Over 60% of the Fortune 500 companies are already employing AI to improve processes (2019 Fortune survey), and this trend is a rising one. The intent to adopt AI is widespread, no doubt. Still, successful digital transformation happens when you dig deep and get to the core of identifying the business problem to be solved. AI is not about solving broad spectrum problems; it requires a definitive problem statement. How does one arrive at this? Can one problem statement encompass all of the business problems? How does Fractal look past the haze to find value through AI for the client?
Bhaskar Roy from Fractal and Daniel Fagella from Emerj get into an exciting conversation to unravel how to find an AI project that matches the client’s actual goal. It involves understanding the client and their business better and then some more.
Some of the key differentiating factors to the process include,
- The business vision and its drivers
Every business has a vision, that’s driven by an ecosystem. Understanding that vision better gets one closer to understanding the gaps in the ecosystem. In some cases, AI may not even be the answer, while AI could be the only answer in others
- Key problem areas in achieving the goal
A problem statement clearly defines the current state of the issue and its relation to existing systems and processes. This leads to defining the multiple areas that can be improved using AI.
- The technical maturity of the company
An HBR article (https://hbr.org/2019/07/building-the-ai-powered-organization) states that only 8% of firms engage in core practices that support widespread AI adoption. An enterprise needs to possess a foundational understanding of AI to deploy it successfully. AI service providers need to gauge this technical maturity and may even have to educate the organization.
- The overall decision-making ecosystem and the different players involved
The whole idea of AI is to drive better decisions. That is why it is vital to understand the current decision ecosystem and the multiple internal and external players involved. AI needs to seamlessly merge into the recent decision ecosystem to enhance the process.
- Insights from internal AI and analytics team to build upon
Existing data and insights are an excellent place to begin. It helps in narrowing down on a problem
- Breaking down the larger AI project into smaller, unified ones
A smaller AI project can be sliced out of the more significant possible project to build trust and provide proof of concept. This sets things snowballing into achieving that big AI goal. When several such projects come together unified, they lead the business towards the intended goal.
Tune in to the podcast for more insights on deep diving beyond the initial brief to establish successful AI deployment.