Artificial Intelligence (AI) continues to drive the technology discussion of numerous organizations. Many of our clients want AI solutions, but first want to know exactly what it is, how to integrate it into their existing technology stack, and what use cases demonstrate the most value.
If you still cringe at the first step and thought of defining AI in your organization, don’t worry—you are not alone. The most common questions I continue to get asked, as a Gartner analyst researching AI, is “what should AI mean to my organization, and how should we define it?”
So let me help, by offering three key insights and guiding principles that offers CIOs and technical professionals an alternative perspective that leads to a greater path of delivering value from AI to their organization. These will also help prepare you for the next AI revolution—although, I admit, this has not been formally defined yet. Nonetheless, preparing your AI strategy with these insights will, at least, help minimize the shock of what I believe to be the future of AI—changes in the relationships and experiences between humans and technology on a much more connected scale. I do agree with Facebook’s Yann LeCun in that “The Next AI Revolution Will Not be Supervised“. And, I believe this will result at the beginning of more rapid development in ensembles, model-to-model interchange standards, machine-to-machine understanding, and intelligent data sharing. Stay tuned, we will be writing much more in these areas in the next few months.
The most important element to any strategy is the vision. The strategy has a tactical approach to driving toward that vision. It’s the means to the end. AI should always be the means to the end vision, regardless of whether you’re a technology company, manufacturer, or service provider. As an example, one of my favorite frameworks to reference is the Business Motivation Model by the Object Management Group.
“The Business Motivation Model specification provides a structure for developing, communicating, and managing business plans in an organized manner.” (Object Management Group)
So, how should we be thinking about the business motivation of the AI strategy? Before building your strategy, you should consider these three insights:
1) AI is much more than one thing. Stop trying to define AI as simply a new technology, and instead, focus on AI as an integral part of the organization’s fabric that drives performance. Vendors will continue to define AI in the context of their offerings. Some will be hurtful hype, some will be promising, and some will continue to be a major disruptor and key differentiator in the market.
As vendors label their AI offerings, be sure they clearly outline how the solution integrates into the day to day operations of the business to deliver value. Your question should be “how does your solution enhance my user experience to deliver more value to the business”? And “how can our existing technology stack be integrated with your solution to enhance that benefit? Which leads me to #2:
2) AI solutions are about innovating relationships. The relationship is between the user/customer/client community and the business. Focus on enhancing the daily lives of the user community so that the business gets value in return—through productivity and innovation. AI technology should remain the enabler to enhancing the user experience, but it should not be the end goal.
AI can be transformative, but not because it’s a cool technology, but because it has the potential to enhance the daily lives of every consumer, client, citizen, or organization.
3) The real power of AI solutions is in the interconnectedness of its technologies. How are multiple AI technologies connected to deliver an aggregate value stream? Think of AI solutions as the sum of its parts, similar to how we think about the Internet of Things and other complex systems. In other words, the value of any long-term AI solution is in the aggregation of lower order AI technologies. This, I believe, is the next AI revolution. Perhaps, the emergence of intelligence is what we are waiting on, rather than assuming AI is or is not that intelligent. Can the combination of multiple AI technologies working together present some emergent behavior that is intelligent? Reminds me of the old agent-based and bio-inspired modeling days–now this is called “Swarm Insights“. Hmmm, how history seems to repeat itself, but maybe we’ll get better at it this time.
At MS Build this year in Seattle, one of the most interesting advances by Microsoft and Amazon was in the integration of their conversational AI devices, Alexa and Cortana. Alexa was able to communicate with Cortana, and vice versa, to achieve an objective. This is an important step in advancing the field of AI and its impact on the business.
Try to keep these guiding principles at the forefront of building your AI strategy:
A. Empowerment. Organizations should empower developers and technical professionals to create better user experiences fueled by AI. The vision of AI should always be about empowering technical professionals and the business citizens to build a better user experience. Technology cannot do this alone, and neither can AI. This was a core theme at Microsoft’s Build 2018 conference in Seattle, and it is a good place to start thinking about how technology fits into your AI strategy.
Let Gartner for Technical Professionals demonstrate how to empower your staff to build and leverage AI technologies that enhance the daily lives of corporate citizens so that the business gets value in return.
B. Data. Data is still the nucleus that enables successful AI solutions, and it remains the most important driver. Part of the reason machine learning (ML) has been so successful is that of its ability to train models based on data—as opposed to traditional methods that explicitly defined how the application would behave. Leveraging ML in your organization tells the world that you are truly data-driven. If asked “are you a data-driven organization?”, then respond with “YES, because we use ML to train models that help us achieve our goals”, for example.
Don’t drown in your own data. Let Gartner for Technical Professionals help you build a robust data strategy to support AI/ML initiatives.
C. Governance. Governance of AI solutions drives greater responsibility and new opportunities. Focus on doing things right AND doing the right thing. If you use that slogan, you’re much better prepared for taking advantage of new opportunities and being in a better position to support compliance, while minimizing risk. Remember, the more AI is fused into the fabric of your organization, the more governance is needed to ensure you are doing the right thing by avoiding algorithmic bias, misuse of data, improper data wrangling techniques, and exposing private data. Wow—governance-related AI jobs might just be the next major global job trend (hint hint).
Let Gartner for Technical Professionals help align existing and new staff to the major trends in the skillset, roles, and responsibilities needed to support the next generation of AI.
For more information on developing an AI strategy, please check out Gartner reading “Driving an Effective AI Strategy” and “Craft an AI Strategy: A Gartner Trend Insight Report”
A great resource for learning the basics around AI.
Microsoft Build 2018.
Business Motivation Model Specification by the Object Management Group.
Next blog post: Why Support Vector Machines Should Be In Every Data Professional’s Toolkit
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