Non-Tech Firms on AI
Brief thoughts on AI in Industry
April 9, 2019
Non-technology businesses are increasingly embracing the role of automation (seen below with Walmart) in their operations. Be it physical tasks or those that are “softer” such as writing an analysis or evaluating hundreds of credit applications, AI is at the center of the future. In fact, just this week, technology bellwethers Apple hired Google’s Head of AI and Microsoft emphasizing AI in a recent reorganization. So what’s happening in the non-tech world?
Well, a recent survey of non-tech companies by MIT Sloan Management Review and BCG reveal the belief that AI will drive competitive advantages with a minority using it today. Industries and companies that naturally generate and warehouse scores of data have shown the tendency to jump ahead of the pack. Recent conversations with a Fortune 500 company also revealed the willingness to invest in outside vendors (namely startups) either strategically or through sales commitments to capture the benefits (and shape products) ahead of the pack. Another is trying to understand with the path to ROI by experimenting with solutions tied to their existing suite of software solutions (SAP and Salesforce). A third conversation shed light on internal efforts to solve relatively simple problems using ML in an effort to be more well-versed and educated during vendor selection. Betting on timing tends to be a fool’s errand where C-suites are trying to balance themselves between over/under commitment to a nascent technology — corporate labs, startup partnerships, third-party consultant contracts, pilots/trials, corporate venturing, and more.
For a corporate customer, the ramp to valuable AI is steep and requires thorough diligence (cause of all the buzz/hype) and patience (it can take months if not years to go from vendor selection, data prep, algorithm development, and on-going monitoring to value). I suspect that much of this benefit around automation, be it robotic-centric or software-centric, to require specialization. This has resulted in an explosion of “AI-driven” startups but current architecture and practices behind valuable models has one common thing — specialization. As a result, on the vendor side, companies that are able to aggregate and offer enough of these modules or have the best modules (measured by ROI) can be deemed near-term winners. On the buyer side, those institutions that find a model to experiment, learn, and decide will quickly prove to find the right cocktail of AI solutions that drive value to their customers, gain operational leverage, and grow in size.
Or better put — “A longer-term concern is the way AI creates a virtuous circle or “flywheel” effect, allowing companies that embrace it to operate more efficiently, generate more data, improve their services, attract more customers and offer lower prices. That sounds like a good thing, but it could also lead to more corporate concentration and monopoly power — as has already happened in the technology sector.”