Part 1 of 3: Understanding AI’s Application in Industry – Today’s Landscape
According to Gartner, “AI adoption in organizations has tripled in the past year, and AI is a top priority for CIOs.” A muscular push toward industry and commercialization. The market has proved that techniques in reinforcement and supervised learning work; we’ve seen their potential applications in financial services, healthcare and marketing. Now the real legwork begins. Every Fortune 1000 company that wants to remain on the list is currently rolling up their cuffed sleeves and figuring out how to best integrate machine learning solutions into product roadmaps or within internal systems.
Of course, any enterprise worth its salt knows what’s involved in applying new technologies to both commercial products and proprietary systems. But AI is not just a new technology; it’s a field of scientific research, the complexity of which demands an unprecedented level of expertise that’s very difficult to simply “pick up on the job”. Depending on the scale of your AI ambitions, this could mean investing in a robust in-house R&D team trained at top universities, or simply hiring a good consulting firm to help scope, then build potential projects.
There’s no time to lose. While still in its early days, industrial AI is proliferating at exponential rates and projects market estimates of anywhere between $2 trillion to $4 trillion by 2022. Innovators like JP Morgan Chase and Accenture have already built major AI research labs headed by leading academics to reshape everything from equities trading to client services. Fintech start-ups with access to third-party data sets are nimble enough to innovate and disrupt everything from payment systems to open banking. Simply put, if you’re still thinking about AI it’s past time to get started.
The challenges involved in “doing AI” can seem insurmountable, but they don’t have to be. There are ready-to-use tools available to help solve the vanilla problems of the world and, while they won’t necessarily propel you to the head of the pack, they can help optimize operations to make exponentially better use of data in order to generate leads, drive customer loyalty and consider new business lines. In short order, these off-the-shelf frameworks will become as ubiquitous to any competitive organization as email.
Complexities escalate around unique, real-world business applications for machine learning and AI. Next week, we will dive deeper to understand the difference between “off the shelf” AI and the application of customized solutions. Stay tuned!