AI: Now that you’re a believer, where do you start?
Over the past several months, we’ve talked extensively about how artificial intelligence (AI) is revolutionizing the enterprise—from helping to guide governance decisions, shifting to more informed decision making, turning chaotic data into actionable insights, enabling the overhaul of ineffective performance management systems, and more.
The potential is clear: When Deloitte surveyed AI early adopters, 64% reported that their investment in AI has allowed them to edge slightly ahead, widen a lead, or leapfrog ahead of their competitors.
As we discussed in our “AI in the boardroom” post, the opportunity AI presents—and the rate at which it’s gaining traction—can’t be overstated. But even though most executives recognize the importance of AI, developing and executing an AI strategy that will lead to value creation for their organization still remains out of reach for many.
So you’re ready to get on board with AI, but how? Where do you start?
It might seem obvious, but it’s worth repeating this important point noted by HR Technologist: “Companies will witness the highest return on their investment if they implement AI in areas where errors or problems tend to occur frequently.” In one of our most widely-shared posts we explained why AI must be laser-focused on purpose and outcome, seeking to answer those two key performance questions of “What do I need to know?” and “What do I need to do?”
After you have a good idea of the “what” and “why,” PC Magazine argues the next step is to “acknowledge the internal capability gap.” Recognizing what technology and business process capabilities your organization has—and is lacking—and determining what you can acquire or how you can internally evolve to address those holes is key.
Then it’s time to think about the “who.” As Gartner notes, “analytics requires a cross-functional team that blends domain expertise with data engineering and data science skills.” In most cases, this will mean identifying and training existing staff, hiring new workers, and bringing in consultants to fill in the gaps.
Once it’s time to start, start. It is prudent to start small, but don’t waste time waiting for perfection. In this fast-paced, constantly evolving business environment, with agility valued more than ever, a successful AI program requires a prototype-first approach. It will be important to quickly be able to prove value and collect feedback, and then you can consider how to scale or expand accordingly.
Every day more and more companies are dipping their toes into the AI water—if not jumping in completely. Some will drown, many will abandon ship when the seas start to get rough, and a few will make it safely to shore. If you need a life preserver, we can help. Let’s talk.
Jeff Loucks, Tom Davenport, and David Schatsky, “State of AI in the enterprise, 2ndedition,” Deloitte, October 22, 2018. https://www2.deloitte.com/insights/us/en/focus/cognitive-technologies/state-of-ai-and-intelligent-automation-in-business-survey.html?icid=dcom_promo_featured%7Cus;en
Marcus Mossberger, “Artificial intelligence: coming soon to an office near you. Or is it?” HR Technologist, August 24, 2018. https://www.hrtechnologist.com/articles/digital-transformation/artificial-intelligence-coming-soon-to-an-office-near-you-or-is-it/?zd_source=hrt&zd_campaign=2657&zd_term=chiradeepbasumallick#
Rob Marvin and Brian T. Horowitz, “10 steps to adopting artificial intelligence in your business,” PC Magazine, November 12, 2018. https://www.pcmag.com/article/350965/10-steps-to-adopting-artificial-intelligence-in-your-business
Kurt Schlegel, Valerie Logan, and Melissa Davis, “Toolkit: How to get started with analytics,” Gartner, December 3, 2018.