We’ve curated a selection of interesting perspectives from the latest guests on AI at Work. On Episode 34, you’ll hear a roundup of insights on frequently asked questions about the timing and first steps of getting started with AI as well as other useful recommendations that companies can use to strategically leverage technologies, priming themselves to move ahead quickly.
#1: Get smart about when to partner and when to keep things in house
Jeetu Patel, Chief Product Officer at Box, says that for anything that’s an essential component but not core to your differentiation, “go to someone whose core business it is to do that thing and partner with them.”
As an example, he says that for a financial services company, it would be better to partner with someone like Box, rather than trying to reinvent the wheel by building out an entire content management system themselves. Focus on your core and partner for the rest, Jeetu sums up.
#2: Clearly identify the problem first before jumping to solutions
Start with the problem and work backwards. That’s the advice that Vishal Sunak, CEO and Co-Founder of LinkSquares, shares with us. By breaking down the problem into smaller components, you can begin to evaluate what available technologies might help you solve it.
Jeetu shares a similar view - once you’ve identified the problem, verify whether this is something people will pay to have solved. The goal, he emphasizes, is to build “painkillers, not vitamins,” meaning that people will move over when there is a ten-fold improvement over the current process, not just a 20% one.
Thinking clearly about AI comes from first-hand experience, says James Cham of Bloomberg Beta. Starting small, but model- and data-centric, will allow companies to start to get their feet wet. What this looks like is identifying small business processes that can be replaced with models and experimenting with what effect that has.
Gabi Zijderveld, CMO of Affectiva, recommends talking to people in your network to find out what experience they have working with and deploying AI - what solutions are they already getting value out of? Looking at applied AI solutions and talking to their customer base can give many insights.
#3: Don’t “wait and see” when it comes to AI
In the past, waiting to adopt a new technology has often been advantageous - leaving room for bugs to be worked out and no clear disadvantages to giving it some time. AI is different. James Cham says that there’s a realization that people who figure out how to apply AI - and sooner - will win. Specifically, he says that companies who are smart and aggressive about differentiating when to automate and when not to will be the ones to emerge as leaders.
Vishal Sunak puts it this way: When thinking about whether or not to adopt AI, it is helpful to ask the following questions - how big is your pain, and how expensive is it to let your problem go unsolved? No technology can solve every problem, but 90% is an amazing efficiency. If you have the pain point, the time to adopt AI is now, says Vishal.
#4: Use automation to create new types of roles, not eliminate them
For LinkSquares, automation around contracts makes legal teams within companies much more efficient. However, as Vishal explains, that doesn’t translate to positions getting cut. Rather than cutting three positions, he points out that AI makes existing employees more efficient. Some of their customers have even created dedicated positions for running LinkSquares instances.
James Cham shares a business puzzle that’s on the other end of the spectrum. People who work on the ground are the ones who know best where automation can happen. Yet there’s a clear disincentive to share that information, as that very process of automation potentially runs the risk of displacing the jobs of those individuals. It’s an interesting challenge that companies will have to innovate around in the future as more automation inevitably takes place.
#5: Clearly communicate the value prop of AI
Gabi Zijderveld, CMO of Affectiva, shares about the importance of being able to clearly communicate the value proposition of your innovation to others. “Our best practices are simplicity, clarity, and lots of examples of the “so what,” she says. The features of a product may be great, but so what? It’s essential to be able to demonstrate how these features will help make products better, generate revenue, and keep clients happy. It’s all about the value proposition, she says.
Becoming very clear on what you can partner on and what you should keep in house, communicating your value proposition as simply and clearly as possible, and breaking down problems in order to see where AI can come in are among the key points for companies interested in AI to keep in mind. We hope that some of these “how-to” tips we’ve gathered from AI leaders will help inform your journey to learn more about AI and get started in bringing it to your company.