| | April 20179CIOReviewlearning, natural language processing, vision, voice to text, data ingestion, reasoning and deductive engines, and the like. Some firms like a lower level of granularity and others a higher levelI personally don't have a bias one way or another as long as you develop a clean and consistent framework to build enterprise services that can be leveraged across multiple use cases. Many vendors will tell you that their AI platform can handle all of your needs. I have had the luxury of partnering with most of the largest firms in the market, as well as many of the smaller ones, and I can tell you that while there are many amazing technology platforms in this space, I have yet to meet the firm that can deliver on that promise. The very term, whether it is AI or Cognitive, is what creates a great deal of the problem. There are a multitude of different technical capabilities that are grouped underneath the umbrella of those terms. I have found it best to identify the top two firms per service that you are seeking to develop. Often the same platform will be in play for multiple services, which is a good thing, but keeping at least two in the mix ensures that you maintain negotiating leverage and design your services with an eye to being able to add and remove vendors as the landscape evolves.Execute Against Uses Cases that Demonstrate Value QuicklyNow that you have a clear picture of all of the services that need to be built and the technology platforms that you plan to leverage, it's time to go back to your use cases. Pick a small set of use cases that you can execute against that require different services, enabling you to build your architecture piece by piece while still creating real value along the way. I have found that three key criteria that I like to use as part of my prioritization process are 1) ability to materially improve either customer or employee experiences, 2) ability to validate technology components that you believe could be risky, and 3) those use cases which have compelling financial business cases in either the form of incremental revenue generation or cost reduction. By deploying use cases in this measured fashion, you are able to develop the enterprise architecture that you need while delivering real value in the timeframe of months instead of years.Some Final Lessons LearnedThere are a few final thoughts that I would like to share. I hope that you keep these in mind as you embark on your transformative journey:· Experience design matters: In many cases, the experience design is actually more important than the technology implementation. People are excited by new technology, but also apt to reject it if their usability expectations are not met. · The technology and data providers are plentiful (and will change): This industry is evolving faster than almost any other technology in history. It is safe to say that there will be new players before long that you will want to include in your architecture, and some firms that you will want to replace. Design your architecture (and your mindset) accordingly.· Under promise and over deliver: People in your organization have wildly incorrect (and varied) beliefs about what is possible with cognitive technologies. Set their expectations at a level that is possible with the tools you have at your disposal today don't trust that they actually understand what is and isn't possible.The opportunities provided by cognitive technologies are compelling, however the execution of solutions isn't as easy or simple as many would like. A well-executed enterprise strategy, however, may very well be what separates tomorrow's market leaders from laggards. Josh Sutton
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