5 ways CIOs can keep pace with AI innovation

Doug Drinkwater

Inside AI’s innovation

From GenAI tools to agentic systems, technology executives must navigate the relentless pace of AI innovation, balancing business empowerment with governance to achieve measurable ROI.

Keeping up with AI innovation has become a daily challenge for technology executives.

In the last 18 months alone, the technology industry has been sent into a tailspin by the introduction of OpenAI’s ChatGPT and Microsoft’s Copilot, the tech stock explosion of Nvidia and the leftfield arrival of China’s Deepseek.

More recently, we’ve seen the beginnings of the 21st century space race – for AI sovereignty – while the US administration's new tariffs have put the tech industry on red alert for what happens next to global economies and supply chains.

For technology executives, this macroeconomic uncertainty breeds caution; Where does market hype meet reality? Where do you ‘safely’ invest in a downturn? Which partners do you work with in a growing market? And what is going to be truly valuable – and measurable – for your organisation?

This Food for Thought discussion, hosted by HotTopics in partnership with Avanade at the Sky Garden in London, was attended by the following representatives, under Chatham House Rule:

Tech executives drive AI innovation through pragmatic collaboration

With AI increasingly making newspaper headlines, and boardroom minutes, today’s tech executive has a critical role to play as AI innovation sense-checkers – bridging the gap between technological viability and practical business application.

This is not, however, without a degree of risk. Larger enterprise buyer teams, growing line-of-business budgets and the consumer ubiquity of AI have, in the words of several executives here, meant that the “genie's out of the bottle” – if your employees aren’t already playing with ChatGPT, they’re probably thinking about it - while your CEO has likely hastily called for your latest AI strategy.

For tech leaders, this not only shows the importance of their roles, and the influence they yield at boardroom level, but also the need for delicate stakeholder management.

To drive responsible AI adoption, the ownership and accountability of these technologies must rest on everybody’s shoulders, not solely those of the CIO or CTO.

"If you make it too easy for [the business] to say yes, it's going to get stuck at that point,” said Jacqueline Schofield, an executive at Avanade, emphasising the need for executives to have ‘skin in the game’. Another argued that the federated nature of AI deployments - and funding – means that it is a company-wide responsibility.

“I'm using this as an opportunity to change the ownership of the data. It's not IT that owns the data. We own the thing that the data sits on,” said one data leader.

Successful tech leaders are subsequently approaching AI with cautious pragmatism- finding the line between business empowerment and governance - with a ‘meta’ view towards establishing strategic value in the face of heightened market expectations.

As such, many are not rushing into big bets; they are starting with small, ‘safe’ experiments which can be scaled up or down accordingly.

One executive described implementing AI to improve software development - a change that not only improved coding efficiency but also helped train executives on AI capabilities, thereby creating a virtuous cycle of adoption and understanding. Another, having implemented Microsoft’s Copilot, established processes so that users acknowledge guidelines around usage prior to usage.

Small wonder then that executives here colourfully described their AI maturity as ‘toddler’, ‘medium rare’, ‘scattergun’ and ‘watchful’.

AI ROI: How to pass the CFO ‘sniff test’

This ‘watchful’ remit is down, in-part, to fledging AI use cases being (so far) unable to drive tangible Return on Investment (ROI).

Instead, technology executives are exploring softer, more intangible benefits - from improved workplace culture and change management to productivity gains, process efficiencies and enhanced customer experience.

During this session, there were some examples of early gains; from AI being utilised for architectural diagram analysis - thereby reducing the validation time from hours to minutes, and achieving cost savings by spotting design flaws earlier, to expedited document processing and credit checks.

“We've seen things that AI has brought to the table that maybe don't stack up in a business case for the CFO,” said Avanade CTO Tony Hinkley.

As such, the AI ROI conversation requires clear communication and expectations from the outset, but also an understanding of what information is needed to pass the ‘CFO sniff test’:

Does the initiative solve a real business problem?
Can the value be quantified?
Does it have long-term strategic impact?
Is there a clear ROI or value creation pathway?

Despite this, some suggested that the transformational nature of AI should reframe how we view business value, much as it did with the introduction of SaaS and cloud computing.

“We don't need to say what the return on investment we get for [Microsoft] Office 365,” said one data leader.

Another respondent here debated the legitimacy of marginal gains via AI. Working in the financial services sector, a combination of RPA, machine learning and logic-based systems had already improved this firm’s loan processes by as much as 95 percent- but now there was a question of whether AI would be worth exploring at such cost for such marginal improvement.

Reframe AI governance as responsible adoption

The tension between innovation and control has never been more pronounced for technology executives. Organisations are grappling with how to enable experimentation while maintaining robust AI governance - and how stringently those need to be applied.

The consensus from this HotTopics Food for Thought discussion was clear: governance isn't about restriction, but about creating an adaptable framework which allows for scalable AI adoption and growth.

"We need guardrails that are flexible enough to be updated as we learn,” said one leader here. “Don’t be afraid to move the guardrails”, added Avanade’s Hinkley, suggesting policies can and should be regularly updated as technology evolves.

Some key governance strategies from this Food for Thought discussion were as follows:

Establish clear AI policies and guidelines
Create cross-functional AI centres of excellence in each business unit
Develop flexible guardrails that can evolve with technological progress
Implement robust data management practices

Despite this, responsible AI adoption and scalability also goes beyond technological implementation. Participants here stressed the importance of:

Connecting AI initiatives to clear business outcomes
Focusing on incremental value rather than wholesale transformation
Ensuring data quality and integrity
Developing a company-wide culture of continuous learning

Audit your skills gaps for AI workforce transformation

To successfully implement AI, organisations must assess their AI workforce readiness, ensuring their teams have the necessary confidence and skills to drive adoption and long-term transformation.

Some speakers here recommended not only ‘auditing’ the competencies of staff internally, but understanding the ‘push and pull’ of demand; in some cases, individuals, or even entire departments, will be looking to drive AI adoption to enable them to do better, more productive work.

“We've been ‘partner zero’ with Microsoft as they started to introduce CoPilot…We've been experimenting and allowing people to experiment, because we didn't know… even if we sat down with our strategy hats on… all the different potential use cases and benefits that our people have found,” said Hinkley.

And yet organisations also face the laggard effect; of staff disincentivised to learn, or perhaps scared to do so.

“I know a lot of data scientists who feel really concerned because it's like an art form, and the thing that they just spent years doing their PhD on. Is it going to be eroded by somebody with a CoPilot license and a bit of enthusiasm?" added Schofield.

At Avanade, the Accenture and Microsoft company, the professional services firm has run cross-sector AI training for thousands of global employees. The focus has been on training across all organisational levels, addressing workforce knowledge gaps and preparing for AI-driven workforce transformation.

In other organisations, AI champions or working groups have been established to upskill and reskill, while addressing fears.

Human augmentation, rather than displacement, should be the overarching message here. As one such example, in software development, AI tools are already proving transformative – not by replacing developers – but by enhancing their productivity; generating unit tests, improving code quality and reducing repetitive tasks.

Pragmatic AI innovation: Don’t get sucked into the hype bubble

The continued pace of AI transformation can be hard to keep up with. Alongside the technological disruption, tech executives face increasing global and industry-specific regulation, generational shifts in workplace skills and attitudes, and an increasingly complicated (and expensive) tech stack.

“Avoid getting overly sucked into the turbine of the hype engine”, said one speaker here. "This is just another product following a passing trend", added another.

By maintaining a balanced approach to AI innovation which prioritises governance, values incremental change and keeps human potential at the centre, organisations can transform AI from a buzzword into a genuine competitive advantage.

Here are some actionable insights for navigating the AI landscape, given the pace of change:

Invest in comprehensive training

Implement AI training across all organisational levels, from the shop floor to executive suite. Understanding creates comfort and reduces resistance.

Embrace a learning mindset

Recognise that Generative AI is a first-generation technology - a ‘revolutionary’ technology implemented in an evolutionary fashion. Expect iterations, be patient with imperfections and maintain a ‘meta’ forward-looking perspective.

Prioritise the data foundations

Before diving into AI, ensure robust data management. As multiple executives noted, "garbage in, garbage out" remains a fundamental truth.

Establish AI champions

Identify and empower users who naturally gravitate towards technological innovation. These individuals can become powerful internal advocates to drive wider change.

Maintain a holistic value perspective

Evaluate AI initiatives beyond simple cost-saving metrics. Consider broader impacts like employee bandwidth, process efficiency and potential revenue generation.

Stay adaptable

The AI landscape is changing rapidly, with vendors rolling out AI functionalities faster than organisations can implement. Maintain flexibility in your approach, policies, and technological infrastructure. Critically evaluate value and resist this knee-jerk technological adoption driven by market hype.

To find out how to close the gap between AI vision and reality, read Avanade’s global research report to explore the primary drivers behind AI adoption - and what’s holding organisations back.

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5 ways CIOs can keep pace with AI innovation

Inside AI’s innovation