Whose Job is AI
It’s common for management teams to assume that data scientists inherently know which problems to solve for the company. However, this bottom-up approach to AI rarely leads to meaningful results. While data scientists and ML engineers can identify AI opportunities and initiate exciting projects, many of these initiatives are more suited for research publication than for generating business value. Relying solely on this bottom-up method for discovering AI opportunities is risky.
- Limited Business Insight: Data scientists new to a company may not fully understand the business challenges. Their data exploration might not reveal inefficiencies in daily processes and workflows, making it difficult to propose impactful AI solutions.
- Need for Extended Business Exposure: Without significant exposure to the business, data scientists are less likely to identify and solve real problems that the company faces.
- Difficulty in Gaining Management Buy-In: Data scientists, who are often focused on technical aspects, may **struggle with company politics **and lack the authority or inclination to engage with upper management to get support for AI projects.
- Budgetary Oversight: Unaware of the company's financial constraints, data scientists might initiate AI pilots that are too costly or risky, leading to potential cancellation or postponement by management due to inadequate cost justification.
- Risk of Poor Outcomes: A bottom-up approach can result in the organization not recognizing the value of AI, leading to the possibility of abandoning AI pursuits and reassigning or releasing data scientists.
‼️A Better Approach: Leadership-Driven AI Strategy
- Leaders as AI Opportunity Identifiers: Instead of leaving AI to technical teams, leaders and domain experts can develop the skills to spot promising AI opportunities that align with business goals.
- Technical Teams as Enablers: Technical teams should inform leaders about AI feasibility and assist in executing the vision, ensuring that AI initiatives are grounded in reality and business needs.
- Maximizing Results: By adopting a leadership-driven approach rather than a bottom-up method, organizations can focus on AI projects that are purposeful and have a high impact.
two ways you can find AI opportunities that are aligned with the business:
- “organic discovery” 原汁原味的AI
The first starts from a new business problem you’re looking to address. The breakdown of the problem reveals that parts of it can benefit from AI. As this happens organically, I’ll refer to this as the “organic discovery” of AI opportunities.
[新问题剖析以挖掘AI应用] the need for AI often surfaces as projects are fleshed out and broken down into subproblems. While, for some problems, it could be immediately apparent that they could benefit from AI, for others, it can be more obscure. - “proactive discovery” 老问题的AI赋能
The second approach is to actively investigate existing processes, customer pain points, and legacy systems in the organization with the goal of finding opportunities that would benefit from AI. I’ll refer to this as the “proactive discovery” of AI opportunities.
Most beneficial scenarios to apply "proactive discovery":