Many companies nowadays feel the urge to develop an artificial intelligence (AI) strategy. However, they often get caught in a dilemma: How to hire the talent and identify the opportunities?
From my experience, to test the AI waters, it is not necessary to hire new, fully trained talent from the start. It might be better to work with in-house experts or, if needed, with a specialized consulting company to seize the new opportunities and to determine which projects are feasible and what new resources would be required. Identifying the possible areas of improvement within the company, that is, the benefits AI could bring, has priority over the full hiring step.
Hiring is a complex process, and hiring decisions often have long-term consequences. Before proceeding, the company must be comfortable with the level of innovation that is to be introduced.
Identifying opportunities for AI
The first step in AI adoption is identifying the initial data science application to implement: The starter project. Its success or failure and the effort required will give management the information it needs to decide whether and how to proceed with introducing more AI-guided applications. This project will signal the turning point for the AI adoption process.
So, how can we choose it? Among the many possible AI projects, how can we identify which use case brings the most benefits with the smallest investment in time and resources?
This can become an overwhelming step in the AI adoption process. There may be ideas all over the company about what use cases might benefit from AI; however, often a clear understanding is missing about the difficulty of the implementation and about the consequent benefits. An idea might sound brilliant, but the technical counterpart looks overly difficult to non-technical eyes, enough to curtail all enthusiasm. On the other hand, an idea might sound trivial to the technicians but bring benefits only to a niche area of the business. There are also ideas whose implementation would be prohibitive for the company size and budget. And so on. It can be discouraging to try to orient oneself amid the jungle of possible AI use cases.
Luckily, there are guidelines. Best practices in this area, like the ones described in “How to Identify the Best AI Opportunities for Your Business,” often involve two steps:
- Identifying within the company a number of possible AI use cases
- Selecting the top priority use cases in terms of budget, technical skills and benefits
Holding a “Dream and Evaluate” workshop
The rules are out there. So, what’s stopping us?
Even though we know what is important when listing AI projects and defining priorities, that does not translate into a straightforward sequence of steps. Often, we need the help of our technical employees to properly evaluate the difficulty of each idea, as well as assistance from business experts to quantify its benefits. Again, we are stalling.
When I end up in this kind of situation, I resolve to hold a short brainstorming workshop, inviting people across departments from both sides of the company: the technical employees and the business experts.
The workshop is usually divided into two sessions:
- Wild dreams: The first session covers the creative part of the workshop; it is there to stimulate ideas no matter how crazy. Every participant must come with an open mind and contribute at least one AI project idea. No judgment is allowed. In this session, we are free to dream. All objections such as “But we do not have the talent on staff?” or “But this is too hard to do…” must be stopped, as they do not belong here. During this session, imagination can and must run wild.
- Coming down to reality: The second session brings us back to reality. Here we stop dreaming and evaluate all ideas from the first session in terms of feasibility, budget and return on investment.
While the previous session was the place for creative skills to shine, this session is the kingdom of caution. Usually, after going through each idea from the previous session, after stating its costs and benefits as well as feasibility, a very clear list of prioritized projects emerges.
Making the cut
At the top of the list, we will find the low-hanging fruits: Those projects which require little work, little budget and often only in-house skills for some valuable returns. Then, progressing through the list, we will find more complex projects, likely bringing more returns on investment and sometimes requiring external data skills or even new hires.
Where to draw the line for the next projects to implement depends on management expectations. For a proof of concept, we will stop at the first one or two projects from the list. For a wider breadth of innovation, we might consider a few more projects down the list. If the introduction of AI has already been approved and is strongly supported by upper management, then we could even start one of the more complex projects, possibly requiring new hires.
After this “Dream and Evaluate” workshop, goals can become clear and trigger the rollout of the starter projects.
The workshop duration varies, depending on the size of the company and its AI ambitions; it may take a couple of days for bigger companies or just a day for smaller ones. However, at the end of the workshop, a very clear vision of AI opportunities emerges. This excites all participants, the dreamers as well as the cautious souls. For all of them, the path to AI adoption becomes clear, and its implementation can follow quite smoothly. The time investment is worth the result!
Rosaria Silipo is head of data science evangelism at KNIME.
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