This short quiz will reveal whether your company is using AI strategically
The conversation around AI today reminds me of the dot.com conversations we were having in the late ’90s, or the direct-to-consumer conversations we were having in the 2010s. And we’re seeing similar results, too: a proliferation of projects that nobody in the organization truly has their arms around. In some ways, that’s great—after all, innovation is a numbers game. Statistically, the more you try the more likely you are to find something that will be valuable. But containing costs can be a huge challenge. According to Gartner, by next year 35% of companies will have appointed a chief AI officer, a big indication that serious resources are going into AI. Another study, this one from Boston Consulting Group, projects that spending on GenAI will grow 60% over the next three years, accounting for more than 7.5% of the average corporate IT budget. Spending on AI systems looks a lot like spending on earlier-generation Software as a Service (SaaS) products. One recent report concluded that the average organization has 269 SaaS applications. For large organizations, there can be two or three times this many applications in place. Of these, the same report finds, only 17% are managed by a central operation, such as the IT department. Further, a typical company adds six new SaaS applications every 30 days (and we’d be willing to bet they are not retiring an equal number). For a company with 10,000-plus employees, the average IT spend on SaaS products is $264 million! Shadow IT and app proliferation Often called “shadow IT,” app proliferation creates noticeable operational problems. The budget for these things is not transparent; typically they are lumped together in an overall spending line and nobody knows whether the software is working, whether anybody is using it, whether it’s a duplicate (with a duplicate payment), or whether it’s producing desired benefits. The spending is bad enough, but the impact on the employee experience is even worse—a point made by Tiffani Bova. A 2021 study (conducted even before the current AI buzz) found the following: 69% of respondents reported that finding the information they need to do their jobs is difficult. 54% said that the applications used to access information made the work harder, not easier. 49% were worried that information would get lost. 43% reported spending too much time switching between different online tools. Strategic use of AI Doing a lot of experimentation with AI is great, in my view. But experiments and tinkering, while often useful, aren’t going to yield a corporate-wide competitive advantage unless their results are discovered and scaled. So this is probably a good time to consider whether your AI efforts are set up for strategic success. Consider the questions below (devised in collaboration with my colleague Kes Sampanthar): YesNoWe understand exactly how AI is going to improve a key metric in our business (no “black box” claims). We understand exactly how AI projects fit into our overall Innovation Strategy/Portfolio. We have clearly identified the specific business problem we seek to solve, then used the appropriate AI to achieve that result. We have a forum for regularly communicating what we are learning about the uses of AI across our organization. We are providing licenses and training to critical numbers of people across our organization who will benefit from understanding AI. We understand how AI will help us get information about key changes in our external environment and what we should do about them. We understand how AI will help us improve the employee experience. We have visibility into what AI projects are in the works and what their results are. We have created a governance board of business leaders and AI experts who can evaluate how projects map to market and technical uncertainties. We have confidence that people in strategic decision-making roles understand how AI will affect our business. More than four no answers suggests that you could be better leveraging your approach to AI for strategic benefit. A Case Study One of the companies I work with suspected they were dealing with AI proliferation. They had several concerns. Projects were being justified as “AI” projects when the real intention was to find budget for something else. Business leaders were running their own AI experiments, often without capturing what they learned or sharing it with others. There was no visibility into all the AI projects and no governance to reduce risk and minimize redundancy. Specialist groups of AI “experts” and consultants were getting contracts to launch isolated AI studies and projects. And company leadership had made a major investment in a group with a specific company-wide AI mandate that was not necessarily supported by the additional spend. None of this (well, almost none) is badly intentioned. It’s natural for people with resources to want to use them to learn
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The conversation around AI today reminds me of the dot.com conversations we were having in the late ’90s, or the direct-to-consumer conversations we were having in the 2010s. And we’re seeing similar results, too: a proliferation of projects that nobody in the organization truly has their arms around.
In some ways, that’s great—after all, innovation is a numbers game. Statistically, the more you try the more likely you are to find something that will be valuable. But containing costs can be a huge challenge.
According to Gartner, by next year 35% of companies will have appointed a chief AI officer, a big indication that serious resources are going into AI. Another study, this one from Boston Consulting Group, projects that spending on GenAI will grow 60% over the next three years, accounting for more than 7.5% of the average corporate IT budget.
Spending on AI systems looks a lot like spending on earlier-generation Software as a Service (SaaS) products. One recent report concluded that the average organization has 269 SaaS applications. For large organizations, there can be two or three times this many applications in place. Of these, the same report finds, only 17% are managed by a central operation, such as the IT department. Further, a typical company adds six new SaaS applications every 30 days (and we’d be willing to bet they are not retiring an equal number). For a company with 10,000-plus employees, the average IT spend on SaaS products is $264 million!
Shadow IT and app proliferation
Often called “shadow IT,” app proliferation creates noticeable operational problems. The budget for these things is not transparent; typically they are lumped together in an overall spending line and nobody knows whether the software is working, whether anybody is using it, whether it’s a duplicate (with a duplicate payment), or whether it’s producing desired benefits.
The spending is bad enough, but the impact on the employee experience is even worse—a point made by Tiffani Bova. A 2021 study (conducted even before the current AI buzz) found the following:
- 69% of respondents reported that finding the information they need to do their jobs is difficult.
- 54% said that the applications used to access information made the work harder, not easier.
- 49% were worried that information would get lost.
- 43% reported spending too much time switching between different online tools.
Strategic use of AI
Doing a lot of experimentation with AI is great, in my view. But experiments and tinkering, while often useful, aren’t going to yield a corporate-wide competitive advantage unless their results are discovered and scaled. So this is probably a good time to consider whether your AI efforts are set up for strategic success. Consider the questions below (devised in collaboration with my colleague Kes Sampanthar):
| Yes | No | |
| We understand exactly how AI is going to improve a key metric in our business (no “black box” claims). | ||
| We understand exactly how AI projects fit into our overall Innovation Strategy/Portfolio. | ||
| We have clearly identified the specific business problem we seek to solve, then used the appropriate AI to achieve that result. | ||
| We have a forum for regularly communicating what we are learning about the uses of AI across our organization. | ||
| We are providing licenses and training to critical numbers of people across our organization who will benefit from understanding AI. | ||
| We understand how AI will help us get information about key changes in our external environment and what we should do about them. | ||
| We understand how AI will help us improve the employee experience. | ||
| We have visibility into what AI projects are in the works and what their results are. | ||
| We have created a governance board of business leaders and AI experts who can evaluate how projects map to market and technical uncertainties. | ||
| We have confidence that people in strategic decision-making roles understand how AI will affect our business. |
More than four no answers suggests that you could be better leveraging your approach to AI for strategic benefit.
A Case Study
One of the companies I work with suspected they were dealing with AI proliferation. They had several concerns. Projects were being justified as “AI” projects when the real intention was to find budget for something else. Business leaders were running their own AI experiments, often without capturing what they learned or sharing it with others. There was no visibility into all the AI projects and no governance to reduce risk and minimize redundancy. Specialist groups of AI “experts” and consultants were getting contracts to launch isolated AI studies and projects. And company leadership had made a major investment in a group with a specific company-wide AI mandate that was not necessarily supported by the additional spend.
None of this (well, almost none) is badly intentioned. It’s natural for people with resources to want to use them to learn and explore. However, to truly benefit from the spending, there must be visibility into the portfolio of projects, how they align with the business strategy, and what is being learned from these efforts. Otherwise, the knowledge remains in individual heads.
As Arie de Geus pointed out decades ago, for an innovation to become a corporate asset, three elements are needed: Individuals need to be inventing new capabilities or skills, they need to move around and travel in “flocks” rather than remain in individual territories, and they must have a way of transmitting the new knowledge to others directly.
Recommendations
The challenge is how to get the benefits from all the experiments while not wasting too much time and money, and without exerting heavy-handed central control. Here are my suggestions:
- Identify the key people in the AI ecosystem in the firm.
- Encourage them to get to know one another and share what they are doing.
- Capture some basic information about their projects in a common database.
- Analyze the patterns.
- Provide a light, agile governance to ensure that innovation isn’t stifled but that projects are aligned to strategy.
- Share suggestions with the community and executive leadership.
The goal is to maximize the beneficial learning from investments in AI projects while at the same time making sure the company gets the greatest benefit from them. I am a huge fan of bottom-up tinkering with innovation, but when it starts to become chaotic, there is benefit to creating a modest amount of order.
