How To Build A Business Case For Artificial Intelligence

April 04, 2018

Contributor: Christy Pettey

CIOs must recognize that AI projects differ from most other IT projects and plan accordingly.

There is huge enterprise-level interest in artificial intelligence (AI) projects and their potential to fundamentally change the dynamics of business value. However, most AI technologies are nascent at best. According to a recent Gartner survey, 37% of organizations are still looking to define their AI strategies, while 35% are struggling to identify suitable use cases.

“ The mindset shift required for AI can lead to "cultural anxiety" because it calls for a deep change in behaviors and ways of thinking”

This is clearly problematic when, in order to secure the necessary investment for AI projects, CIOs must put forward a solid business case. Part of the issue is that there is no such thing as an AI business case. Instead, the business case will be for a particular business scenario, problem or use case that employs AI methods and techniques as part of the overall solution. Focus on answering these four questions when you want to define an AI project:

  • Why are you doing this project?
  • For whom are you trying to deliver this solution?
  • What solution and technology framework will you employ?
  • How will you deliver this project?

“Business cases for AI projects are complex to develop as the costs and benefits are harder to predict than for most other IT projects,” explains Moutusi Sau, principal research analyst at Gartner. “Challenges particular to AI projects include additional layers of complexity, opaqueness and unpredictability that just aren’t found in other standard technology.” To build a successful business case for AI projects, CIOs need to articulate and address the specific factors around how AI projects differ from other IT solutions.

AI solutions can appear costly without providing immediate gain

Building a business case includes analyzing the expected benefits and costs associated with a project. However, in the case of AI, the answer is unlikely to be straightforward. AI projects can appear costly without any immediate gains — particularly for loosely bound scenarios and in organizations that aren’t used to setting aside budget to develop and deploy solutions for new business scenarios.

The return values from the project are closely intertwined with the aspirational value that the organization is seeking. Past examples of significant and successful investments in AI show that organizations ahead of the curve in digital transformation have an advantage with AI. Organizations must have a serious strategy around investment in AI projects, along with strong management support. Amazon’s acquisition of Kiva Systems, for example, shows how the use of robots in its warehouse automation provided competitive advantage. It’s no accident that companies now reaping the benefits of AI invested long before their competitors.

“An adaptive approach is required here. Don’t be afraid to be upfront about expected costs and set expectations that they might change significantly as the solution scope is explored and refined,” says Sau. “By the same token, there also needs to be readiness to close down experimental AI projects where no clear benefit is emerging from the early stages.”

AI will need substantial cultural change

For most enterprises, the mindset shift required for AI can lead to "cultural anxiety" because it calls for a deep change in behaviors and ways of thinking. CIOs should acknowledge the cultural changes, be proactive in managing related challenges and build trust over time. Cultural change and successful transitions to new roles and practices are dependent on open dialogue and mutual respect among IT members and between management and staff.

AI projects require different technology and skills

The biggest pain point that emerged from Gartner’s 2018 CIO survey was the lack of specialized skills in AI, with 47% of CIOs reporting that they needed new skills for AI projects. As such, talent acquisition is likely to be one of the biggest barriers to AI adoption going forward.

While long-term strategies should include how to leverage academic communities and open-source technologies to ease the lack of resources, the immediate priority is working out what needs to happen now. Leveraging and training existing resources — particularly on data science tools — will be a key strategy. Lessons learned from initial pilots will also help CIOs decide to whether they will ultimately build, buy or outsource future projects.

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