December 6, 2017
Ready to put artificial intelligence to work at your company? Make sure to get these 10 things right.
A few weeks ago, we joined hundreds of other business and technology leaders at the AI Summit San Francisco. The conference was packed with executives and technologists who were looking for ways to put artificial intelligence (AI) to work in the enterprise. One continuing theme was just how quickly the technology—and corporate interest in AI—had advanced. As conference organizers remarked, in just one year the conversation has evolved from imagining what would be possible with AI to demonstrating practical use cases that are delivering tangible value.
Despite the early successes, however, there’s no clear path for businesses intent on capitalizing on AI. Like any emerging technology, there are a number of considerations and challenges your organization must work through. That’s where our short list comes in: 10 tips you’ll need to get things right. We’ve identified the key business issues you should understand and address. In a future post, we’ll look at the top tech trends that we expect will shape the AI market in the coming year and inform the techniques and tools your data scientists and AI leaders should deploy.
Here now are 10 business trends that could make or break your company’s AI initiatives:
1. Focus on business-led use cases
This seems like an obvious one, but with so many potential areas for AI exploration, starting with the right projects—and stakeholders—is crucial for long-term success. First and foremost, the process of identifying and selecting use cases shouldn’t be driven by technology alone. That is, you don’t want to think about AI solely in terms of where you can apply natural language processing, for example, or how you can leverage a labeled data set. Instead, ask where you seek to increase productivity or derive new value. Some of the questions we covered in the AI Summit keynote help leaders focus in this way: What parts of our business processes are high volume and low margin? What work frustrates our teams? What new markets do we want to enter? In our own Global AI Study we have identified some 300 high-potential use cases across eight industries and three time horizons—but these are only a starting point for each organization to consider and customize.
2. Take a portfolio approach that blends practicality and strong potential
Going through the questioning exercise above with the various leaders who may own or touch AI, such as the chief information officer, chief digital officer, chief data scientist, and other specialists (see #3), will enable you to identify where to start.
Ultimately, you want to take a portfolio approach that might include several quick-win projects as well as one or two that are more complex but have significant potential. This emphasis on “practical” AI is one we expect to see a lot of in the coming year. At the AI Summit, Nvidia’s Brian Catanzaro illustrated this approach. While the company is known as an AI powerhouse thanks to its graphics processing units and AI software tools, Catanzaro, Nvidia’s VP of applied deep learning, talked about how the company is also using AI in less glamorous areas like quality control and resume matching. Likewise, JD.com, the largest retailer in China, not only showcased the more exotic ways it is using AI, drones, and robotics in its warehouses and delivery, but Hui Cheng, head of robotics R&D, also talked about AI’s application in more mundane areas like inventory management, advertising, and pricing.
3. Don’t forget your domain champions
One thing that came across loud and clear at the conference was the importance of functional and other business specialists as you develop AI solutions tailored to your organization. These are the people in the trenches who have the knowledge and expertise about your business processes, pain points, and data. They’re also the ones who will help shape solutions and drive adoption.
Fedex Data Scientist Clayton Clouse drove this point home when talking about the key role that operations managers played in the development, testing, and roll-out of a solution that optimized truck inventory. We think of this trend as the third wave of AI in the enterprise. It started with data as a key advantage. The second wave was all about creating AI platforms using Amazon Web Services, Google Cloud Platform, and Microsoft Azure. And now this new wave will center on vertically focused AI led by domain experts.
4. Meet the labeled data challenge
Getting a handle on your organization’s data has been front and center for some time now. But AI raises the stakes because it adds another layer of complexity beyond the already formidable challenges of gathering disparate data—including the mushrooming amount of unstructured data that lives outside of the rigid confines of a database—and prepping it for analysis. With supervised machine learning, where humans train and tune the algorithm, you not only need very large data sets, but they also must be labeled so that the model can “learn” to identify the correct outcome. For even a simple model, labeling might take 30 seconds per label; if you have 10,000 pieces of data, that amounts to about 100 hours of work. Some companies outsource the work of labeling or use open source or crowdsourced data that has already been labeled. Looking outside your enterprise will also become more important as you begin to think about the broader data ecosystem in your industry, such as the partners, customers, regulators, and other entities that are part of your data flows.
5. Put the user experience first
As with any technology solution, one that uses AI will only be successful if customers, patients, employees, and other users adopt it. This requires understanding the user experience—how people interact with the system and/or its outputs. One way to put people first is by applying design thinking or similar approaches that focus on the user journey and identify how and where technology can improve it. Sometimes the surprising answer might be that AI isn’t needed; the solution may be a process change or a simpler technology. On the other hand, it might also reveal a bigger problem that AI can help solve. Putting the user first might be something as seemingly simple as what Kal Mos, VP of R&D at Mercedes-Benz shared about designing the new S class. First, Mercedes-Benz asked drivers if it was OK with them for the car to suggest a destination. This put drivers in control and helped to establish trust in the AI-enabled car.
6. Talk about AI in the boardroom—and the breakroom
As AI permeates the enterprise, everyone—from the CEO and business unit leaders to your middle managers and frontline employees—will need to be conversant in some basic terminology. Speaking the same language gets organizations on the same page and helps demystify AI’s role in the enterprise and what it means for business processes and workers. One key area to understand at a high level is machine learning and the different stages (and data) involved, including data model, training, validating, and testing. Other concepts to know: deep learning, robotic process automation (RPA), and simulation.
7. Understand the ROI of AI
Other new terminology you’ll need to learn and think about has to do with how you calculate the return on investment (ROI) for AI. In our panel at the AI Summit on pragmatic, practical, and measurable AI, we focused on the need for two kinds of metrics: the traditional ones by which you’d gauge any business investment (for example, revenue increases, claims processed, service requests resolved), as well as ones that capture new kinds of value. For example, you might look at automated full-time equivalents (AFEs) as automation takes on discrete tasks or business processes and frees up human workers. Or you might look at less tangible returns, such as increased confidence in an outcome or the additional options an AI solution provides, such as different ways to explore strategy (see #9).
8. Make the workforce of the future a reality
You likely keep hearing about automation and the workforce of future, and it’s not a far-off proposition. Companies are already beginning to implement “digital labor” in the form of RPA, machine learning, or other AI, and as they do so they must consider how these new models of human and digital labor will function. Which tasks or part of processes will become automated? What specific higher-value work will human workers assume? Which skills are needed and how will you upskill workers or procure new talent? How will human workers train or be enhanced by their digital counterparts? Your executive team must ask and answer these questions rather than leaving them to the Human Capital or IT department to solve.
9. Harness AI for business strategy and planning
While AI has become somewhat synonymous with automation, it’s important not to lose sight of its strategic potential. We expect to see more and more companies using AI to augment strategic planning, such as the work we did for a Fortune 100 automaker that was exploring a new ride-sharing business. In essence, we built a C-suite cockpit that enabled leaders to assess more than 200,000 simulations for entering new markets. (In fact, this work was part of a broader portfolio of projects, including an autonomous fleet-optimization solution that won Best Application of AI in the Enterprise at the Summit’s AIConics Awards.)
10. Internalize the meaning of Responsible AI
Questions regarding ethics, trust, and transparency will only continue to grow as AI becomes more widespread. Business leaders must begin addressing all facets of what we call Responsible AI. This includes answering questions such as the following: Is the algorithm or solution unbiased? Can you explain what it is doing in a way that businesspeople and other stakeholders can understand? Can you prove the outcome is correct? Can you assure and verify it is working how it should, every time? Do you have the right controls built in that address various stakeholder groups? What’s your responsibility if others use the output or solution unethically? These are just some of the tough questions that business and government leaders must begin tackling in order to reap AI’s full potential.