Opinion: Calculating the business value of GenAI

After last year’s generative AI hype, executives in Australia are impatient to see returns on their investments, yet many organisations are struggling to prove and realise value.

As the scope of initiatives widen, the financial burden of developing and deploying generative AI models is increasingly felt.

Gartner predicts at least 30 percent of generative AI projects will be abandoned after proof of concept by 2025.

This is due to escalating costs, unclear business value, poor data quality or inadequate risk controls.

While it's relatively easy and inexpensive to pilot generative AI , scaling is when it can potentially get tricky and where the uncertainty and inability to predict costs and value upfront becomes difficult.

A major challenge arises in justifying the substantial investment in generative AI for productivity enhancement, which can be difficult to directly translate into financial benefit.

Many organisations are leveraging generative AI to transform their business models and create new business opportunities.

However, these deployment approaches come with significant costs, ranging from $5 million to $20 million.

Unfortunately, there’s no one size fits all with generative AI , and costs aren’t as predictable as other technologies we’re used to implementing today.

The use cases you invest in, deployment approaches you take and the scale of those deployments, all determine the costs.

The road to realising the value of generative AI should start with identifying your organisation’s AI ambition.

Whether you’re a market disruptor and want to infuse AI everywhere, or you have a more conservative focus on task or job specific productivity gains and extending existing processes, or your aim is to transform your industry.

Each has different levels of cost, risk, variability and strategic impact.

Regardless of the AI ambition, generative AI requires a higher tolerance for indirect, future financial investment criteria versus immediate and direct return on investment (ROI).

Historically, many CFOs haven’t been comfortable with investing today for indirect value in the future.

This reluctance can skew investment allocation to tactical versus strategic outcomes.

Realising business value

By only looking at immediate financial returns, we often miss the full picture of the value created by AI/ generative AI .

There are many early successes, such as reducing the time and cost for drug discovery and creating completely new drugs that are now entering into clinical trials; creating new designs in manufacturing to optimise costs; or simply shortening the time it takes to write job ads by 90 percent.

Most organisations demonstrating early business value are building a portfolio of use cases weighted towards defending their competitive position with incremental improvements or extending current processes for differentiation.

A much smaller percentage of organisations are taking the risks and longer-term investment view needed for use cases that have the potential to upend their industries, core process and business models.

Earlier adopters across industries and business processes are reporting a range of business improvements that vary by use case, job type and skill level of the worker.

Respondents to a Gartner survey of 822 business leaders reported 15.8 percent revenue increase, 15.2 percent cost savings and 22.6 percent productivity improvement on average.

This data serves as a valuable reference point for assessing the business value derived from generative AI business model innovation.

But it’s important to acknowledge the challenges in estimating that value, as benefits are very company, use case, role and workforce specific.

Often, the impact may not be immediately evident and may materialise over time. However, this delay doesn’t diminish the potential benefits.

In fact, generative AI has the potential to generate value in various forms, such as improved productivity where newly gained time can be used to enhance customer engagement and improve conversion rates; or reduce working hours for developers resulting in lower burnout rates and recruitment costs.

These long-term benefits should be considered when assessing the business value of generative AI business value.

Collaborating with HR, finance, legal and corporate strategy as early partners optimises benefits realisation by ensuring efforts are in place to manage change, strategically use time savings from productivity improvements and minimise risk from negative impacts of AI.

Calculating business impact

By analysing the business value and the total costs of generative AI business innovation, organisations can establish the direct ROI and future value impact.

This serves as a crucial tool for making informed investment decisions to build an optimal use case portfolio aligned to achieve the organisation’s AI ambition.

If the business outcomes meet or exceed expectations, it presents an opportunity to expand investments by scaling generative AI innovation and use across a broader user base, or by implementing it in additional business divisions.

However, if the outcomes fall short of expectations, it may be necessary to explore alternative innovation scenarios.

Fast cycle innovation labs should not only be used to quickly assess technical feasibility, but also proof of value. These insights help organisations strategically allocate resources and determine the most effective path forward.

About the author

Rita Sallam is a distinguished VP analyst at Gartner, exploring how leaders can leverage disruptions in AI to create sustainable competitive advantage and realise business value from their data, analytics and AI investments. The latest AI trends will be discussed during the Gartner Data & Analytics Summit in Sydney (29-30 July).