How to Avoid Overspending on AI

How to Avoid Overspending on AI

AI is the darling of the moment and the corporate meme-stream. So much so, it’s forcing some CFOs into tough discussions with executive teams that are overly anxious to gamble with current assets in hopes of gaining future competitive advantages. How can companies avoid overspending in AI?

AI certainly holds serious potential, even as research continues to map its exact capabilities. But CFOs are rightly concerned about overspending and creating enterprise-wide, inflexible AI/IT platforms rather than scalable solutions and capabilities. As we saw with recent overspending on data lakes and clouds, when it comes to technology, imitation for imitation’s sake is dangerous.

Whether executives see AI as a technological magic wand, eliminating all of their current and future woes, or as just the latest “flavor of the month” technology, one thing is clear. There is significant investment risk associated with an incorrect assessment of AI’s value.

Executives who are too optimistic in their assessment of AI’s value risk overspending. They will also fail to achieve projected results, and disrupt their organization. If their view of AI is too pessimistic, they likely will under-invest and potentially fall behind competitors.

There’s little question AI will end up being a transformative technology rather than an enabling one. Still, randomly experimenting with ad-hoc AI pilots without an overarching strategic blueprint is a bad idea. It will inevitably lead to pilot purgatory at worst and the development of limited point solutions at best.

We have developed a set of guiding principles for building executable, scaled solutions and avoid overspending in AI. The below insights are based on emerging client work, 60-plus interviews, and a year-long collaboration with the World Economic Forum. (See WEF Future of Production for details).

Step One: Identify the Right Use Cases

First, identify actual business problems with tangible impact and develop a list of underlying issues or pain points that AI can address. Start by conducting a systematic scan of current pain points. This should be done at the right level of activity specificity — across functions to identify overlapping activities and needs.

Note that assessing whether bots can automate customer-care calls and interactions is shooting too high. No AI technology exists today, or will in the near future, that can handle such a broad range of tasks.

Drill deeper to surface granular-level problems, such as newly trained customer call-center agents’ lack of experience identifying “best responses” during email inquiry sessions (an issue that requires machine-learning solutions that perform text mining and question-and-answer inference). The solution to these problems will become the foundation for designing and prioritizing AI-enabled solutions and use-case development.

When it comes to prioritizing potential use-case opportunities, companies need to ask themselves some basic questions such as:

– Is the underlying AI technology mature enough? Will it be mature enough in 24 to 36 months, to enable the use-case solution?

– Does the organization have access to sufficient data to train and test the AI use-case solution?

– Can these use cases potentially lead to “high-impact” solutions in terms of providing insights, gaining efficiencies, or speeding up decision-making?

Step Two: Manage a Portfolio of Initiatives

Next, companies should manage deployment and scaling of AI use cases as they would an investment or product-innovation portfolio. They should optimize both near-term and longer-term value creation while mitigating risks. They should sequence or group use-case pilots rather than evaluating them individually.

Multiple use cases may need the same training data, creating data-synergy opportunities. Build a data lake by combining shelf point-of-sale data, competitor promotional data, and online sales data. The lake can be leveraged to train AI across multiple use cases. For example, predictive demand forecasting as well as automated inventory allocation and replenishment bots.

Also, the performance of one use case may benefit another. AI that performs “sentiment analysis” can benefit downstream use case for “suggested response to call-center agent based on question.”

Step Three: Engage the Right Ecosystem Partners

Finally, in order to develop rapidly deployable proofs of concepts, companies should leverage internal experts, existing supply-chain partners, and best-of-breed AI technology startups.

While the AI-provider landscape is fragmented, it broadly divides along these dimensions:

– Primary focus: Most AI vendors are specialists in areas such as image recognition, language translation, pattern recognition, or predictive analytics, not generalists across technologies

– Niche players, which focus on developing specific industry and functional use-case solutions and have the benefit of calibrating their algorithms on relevant training data across multiple clients

– Platform players offering more foundational AI algorithm capabilities rather than domain- and function-specific expertise

Companies should leverage platform players to gain access to underlying technologies in order to pursue an in-house path for the development of use-case solutions. This is especially true if a company has training data that’s highly differentiated from what external niche-solution providers offer, or needs to protect a proprietary process it hopes to integrate into subsequent AI solutions.

An investment trading firm looking at deep learning to improve its algorithmic trading predictions would likely source the underlying technology APIs from Google or IBM but develop the use case in-house. This would be help keep its vast trove of trading data and processes proprietary.


While the jury may be out for some time on AI’s full potential, it will clearly be a foundational pillars of the technologically enabled future.

The CFO’s mission is to avoid overspending in AI. This includes separating fact from the “hype cycle,” identifying current and likely use cases, developing a methodology for vendor selection and measurement, and integrating near-term and mid-term AI strategies into their existing technology investment plans

The above article originally appeared in

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