Lessons in Innovation and Artificial Intelligence: How to Define Your Strategy

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Enterprises that compete in quickly changing markets or industries must continue to innovate while maintaining core business operations. This balance of current and future is not easy but has been recognized as a critical component for competitiveness. And yet, according to IDG, 72 percent of CIOs struggle with balancing innovation and operational excellence.1

Excellence in innovation – an example

One example of a firm known for striking this balance is Google. A research study with significant organizational access determined that Google used its org design in combination with talent recruiting to create an environment that fosters both operational excellence and innovation.2 In fact, their team structures and rewards systems have been designed to encourage their employees to support the core business while continuing to innovate, and that is why they’ve been able to move forward in their innovation while continuing to maintain strong core solutions.

Examples of innovation through AI and ML

One key area of innovation today that many people have started to explore is in the area of artificial intelligence (AI) and machine learning (ML). The reason why there is high interest in this technology is that it has demonstrated to be a breakthrough tool for finding new solutions. For example, Israeli company Zebra Medical Vision has created algorithms designed to predict diseases, including cardiovascular events.3 In another example, Descartes Labs has been able to maximize the use of its petabytes of data by using machine learning to predict global crop yields, not only for the benefit of its customers but also in a way that has been profitable for this firm.4

The challenge: Defining and deploying the AI/ML strategy

Of course, for new entrants who seek to use advanced data technologies like AI and ML, one of the biggest challenges is to clearly define what their AI/ML strategy is and determine where to deploy it. This is because sometimes your company or business partners may have unreasonable expectations from reasonable technology. After all, when you see what appears to be extremely impressive technologies starting to emerge, sometimes it’s hard for people to believe that deep learning and neural networks are not able to solve everything. In reality, an algorithm is only one part of a larger fabric needed to solve large and complex problems. Any organization that seeks to explore AI/ML solutions must remember to look at things from multiple angles. And, the technology leaders must continually set expectations and then deliver on those expectations so as to maintain both progress and proper expectations.

Building an AI/ML practice at your organization

In my work with large corporations, small startups and even governmental organizations where I’ve developed and built AI strategies and organizations, I’ve found that it’s important to use the following four-step approach:

  1. First, start from a customer-centric perspective so that you can identify the solutions needed.
  2. Understand what is unique and what is common among your firm’s capabilities and opportunities.
  3. Determine how to address the opportunities in a way that makes the biggest difference for the organization.
  4. Finally, and most importantly, you have to find the right people to execute against the strategy. Without the right people your team will be unable to show measurable progress toward the promised solutions.

Combining the best of customer understanding, processes and people, your organization can advance toward its own AI/ML strategy in a way that works best for your customers and your company.

Ashok N. Srivastava, Ph.D., is Senior Vice President and Chief Data Officer at Intuit. He is responsible for setting the vision and direction for large-scale machine learning and artificial intelligence across the enterprise, to help power prosperity across the world.

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1 IDG, 2017. State of the CIO. https://www.cio.com/article/3163000/cio-role/state-of-the-cio-2017-more-challenging-still-complicated.html

2 Steiber, A. 2012. Organizational innovations: A conceptualization of how they are created, diffused and sustained, Chalmers University of Technology. https://research.chalmers.se/publication/156232

3 WIRED, 2017. Most innovative companies in AI/machine learning: Zebra Medical. https://www.fastcompany.com/company/zebra-medical

4 WIRED, 2017. Most innovative companies in AI/machine learning: Descartes Labs. https://www.fastcompany.com/company/descartes-labs

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