AI continues to grow exponentially as a field. If you’re a business leader who wants to jump onto this quickly moving train - use this guide as a resource to help get you started.
We’ve spoken with several AI experts and industry leaders to bring you advice on how to incorporate AI into your business, ranging from tips on how to take the first steps, to setting up a team structure which supports success. For a closer look, head over to AI at Work, our podcast featuring full-length interviews with leaders and domain experts in AI. Here's a round-up of the top 5 tips so far.
1. Build a foundation of knowledge
AI is a rapidly evolving technology - those who do not adopt it may be left behind. Tom Davenport, distinguished professor of information technology and management at Babson College, shares his thoughts on AI-driven leadership: “The likelihood that your senior executives are going to embrace it and act on it is probably the single most important factor in how quickly you get moving.” The biggest obstacle? Understanding it.
Katherine Gorman, the host of Talking Machines, says that “it feels like magic, but it's absolutely not,” and the best way to get started is to “always be asking the question, how does it work?” and find the person on the team who can explain it to you. Gorman suggests “starting with immersing yourself in the language and being able to parse the big terms in a precise way that is informed by the technicality of it, but it doesn't have to be 100%. Knowing the difference between machine learning and deep learning. Knowing about artificial intelligence in general.”
In the same vein, Jana Eggers, CEO of Nara Logics, advises CEOs to “understand it to a level enough that it will be beneficial to their business. It's not something that I think they should say, “Oh, that's my tech and that's my smart guys.” It's everyone. Don't put it off in a corner.”
Beyond an understanding of the basics, Davenport also emphasizes clarity. “You need to be clear about what you want to accomplish with AI in your business,” he explains.
HubSpot’s former Chief People Officer and investor Jim O’Neill recommends foundational books such as The Master Algorithm and Life 3.0, as well as narrowing on a couple of thought leaders in this space as a starting point for “mere mortals” to build a foundation in AI knowledge.
2. Four steps to get started
In her article - Four Tips to Stop Reading About AI and Start Doing AI - Jana Eggers shares the following: 1) understand that AI is not magic, 2) get serious about your digital transformation - understand what data you have, the quality of it, and how to use it across your organization, 3) experts are important, but more importantly, understand what your organization is trying to accomplish and build around that, and 4) build a learning organization, where “It's not about failing. It's about learning.” She recommends the book The Fifth Discipline as a resource for becoming a learning organization.
Eggers also elaborates on the “holy trinity” of AI: data, algorithms, and results. She recommends starting with your data, iteratively trying out algorithms, and as an executive, understanding the results you’re trying to drive.
3. Forget the moonshot, start with small goals
One of the biggest barriers to starting something is thinking that there has to be a huge leap right away. Incorporating AI into your organization doesn’t have to be a fundamental revolution, all at once - you can start with small, actionable steps.
Media gives a lot of attention to huge breakthroughs, but as Tom Davenport explains, “what's not as widely publicized is the low-hanging fruit, every day-- make this decision a little better; make this process more efficient and effective. [AI is] good at that. In fact, I think that's really the only thing that it's good for just because it tends to be very task-based and not entire process or even job based.” These day-to-day, invisible operational improvements, Davenport goes on to say, will over time “add up to a huge amount of improvement in productivity and business capability. But we somehow only want to look at the moon shots.”
Steven Peltzman, Chief Business Technology Officer at Forrester Research, reaffirms this view, stating “maybe you don't go after the big change. Maybe you try and find a pocket, and then work that into it. Work the process, figure out how to do what you just described. I'm not really sure, but we were talking about how everyone's going at the big things, and they should be going at the smaller things.”
4. Building successful AI teams
Integrating AI into your workflow means scouting for talent and figuring out where and how a data science team fits into your company. Brendan Kohler, co-founder and CTO at Sentenai and co-founder at Hyperplane Venture Capital, shares that from his experience, incorporating smaller data science teams into different parts of the organization typically has more success than a centralized core team.
The dynamic within a team is also very important. David Murgatroyd, machine learning lead at Spotify, advises product managers to allow the data scientists and machine learning folks to be the domain experts, while setting a clear goal of “here's how I'd like the behavior of this product to change. And I want it, for these kinds of input, to have this different behavior. And here's a metric by which you could be able to quantify that.”
Kohler also shares advice for how larger organizations can work with smaller AI companies. He points out that the most common mistake he observes in this type of interaction is that smaller companies tend to “assume that they understand the complexities of the business they're trying to help.” The root of the problem is being able to recognize within a larger company, “what problems are actually solvable and what problems boil down to issues of companies structure or processes.” Therefore, he advises AI companies to “engage with companies that are willing to invest the time and resources, learning and bringing these smaller AI-focused companies into the fold.”
5. How to support employees through automation
So far, we’ve been speaking more about advice for executives. Erin Winnick of MIT Tech Review shares a very different perspective: how AI-driven automation affects front-line workers. Here’s some advice she shares for workers: “even if the company isn't supporting you to try to retrain yourself, you need to take it on yourself a little bit to be able to do that. I think, personality-trait wise, self-starters, people that are willing to look at the landscape of the industry and see, these are the skills I need, even if my company isn't necessarily providing the support-- in my free time, I need to try to do those things.”
Winnick also considers how those people might “still have a role in helping facilitate some of this [automation] and work with the companies to better optimize these AI processes and things like that.” As a company, providing retraining opportunities for employees are considerations to help alleviate the pressures of potential job-loss to AI & automation.
We hope these expert opinions will help you get started in leveraging AI technologies across your organization. Start by gaining an understanding of AI and basic terminology, focusing on small steps instead of a grandiose moonshot, starting with the data you already have, and understanding what outcome you’re trying to achieve when building a data science team. There are so many exciting opportunities in the AI space: from life sciences applications, to hardware advances, to the lowest-hanging fruit of enterprise and industrial spaces with decades of data that’s not currently being utilized. What are you waiting for? Jump in.