The single most critical and most neglected aspect of artificial intelligence (AI) projects is problem definition. All too often, teams start with data, determine what kind of machine learning (ML)/AI insights they can generate, and then go off to find someone in the business who can benefit from it. The result? Lots of successful AI pilots that can’t make it into production, and they don’t end up providing viable and positive business outcomes.
It’s estimated that 97% of enterprises have invested in AI, but is it really serving the business?1
Gartner’s 2019 CIO survey points to the fact that, although 86% of respondents indicate that they either have AI on their radar or have initiated projects, only 4% of projects have actually been deployed.2
Susan Athey, Economics of Technology Professor at Stanford Graduate School of Business, calls out the gap between ambition and execution when it comes to AI projects: “Only one in 20 companies has extensively incorporated AI in offerings or processes. Across all organizations, only 14% of respondents believe that AI is currently having a large effect on their organization’s offerings.”3
So what’s the problem? For one thing, many AI projects are technology-led, focusing on algorithms or tools that teams are familiar with. Others start with whatever data the team happens to have available. But data is frequently siloed and difficult to access, so is it the right and relevant data? While it’s true that data, tools, and algorithms are vital for the success of AI projects, putting the focus on the technical aspects is risky. Combining readily available data with known tools and algorithms is certainly likely to produce an AI-driven result more quickly—but there’s no guarantee it will have business value.
There’s a better way. Though it may sound counter-intuitive, AI teams need to work backwards to get their projects into business production. In other words, they need to pinpoint where they want to end up and then figure out how to get there. For a more practical and rewarding payoff, they need to focus on decision-making and on what a better decision looks. By collaborating with business units to define the decision-making that needs to be improved, identifying the kinds of ML/AI that would really help, and only then going to look for data, AI project teams will drive true business value.
So how does your team step out of its comfort zone and learn to work backwards? Advisory Data Scientist at IBM Aakanksha Joshi and Decision Management Solutions CEO James Taylor will show you how to achieve success with your next AI project. They will be offering five lightning rounds at the IBM Digital Developer Conference, where you’ll gain data and AI skills from IBM experts, partners, and the worldwide community. You’ll have the opportunity to participate in hands-on experiences, hear IBM client stories, learn best practices, and more.
Data & AI 2021
June 8, 2021 | 24-hour conference begins: 10:00 am AEST
Free and on demand
We look forward to seeing you there!
1 Building the AI Powered Organization, HBR July-2019
2 2019 CIO Survey: CIOs Have Awoken to the Importance of AI
3 MIT Sloan Management Review September 06, 2017
