Contributed ArticleThere is huge enterprise-level interest in artificial intelligence projects and their potential to fundamentally change the dynamics of business value. However, most artificial intelligence technologies are nascent at best. According to a recent Gartner survey, 37% of organisations are still looking to define their artificial intelligence strategies, while 35% are struggling to identify suitable use cases.
The mindset shift required for artificial intelligence 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 artificial intelligence projects, CIOs must put forward a solid business case.
Part of the issue is that there is no such thing as an artificial intelligence business case. Instead, the business case will be for a particular business scenario, problem or use case that employs artificial intelligence methods and techniques as part of the overall solution.
Focus on answering these four questions when you want to define an artificial intelligence project:
#1 Why are you doing this project?
#2 For whom are you trying to deliver this solution?
#3 What solution and technology framework will you employ?
#4 How will you deliver this project?
Business cases for artificial intelligence projects are complex to develop as the costs and benefits are harder to predict than for most other IT projects. Challenges particular to artificial intelligence projects include additional layers of complexity, opaqueness and unpredictability that just are not found in other standard technology.
To build a successful business case for artificial intelligence projects, CIOs need to articulate and address the specific factors around how artificial intelligence projects differ from other IT solutions.
# Costly without providing immediate gain
Building a business case includes analysing the expected benefits and costs associated with a project. However, in the case of artificial intelligence, the answer is unlikely to be straightforward. Artificial intelligence projects can appear costly without any immediate gains — particularly for loosely bound scenarios and in organisations that are not used to setting aside budgets to develop and deploy solutions for new business scenarios.
The return values from the project are closely intertwined with the aspirational value that the organisation is seeking. Past examples of significant and successful investments in artificial intelligence show that organisations ahead of the curve in digital transformation have an advantage with artificial intelligence. Organisations must have a serious strategy around investment in artificial intelligence 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 is no accident that companies now reaping the benefits of artificial intelligence invested long before their competitors.
An adaptive approach is required here. Do not be afraid to be upfront about expected costs and set expectations that they might change significantly as the solution scope is explored and refined. By the same token, there also needs to be readiness to close down experimental artificial intelligence projects where no clear benefit is emerging from the early stages.
# Substantial cultural change
For most enterprises, the mindset shift required for artificial intelligence 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.
# Different technology and skills
The biggest pain point that emerged from Gartner’s 2018 CIO survey was the lack of specialised skills in artificial intelligence, with 47% of CIOs reporting that they needed new skills for artificial intelligence projects. As such, talent acquisition is likely to be one of the biggest barriers to artificial intelligence 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.
Key takeaways
- There is no such thing as an artificial intelligence business case.
- The mindset shift required for artificial intelligence can lead to cultural anxiety.
- Challenges particular to artificial intelligence projects include layers of complexity, opaqueness and unpredictability.
- 37% of organisations are still looking to define their artificial intelligence strategies, while 35% are struggling to identify suitable use cases.
- The business case will be for a particular business scenario that employs artificial intelligence methods and techniques as part of the overall solution.
- Business cases for artificial intelligence projects are complex to develop as the costs and benefits are harder to predict than for most other IT projects.
- To build a business case CIOs need to articulate he specific factors around how artificial intelligence projects differ from other IT solutions.
