The Challenge

This morning as you brewed your coffee-to-go, you might have asked Amazon’s Alexa what the weather is for today. Then as you prepare for your morning commute, you jumpstarted Google Maps to check the traffic situation. These artificial intelligence (AI)-led services, among others, are already permeating our lives, with many more business use cases under study and new technologies underway. 

As rapid advances in artificial intelligence begin to change industries, markets and the competitive landscape, how can leaders explore whether this technology—and its branches of machine learning and deep learning—makes sense for your company? How do you go about learning more about these advances and what they can mean to you?

Point B's Perspective

There's a lot of buzz in the technology space about AI. But for all the buzz, there are plenty of gray areas. Many executive leaders are under the impression they'll have to invest in AI in order to stay competitive, but they don't yet know how AI would fit into their organization's business model. At the same time, there’s a plethora of companies, both established (Google, Microsoft, Amazon) and entrepreneurial (, DataRobot, Skytree) ready to help companies attack problems with open-source and proprietary tools and methods, and arrive at an informed recommendation for investment.

Where do you begin? First, you don’t need to understand everything about concepts like neural networks, Bayesian network inference or regression to get going. Start with an exploratory perspective and open mind.

Here are our five recommendations for preparing to test drive AI in your business:

  • Consider AI when building your strategic goals. We encourage our clients to see AI as a means to an end—not the end in itself. AI should advance your strategy, not dictate it.
  •  Align projects with tangible business goals. Do you aim to improve call center service? To reduce employee time spent on repetitive, manual processes? Create a list of applicable use cases with clearly defined success criteria.
  • Gain the agility to pivot as needed. Having a centralized analytics team or innovation hub can help your organization hone valuable skills and stay abreast of advances in AI technology. Consider the balance of analytical versus domain expertise, and also understand the limits of your technological computing power.
  • In this explorative phase, get to know AI players in your marketplace. Evaluate their skill sets. What do they bring to the table? Attend a conference or symposium to see who's out there. Connect with other groups that are open to exploring these emerging technologies, and share lessons learned.
  • Choose a focused pilot project and the right approach for your situation (i.e., proof-of-concept). If you’re a biopharma company, you might look into predictive outcomes of a drug within a therapeutic area. Another company might pick an operational function and look at current work processes with high, manual touchpoints to see where there are opportunities for efficiencies.   

Know what’s in your garage.

More than once, each of us has tackled a home improvement project or just ventured out to our garage looking for that one tool or gadget, only to realize that we don’t know what we do (or don’t) have in our garage. A conversation about AI would not be complete without a discussion on data. It’s never too late to start understanding and cleaning up your enterprise “big data”—the data you have collected and amassed for all the various business priorities in your portfolio. What you can do today is to make that data readily accessible: label, curate and make it meaningful. Once you’ve decided to take on a pilot AI project, data will be like gold. It is the fuel for your algorithms as gasoline is for automobiles.

Operationalize AI from the bottom up.

We recommend taking a bottom-up, grassroots approach to AI by putting it to the test with proof-of-concept pilots. The end goal is to learn: test hypotheses, evaluate internal analytical abilities, assess potential AI partners and their tools and methods, and innovate where you have not been before.

Point B has developed a practical framework to get started on the AI journey. We call it "Prep, Test and Launch." It provides both an internal and external lens to validate the benefits of AI as it relates to your goals and objectives. It applies a "bake-off" approach to understanding internal and vendor analytic skillsets, while enhancing the team’s understanding of AI.

Clients who adopt the Prep, Test and Launch framework begin to think differently, operate with agility, and really learn what matters most. They bridge the gaps between value-added AI capabilities, concept pilots and strategic execution. They gain the insight to inform company strategy—and overall AI strategy.

In the long run, incorporating AI may not bring on just technological changes; there’s likely to be a cultural change attached. Starting with a bottom-up approach and being transparent about what the organization learns from the proof-of-concepts can help build broader understanding and buy-in. 

The Bottom Line

At a time where there are still a lot of unknowns in the AI landscape, there is plenty of opportunity to get ahead of the game and stand out from the crowd. By effectively using AI tools and methods that are readily available right now, your organization may be able to accelerate the path to achieving its long-term objectives.

With a bottom-up approach, you create evidence-based learning experiments that provide new insight to both internal and vendor capabilities. Equipped with the right use cases to solve, you'll understand the value AI offers and determine whether it should play a role within the company’s larger mission and vision.

As always, let your business goals be your guide and look for passionate adopters who are innovative, ready and willing to evolve with you.