Kartik Hosanagar, Founder/CEO at Stealth Media/Data Start-Up
One pitfall companies often encounter in the process of starting new AI initiatives is that the excitement around AI might lead to AI being viewed as a goal in and of itself. But executives should be cautious about developing a strategy specifically for AI, and instead focus on the role AI can play in supporting the broader strategy of the company. In short, view AI as a tool and find AI applications that are particularly well-matched with business strategy.
A related comment is that I wouldn’t recommend that companies pool all their AI resources into a single, large, moon-shot project when they’re first getting started. Rather, I advocate taking a portfolio approach to AI projects that includes both quick wins and long-term projects. This approach will allow companies to gain experience with AI and build consensus internally, which can then support the success of larger, more strategic, and transformative projects later down the line.
A second area is reskilling. The skills needed for AI projects are unlikely to exist in sufficient numbers in most companies, making reskilling particularly important. Focusing on growing the talent base is crucial given that most engineers in a company would have been trained in computer science before the recent interest in machine learning. In addition to developing engineering talent, an equally important area is that of consuming AI technologies. Managers, in particular, need to have the skills to consult AI tools and act on recommendations or insights from these tools.
Remember AI is not the goal. View AI as a tool and find applications that are particularly well-matched with business strategy.
Organizations can increase the odds of AI success thinking through the following interrelated dimensions of business, data, people, process, and technology.
Business: Pick the right set of use cases based on the alignment between business needs, AI technology maturity, and organizational capabilities.
Data: Having the right data is at the heart of AI success. Most AI projects fail because there is no data available or the available data is of low-quality.
People: AI talent, particularly expertise in real-world at-scale AI deployments, is in short supply. Organizations need to build both an in-house AI Centre of Excellence and also partner with external specialists.
Process: AI projects are different from IT projects and you need to accordingly adapt your process, methodology, and governance frameworks. Don’t forget to analyze and mitigate any unintentional harm caused by the AI solution.
Technology: There’s no best AI platform but there’s the right AI platform for your organization’s needs. And don’t forget that you’ll need MLOps tools/capabilities too.
AI projects are different from IT projects. You need to adapt your process, methodology, and governance frameworks appropriately.