Review, March-April 1995.

** Valaskova, K., Nagy, M., & Juracka, D. (2025). Digital transformation and financial performance: an empirical analysis of strategic alignment in the digital age. Journal of Enterprising Communities.

*** Noy, S. and Zhang, W. (2023). Experimental evidence on the productivity effects of generative artificial intelligence. Science, 381(6657), pp. 582-586.

Caterpillar Inc. (2022) Leading the Way in Digital Technologies. Available at: https://www.caterpillar.com/en/news/caterpillarNews/2022/digital–commitment.html (Accessed: 8 October 2025). 

Caterpillar Inc. (2023) Executing Our Strategy (Annual Report). Available at: https://www.caterpillar.com/en/investors/reports/ar–archived–reports/annual–report–2023/executing-our-strategy.html(Accessed: 8 October 2025). 

Rio Tinto (Undated) Key performance indicators. Available at: https://www.riotinto.com/en/invest/reports/annual–report/key–performance–indicators. 

Rio Tinto (Undated) R&D and technology. Available at: https://www.riotinto.com/en/about/innovation/rd–and–technology. 

Rio Tinto (2018) Breaking from tradition: The Mine of the Future (Speech by Stephen McIntosh, Group executive Growth and Innovation, 14 May 2018). Rio Tinto, Sydney. 

Mine Australia (2024) Data, analytics, AI ‘vital’ in mining – Rio Tinto. Available at: https://mine.nridigital.com/mine_australia_dec24/ai–mining–rio–tinto