Re-thinking IT investment strategies in the age of AI – what’s at stake?

By Giorgio Danesi, EMEA Leader for Data and Technology Transformation at IBM Consulting


As businesses across industries recognise the power of generative artificial intelligence (AI), budgets allocated to these initiatives are also on the rise. This revolutionary technology has the potential to rewrite even the most time-honoured business functions – and in tandem, is changing the way leaders manage their IT estate.

Recent insights from IBM Institute for Business Value’s ‘The CEO’s guide to generative AI’ on tech spend reveal where the biggest changes to IT planning are taking place. IT executives expect this year’s AI budgets to be 3.4 times greater than they anticipated midway through the year. And we expect the projected spend will continue to rise as generative AI matures and use cases take shape. But where and how will the increased budget be spent? And where is it coming from?

Broader IT implications

While greater investment in AI is critical for staying ahead, it comes with the challenge of diverting resources away from other essential areas. Only 15% of execs expect to fund this increase with net-new spend. Instead, many plan to plunder other parts of their IT portfolio, with 33% saying the money will come at the expense of non-AI IT spend. A slightly larger portion (37%) expect to pull generative AI spending from the broader AI investment portfolio, which could reflect expected synergies across traditional and generative AI projects.

Investing in growth

This approach to reallocation seems reasonable, but is it realistic? As generative AI is rolled out across a business, it will have cost implications across-the-board. For example, spending on specialist staff and cloud provision will need to grow to support the increasing demand for generative AI solutions.

What’s clear is that CEOs need a sharper understanding of how high-impact projects will draw in resources, both human and technical, to accurately budget for associated costs. To help achieve this, leadership teams must:

  • Assess the entire gamut of the costs required to deliver the impact they expect from generative AI. This should cover the range of IT, cloud infrastructure and people necessary to deliver it.
  • Enhance cloud financial operations (FinOps) to gain visibility into costs and spending across all AI, hybrid cloud and application modernisation investments.
  • Understand what people are working on and how much they cost, and map that back to specific projects, applications and initiatives to optimise spend.
  • Keep an eye on graphic processing unit (GPU) chips, which are needed for generative AI but are in very short supply. Their market price will drive up the cost of building and delivering generative AI services and is also likely to show up in enterprise cloud costs.

The cost of talent acquisition

Generative AI is already more intuitive than many hype-cycle innovations but companies still need internal expertise to gain a competitive advantage. According to IBM’s ‘Leadership in the Age of AI’ study, 95% of leaders say they are taking steps to ensure they have the right AI skills within their organisations. However, deep generative AI experience is scarce, which makes talent acquisition an expensive proposition.

If companies want to obtain the necessary skills, they’ll have to be willing to pay market rates and create positions that offer employees the types of roles they’re after. IBM’s CEO guide on tech spend indicates, though, that IT executives are forecasting declining labour costs. This could turn out to be wishful thinking as each generative AI model comes with its own set of staffing requirements, which means net-new costs will vary for each implementation.

This puts leaders in a tough position, forcing them to estimate the financial impact of job roles that don’t yet exist. But they can, and should, invest in the skills of existing employees, preparing them for this new world of work.

For the existing workforce, it’s not about being an expert in every aspect of AI, but understanding how it will impact an individual’s field and having the ability to work with AI in their specific domain. People also need to understand the governance of AI – that it needs to be transparent. In this way, infusing AI across a company’s workflows will not only drive new insight and innovation, but also lead to a more empowered and productive workforce.

A focus on income generation

The old adage “you can’t cut your way to growth” remains as true today as it ever was. CEOs need to identify which AI use cases will drive the most transformative developments and invest in them, as the cheap options aren’t expected to deliver. Just 2% of executives expect to gain an edge by subscribing to public generative AI services, such as ChatGPT, whereas 38% say using a vendor’s platform with their own proprietary data will deliver that advantage.

Rather than focusing on income-generating areas of the business, organisations are spreading generative AI funding across multiple cost centres. Almost three quarters (74%) of generative AI spend will go on HR, finance, customer service, sales and marketing and IT. Only 26% will go to product-related business functions, the source of growth-driving innovations.

This makes it difficult to define business cases that break the mould. CEOs therefore need to make data-driven decisions about which generative AI implementations are the best at landing their strategic objectives and provide the greatest return. They should worry less about financial precision until an initiative worth doing has been designed. And be ruthless – eliminate any schemes that won’t improve the value of the enterprise within three years and direct funds to programmes that will. That way, spend will drive clear growth potential, not just short-term savings.


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