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TDWI Upside - Where Data Means Business

Working AI into Your Enterprise Initiatives

Find the right focus for artificial intelligence in your enterprise in the coming year.

When determining where to apply artificial intelligence in your enterprise, the possibilities are quite broad. In the coming years, almost every new and updated application will depend on some form of AI. For example, you might introduce AI in:

  • The products you make and the services you offer
  • The supply chain for those products and services
  • Business operations (hiring, procurement, after-sale service, etc.)
  • The intelligence used in determining and designing your product and service set
  • The intelligence used in the marketing/approval funnel for your products and services

For Further Reading:

Artificial Intelligence Starts with Data

In the Middle of DatA Integration Is AI

The AI Toolbox: New Skills for a New Generation

Effectively matching AI to the challenges in these business areas requires you to understand what AI does better than a human and better than human-controlled business intelligence.

When AI Performs Best

The identification of objects by a well-trained AI-based camera is on par with a human. Sure, there can be an immense amount of work training the algorithm on all the varieties and conditions of speed-limit signs, but that autopilot camera on the Tesla is going to scan for the signs all the time. Although eyeballs and cameras might have similar recognition capabilities for speed-limit signs, as the object grows more complex, AI has been shown to detect objects more accurately and rapidly. Furthermore, the human driver frequently just assumes the speed limit, goes with the flow of the traffic, or just doesn't care.

Speech recognition is another area where AI-trained agents can excel. With deep learning, Microsoft researchers created a system that performed as well as a human in recognizing conversational speech. The team has subsequently lowered the error rate of AI in speech recognition to well below that of a human transcriptionist.

AI is great at enabling deep analysis when the massive amount of data would otherwise inhibit analysis or not meet performance expectations. The financial services industry, for example, looks for fraud and next-best customer offers in real time. To determine fraud accurately or the most beneficial offer or guidance, all of the organization's up-to-the-minute data can be brought to bear. You can't possibly prepare for these discrete situations with prebuilt analytics.

Similarly, in healthcare and medical research (where the best discoveries, diagnosis, and care patterns are essential), all data is instructive. Eventually healthcare will incorporate mind-bogglingly data-rich DNA data sets in many of its decisions.

When Human Intelligence Is Better

Given these areas of AI's impending domination, it is important to point out areas where humans continue to outperform the machine. Mismatching AI to workloads that need human intelligence could result in failure and in giving AI the proverbial "black eye" in your organization. Recovering from this could take years, during which time competitiveness will likely wane.

Humans are better at finding relevant information during a maelstrom of data overload during unstructured discovery. We can still generalize better than AI, can apply more flexible thinking, and have a sense of unspoken requirements and the broad ramifications of decision making.

Find the right focus for AI in your enterprise in the next year, starting with those aspects of your business and initiatives that align with AI's emerging core strengths.


About the Author

McKnight Consulting Group is led by William McKnight. He serves as strategist, lead enterprise information architect, and program manager for sites worldwide utilizing the disciplines of data warehousing, master data management, business intelligence, and big data. Many of his clients have gone public with their success stories. McKnight has published hundreds of articles and white papers and given hundreds of international keynotes and public seminars. His teams’ implementations from both IT and consultant positions have won awards for best practices. William is a former IT VP of a Fortune 50 company and a former engineer of DB2 at IBM, and holds an MBA. He is author of the book Information Management: Strategies for Gaining a Competitive Advantage with Data.

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