Data science, machine learning and artificial intelligence are key enabling capabilities in today’s business world but few executives know how to deploy them effectively.
According to a new Forrester report, “The Tech Executive’s Primer On Data Science, Machine Learning, And AI, a lack of understanding is hampering the ability of business leaders to effectively deploy data science, machine learning and artificial intelligence projects to solve business problems. “All executives need to make strategic decisions about how and where to leverage these technologies, but few leaders have experience with them, so misconceptions abound, causing poor outcomes, wasted resources and resistance to future initiatives,” the report said.
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The report defines data science as extracting meaning from data; machine learning as applying algorithms to data to train machine learning models; and artificial intelligence as an umbrella term for machine learning and automation methods used in new ways.
To successfully deploy these technologies requires business and technology acumen and executive leadership, the report said. While technical experts can be hired, finding business executives who understand these complex, cutting-edge technologies is much harder.
The report suggests seven insights and best practices businesses can deploy to tilt the odds in their favor:
1. If it looks like you think AI “should” look, it’s probably not. As smart as AI technologies like personal assistants or grammar-checkers appear, real-world AI does not exhibit anywhere near the intelligence and autonomy portrayed in the movies. “The actual advantages and disadvantages of ML and AI technologies vary so dramatically from popular perceptions that if an idea, proposed solution, or vendor offering looks like something a layperson would expect, it will be doomed to fail, is overly hyped or will have to rely on a person hiding behind a curtain,” the report said.
2. Look for projects that are technically feasible and provide measurable business. DSMLAI shouldn’t start with just the end in mind or with what the AI and ML technologies can do. You have to meet in the middle. “Start purely with the business value and you’ll choose use cases that play to AI’s weaknesses and miss its strengths (think fully autonomous vehicles). Start with the data and you’ll find true but worthless insights (e.g., bookings drive revenue),” the report said.
3. Take a lifecycle approach. No matter what you do, if users don’t care or won’t use it, it doesn’t matter. “Usually, that involves deploying your AI solution, getting it into the hands of end users and training folks. If you haven’t planned for how that will happen, be prepared for lengthy delays at best; at worst, you’ll discover that deployment is impossible. Increase your likelihood of success by planning your project from end to end and involving the solution’s intended end users from the start and throughout the process,” the report said.
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4. Improve your data over time. Don’t wait until you have the data just right to get started or you never will. “When it comes to AI projects, quality data is a myth. You won’t know what data you need and the form that you need it in until you know how you are going to use it—and vice versa. Instead, work with the data that you can get hold of rapidly, drive the value you can quickly and use the success to advocate for the next round of investment in your data assets and pipelines,” the report said.
5. Improve AI capabilities over time. Just like with data, most successful DSMLAI projects start small and build on successes to scale. “That often means buying horizontal or vertical point solutions with embedded AI capabilities first and then going beyond the capabilities of these solutions using custom models and applications,” the report said.
6. Worry about human bias first, then AI. Because AI is a tool developed by humans it will likely contain built-in biases. The best way to avoid bias is to carefully screen the data you use to train your AI models. “Above all, test multiple hypotheses, validate models, and monitor them over time for bias and, when applicable, fairness. If you do, your resulting models will almost certainly be less biased than human decisions. If you don’t, you risk reinforcing and proliferating bias,” the report said.
7. Do not let AI projects linger. Because they are poorly understood, implemented or abandoned by their executive sponsor, AI projects are subject to relegation. The best way to avoid this outcome is to kill them off sooner, rather than later. “Empower your teams to kill projects but capture the learnings and resurrect them in new, more viable incarnations,” the report said.