AI solutions aren’t just a tool approach; it’s about well-understood use cases and ways to measure their impact
The world we are shaping around AI includes different types of organizations: those building sophisticated AI technologies, others developing AI-based solutions, and finally, organizations that aim to use AI for positive impact or to support their businesses. When initiatives to deploy AI in the last group take place, capacity building and training are mostly oriented towards technical infrastructure, data ecosystems, or technical skills; and although its significance is indisputable, we fail to understand that in most cases, AI solutions aren’t just a tool approach; it’s about well-understood use cases and ways to measure their impact. This guide aims to be useful to anyone leading AI initiatives and to complement any strategy aimed to enhance innovation capabilities through AI.
“AI is not about its capabilities and promises, but also about how its used…” (The age of AI: And our Human Future -Kissinger, Schmidt, Huttenlocher)
Every process of innovation through artificial intelligence consists of two parts: capturing knowledge and utilizing knowledge. This guide aims to demonstrate the strong relationship between both and the five dimensions that compose them (Use Cases, Early Wins, People, Technology, and Governance). Although they can independently coexist, together, they can significantly improve the chances of identifying and deploying AI-based solutions to make a substantial impact.
I would like to clarify the intended scope of this guide. There is a lot of good work on this topic by consulting firms (Deloitte, McKinsey, BCG, Gartner, to name a few) and companies in the private sector or independent research ( Catalyst Fund,Profit.co,Dorien Herremans , to name a few). Therefore, it’s not my intention to present another bespoke conceptual framework or reinvent the wheel. In fact, some of the steps presented may sound very familiar to anyone leading an AI practice in a B2B tech consulting company. My intention is to move away from the abstraction of a conceptual framework and attempt to operationalize a set of steps with some tools that can help companies significantly improve their chances of identifying and deploying AI-based solutions to make a substantial impact.
It’s not an AI tool approach; it’s all about USE CASES. This means that to increase our success rate on our AI project, we must identify real problems that affect our end users or the company we are working with. This really isn’t anything new, as most frameworks around AI strategy emphasize the importance of identifying good business cases as a starting point.
This part is what I call “capturing knowledge”, and although everyone recognizes it as an important step, there is little information about the “How?” to do it. For this guide, I divide this capturing knowledge step into two dimensions: The identifying process and the prioritization process, which specifies parameters to help select which use case could be more relevant to engage with, and achieve Early Wins.
How to identify good opportunities to deploy AI?
- Initiatives: What challenges does the industry you are in face?
- Use Cases: How is the company attempting to solve such challenges?
- Stakeholders: Which division/business unit does the challenge belong to? Who decides? Sponsors? Detractors?
- Insights: With what insights in the company are the challenges identified? Where do they come from?
- Data: What data do you have available to solve the challenge? Is it validated? Do you need more data?
- Tools: What tools (technology) does the company use to solve the challenge?
Every development follows an adoption curve; technology moves faster than the capacity of human beings to adopt it, and much faster than companies’ adaptation to this new customer behavior. This is kind of the essence of the “Collingridge Dilemma”, but it’s also relevant for understanding success in AI initiatives.
