The data imperative: unlocking ROI in tech transformation

We are living through a period of unprecedented change. The pace of technological advancement, particularly in artificial intelligence, is rewriting the rules of business. Our latest C-suite barometer confirms what we’ve started to sense: investment in AI and emerging technologies is at an all-time high, yet the foundational pillars of data quality, governance, security and change management are not being prioritised with the same attention and urgency. This contradiction could alter the future of this transformative revolution, with profound implications for return on investment and long-term resilience.

“AI is moving from being a buzzword to being the backbone of business.”

We all know that artificial intelligence has moved from being a buzzword to what may feel like the backbone of business, but leaders should proceed with some caution. This has created over-confidence and a false sense of security. Getting it wrong can have detrimental financial and legal consequences, not to mention the reputational damage it can bring.

The current state of play with AI adoption

Transformation through IT and technology remains the number one strategic priority for C-suite leaders. Three-quarters of businesses now have a technology transformation strategy and 76% of executives cite AI implementation as their top investment area. Yet only 32% are prioritising investment in data, security, and infrastructure in the coming year, despite 42% acknowledging this as critical to successful transformation.

This gap is not just theoretical. Many organisations have jumped on the AI bandwagon, eager to harness its potential and rushing in to avoid being left behind. While this is important in such a competitive landscape, retrospective fixes are both costly and difficult, and the rush to deploy AI is being done in isolation, without considering the necessary interconnectivity of data requirements and often outpacing the development of robust governance frameworks. As a result, organisations will be more exposed to costly risks and regulatory challenges if they don’t create equal prioritisation across all three elements in their technology strategies.

It's encouraging to see that C-suite executives are taking the importance of data seriously with insufficient data quality and infrastructure confirmed among the top three barriers to successful AI adoption. However, the level of investment in this area doesn’t match up.

Investment vs confidence

Despite AI being the highest area of investment, only 48% of C-suite leaders are confident they will see a return. Nevertheless, 88% expect technology transformation investments to increase profits over the next five years.

There is, I believe, a false sense of security. Many organisations lack a coherent AI strategy and are therefore investing reactively as opposed to strategically. ROI is not always tangible. AI often helps improve quality, but leaders should, be assessing how to get a tangible metric on defining quality too. Unfortunately, the challenge of measuring returns will ultimately be compounded if foundational data quality isn’t equally prioritised.

Why governance, security and controls matter

Data and governance shouldn’t just be viewed as technical concerns. They are strategic imperatives. AI hallucinations, or incorrect outputs, are driven much more by poor data as they are in current technology limitations.  With better more accurate decision-making the ultimate goal of AI among leaders, the lack of rigorous data management puts this at serious risk of decisions being based on flawed insights, trust being undermined and greater exposure to regulatory penalties – especially those outlined in the EU AI Act where we can expect regulators to start setting precedents.

Moreover, the emergence of “shadow AI” (AI tools used without oversight) poses additional risks. More and more, we’re seeing employees across organisations frustrated by slow adoption or ‘censored’ use, when they’ve already embedded it into their behaviour or every day personal lives. Individuals are turning to external tools, often inputting sensitive company data without thought, guidance or proper safeguards. This not only jeopardises data security but also complicates incident management, crisis response and the scale of accountability and consequences on any organisation.

“Data, governance and security go hand in hand. Those are foundation components of getting AI right.”

Our barometer report points out that three in five leaders believe their data is completely protected but perception and reality are two different things. In the last year, the numerous high profile security breaches have been a wake-up call for many organisations. Regardless of the level of confidence being high among leaders, it’s no longer a matter of if, but when and organisation will have a security incident. The common source of breaches in many cases been from third parties, so cyber security also now demands closer management of third-party risk as AI investments increase, and adoption cases expand.

Adaptability is the ultimate competitive advantage

Organisations that can swiftly adapt their operating models, structures, roles, and governance to leverage AI will outpace those that cling to legacy models. I predict a shift towards an “hourglass hierarchy,” with more junior or early-stage workforce members adept at using AI and seasoned senior leaders providing critical oversight, while middle management roles may diminish.

“Adaptability beats ambition right now.”

Boards must become tech and AI savvy, appointing heads of AI governance, policy and security alongside recent innovation roles created. The organisational structure must evolve to reflect the reality of AI-enabled operations and scale of investments made, potentially mirroring the transformation we’ve seen in cyber security and ESG roles over the past decade.

Maximising AI returns, minimising data risks

To unlock ROI in tech transformation, leaders must move beyond the allure of AI investment and prioritise the data imperative. This means investing in data quality, governance, and infrastructure as foundational pillars, not afterthoughts. The organisations that succeed will be those that rewrite the rules, embedding adaptability, robust data practices and proactive governance at the heart of their transformation strategies.

The pace of AI development will not slow, and this data imperative is not optional. It is the key to unlocking sustainable ROI in tech transformation.

The leadership playbook for AI-driven ROI

In a world where technology is rewriting the rules of business, leaders need more than ambition, they need a clear playbook. Here’s how to turn disruption into opportunity and secure sustainable ROI.

1. Prioritise data quality and governance
   Treat data management as a strategic imperative, not a technical afterthought. Allocate budget and leadership oversight to ensure robust frameworks for data integrity, security and compliance.
2. Integrate AI with foundational data and infrastructure
   Avoid siloed AI investments. Align AI deployment with strong data architecture and cyber security measures to minimise risk and maximise ROI.
3. Adapt your organisation’s structure to be AI enabled
   Adapt your operating models, structures, roles, and governance to leverage AI.  Appoint dedicated leaders for AI policy, ethics and security, as well as specific technology adoption roles.  Boards should actively oversee these functions.
4. Address shadow AI risks
   Implement clear guidelines and monitoring to prevent unsanctioned AI use and protect sensitive data from external tools.
5. Measure ROI beyond financial metrics
   Develop KPIs for quality, efficiency, and resilience to capture the full value of AI-driven transformation.