The 60-30-10 Rule: A Key to Successful Tech Transformation

A lot was said and written about AI and new technologies already, so let me make a seemingly counterintuitive contribution: the key to success is not in having the most advanced technology. It’s about cultivating trained, motivated people who can use these technologies effectively, supported by good data for the technology to process, evaluate, and learn from.

After 25 years of implementing smart, data-based solutions, I’ve developed a simple formula: the 60-30-10 rule. On average, 60% of your success (and effort too, ideally) is about people, 30% is driven by data quality, and technologies contribute just 10% of total impact.

Why is the human factor so important? It's about trust and real-world examples. Trust builds slowly, through positive experiences and transparent communication. One of our most successful projects – implementing a digital banking system across Central European countries – is a great example. Our simple core belief – that employees must first embrace a product to convince customers – is what helped us win and complete the project.

To achieve that, we created a network of ambassadors who could explain and demonstrate the new banking system’s benefits to both colleagues and customers. By placing at least one ambassador in every branch and team, we exceeded our (already ambitious) first-year user adoption targets five times, ultimately creating a core platform for all pro-client innovations across the group, serving over 10 million users across Europe.

One thousand songs in your pocket

Leadership involvement makes an enormous difference. When the entire management team – not just the CIO – gets behind a technological change, transformation accelerates. The KPMG Global Tech Report survey shows that 38% of international companies now feel their top management supports new technologies, a big jump from just 10% in 2022.

Top management must create a clear, compelling vision and implementation and marketing strategies for a new technology. The failure of Microsoft Zune music player after just five years on the market is a fitting example. While it was a great piece of technology, it lacked a clear product vision and could not compete with Apple’s narrative of “1000 songs in your pocket”.

The human factor is key for ordinary employees as well. People naturally fear and resist change, particularly when technological advancements threaten to take their jobs. However, the World Economic Forum's 2023 Future of Jobs Report shows that such pessimism is unfounded. While AI and automation might replace 85 million jobs by 2025, they are also projected to create 97 million new (often better) positions – a net increase of 12 million jobs.

In other words: technology remains a tool designed to enhance human productivity, not to make it obsolete. Employee roles will evolve and change, as they always have, but they won’t disappear. That much is already evident across sectors from automotive to medicine, investments, or content generation – content much like this very article.

Individual skills will become increasingly decisive as those without the necessary technological competence will either misuse new technologies or avoid them entirely – and indeed, risk losing their jobs. The WEF report estimates that within a decade, around 60% of the workforce will need to change their workflows or learn completely new ways of doing things. A pointed joke captures this reality: A CFO asks the CEO, "What if we train employees and they leave?" The CEO responds, "What if we don't, and they stay?"

Data, the Raw Material

Let's examine the 30% attributed to data quality. For technology, data is the raw material, so the output quality will only be as good as the data it processes and is based on. The point being: don’t skimp on data collection and quality. Our extensive experience shows that quality data can improve the impact of analytical tools by up to 40% while reducing implementation costs by one-third.

The Tay AI chatbot experiment is an extreme example of data vulnerability. Tay’s purpose was to communicate with Twitter users and learn from their interactions. It was designed to mimic a 19-year-old girl with the goal to engage young audiences. However, within 24 hours, a coordinated attack of Twitter users manipulating Tay with racist, sexist, and other unacceptable behaviour completely compromised the experiment, forcing an immediate shutdown.

The remaining 10% (still a substantial number) is about technology that makes it all possible in the first place. But unlike people and data, businesses don’t need to perfect technology – it’s already optimized by the providers. Apps and technologies are designed and developed meticulously, addressing security, ethics, user experience, and economic considerations, so the standard offering is more than enough and rarely

requires any significant individual changes. That applies to both consumer mobile apps and large gen-AI tools. According to this year’s KPMG GenAI Study, most companies use standard, external GenAI solutions, with only 12% planning to develop proprietary tools.

AI and emerging technologies possess an enormous potential, but their success is largely dependent on the users. The most advanced technology will fail if used poorly or fed bad data – so investing into people that will use the technologies is crucial. Who will succeed in the digital era? Companies that invest in their people and embrace continuous learning. Because technology will never stop evolving, and neither should we.

Originally published in “Právní rádce” magazine