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Artificial Intelligence and Business Get Serious

October 5, 2015


“A confluence of forces has propelled artificial intelligence into the business mainstream,” assert Deloitte analysts Paul Sallomi (@PaulSallomi), Bob Dalton, and David Schatsky (@dschatsky).[1] In addition to improvements in artificial intelligence (AI) technologies, forces that have converged to bring AI into the mainstream include increased processing power, cloud computing, improved data storage, mobile technologies, and better bandwidth. Their observation is confirmed by a recent report from Tractica. According to that report, “The advance of artificial intelligence (AI) technologies has caught the attention of companies and institutions in a wide range of enterprise markets, and executives in almost every industry are considering the potential impact AI will have on their operations, business models, and bottom lines.”[2] The report explains:

“AI technologies being evaluated and deployed are diverse, yet interrelated, and include areas like cognitive computing, machine learning, deep learning, predictive application programming interfaces (APIs), natural language processing, image recognition, and speech recognition. … As enterprise AI deployments gather increasing momentum, cumulative revenue for the sector will total $43.5 billion worldwide during the period from 2015 through 2024. … The largest application markets for AI will be advertising and media, financial services, manufacturing, oil and gas, and retail. All of these industries work with complex systems and large data sets where companies are finding strong return on investment (ROI) by deploying AI technologies.”

As president of a cognitive computing company, it doesn’t surprise me that the first area mentioned by Tractica is cognitive computing. The Enterra Solutions® cognitive computing platform, the Enterra Enterprise Cognitive System™ (ECS), is a platform that can Sense, Think, Act, and Learn®. The ECS uses many of the techniques mentioned by Tractica. It gets smarter over time and self-tunes by automatically detecting correct and incorrect decision patterns; but, the ECS also bridges the gap between a pure mathematical technique and semantic understanding. The ECS has the ability to do math, but also understands and reasons about what was discovered. Marrying advanced mathematics with a semantic understanding is critical — we call this “Cognitive Reasoning.” The ECS’ cognitive computing approach — one that utilizes the best (and multiple) solvers based on the challenge to be solved and has the ability to interpret those results semantically — is a superior approach for many of the challenges facing today’s businesses. Like AI in general, cognitive computing technologies are not all alike, which makes categorizing cognitive computing difficult. The fact that cognitive computing systems use a variety of techniques only complicates easy categorization. So I was disappointed, but not surprised, when Venture Scanner categorized AI companies but made no mention of cognitive computing companies.[3] Venture Scanner’s categories included:


  • Deep Learning/Machine Learning: Machine learning is the technology of computer algorithms that operate based on its learnings from existing data. Deep learning is a subset of machine learning that focuses on deeply layered neural networks.
  • Computer Vision/Image Recognition: Computer vision is the method of processing and analyzing images to understand and produce information from them. Image recognition is the process of scanning images to identify objects and faces.
  • Natural Language Processing: Natural language processing is the method through which computers process human language (written and/or oral) input and convert into understandable representations to derive meaning from them.
  • Smart Robots: Smart robot companies build robots that can learn from their experience and act and react autonomously based on the conditions of their environment.
  • Virtual Personal Assistants: Virtual personal assistants are software agents that use artificial intelligence to perform tasks and services for an individual, such as customer service, etc.
  • Computer Vision/Image Recognition: Computer vision is the method of processing and analyzing images to understand and produce information from them. Image recognition is the process of scanning images to identify objects and faces.
  • Recommendation Engines and Collaborative Filtering: Recommendation engines are systems that predict the preferences and interests of users for certain items (movies, restaurants) and deliver personalized recommendations to them. Collaborative filtering is a method of predicting a user’s preferences and interests by collecting the preference information from many other similar users.
  • Gesture Control: Gesture control is the process through which humans interact and communicate with computers with their gestures, which are recognized and interpreted by the computers.
  • Video Automatic Content Recognition: Video automatic content recognition is the process through which the computer compares a sampling of video content with a source content file to identify what the content is through its unique characteristics.
  • Context Aware Computing: Context aware computing is the process through which computers become aware of their environment and their context of use, such as location, orientation, lighting and adapt their behavior accordingly.
  • Speech to Speech Transition: Speech to speech translation is the process through which human speech in one language is processed by the computer and translated into another language instantly.


One of the things that the Venture Scanner does accomplish is demonstrating many tasks to which AI systems can be applied and why businesses are exploring new uses for AI. Sallomi, Dalton, and Schatsky make it even simpler. They place AI uses into three broad categories:


  • Product applications embed AI in a product or service to provide customer benefits. Examples include Netflix’s recommendation engine and the use of computer vision to improve car safety.
  • Process applications incorporate AI into an organization’s workflow to either automate processes or improve them by augmenting worker effectiveness. Automated voice response systems have been used for years to replace human customer service agents for first-tier customer support. The Hong Kong subway system employs AI to automate and optimize the planning of workers’ engineering activities, building on the learning of experts.
  • Insight applications harness advanced analytical capabilities such as machine learning to uncover insights that can inform operational and strategic decisions across an organization. For example, chipmaker Intel employs a predictive algorithm to segment customers into groups with similar needs and buying patterns. It then uses this information to prioritize its sales efforts and tailor promotions. The company expects the approach will generate an additional $20 million in revenue once it is rolled out globally.


Cognitive computing systems can be employed in all three of those categories, which is why we represent the Enterra ECS as a tool that can help companies transform into digital enterprises. A transformation consultants insist most businesses will have to make to remain relevant in the decades ahead. If your company hasn’t already started the transformation to a digital enterprise, Harvey Koeppel believes that you should begin immediately and he offers tens steps that business executives can take to embrace “existing and new opportunities arising daily from AI technology.”[4] Those steps are:


  • If you are not already familiar with the basic concepts and principles of AI, read a book, take a course, talk to colleagues and acquaint yourself with the discipline.
  • Familiar or not, continue to monitor new developments in AI technology — this stuff changes faster than we do.
  • If you already have an innovation function within your enterprise (business or IT), ensure that AI is one of the disciplines being tested and developed. If you don’t have an innovation function within your enterprise, create one.
  • Hire and train the best and the brightest AI talent that you can find and afford.
  • As with all new technologies, ensure products and services that you build with AI have commercial viability (i.e., reasonable economic return on investments).
  • With AI applications in particular, be transparent with your internal and external stakeholders. Proper messaging in this area is critical and, if managed well, can be a competitive differentiator.
  • Where the development and deployment of smart technologies may displace human workers, ensure that you have a good HR strategy and plan. Full communication and retraining of affected staff go a long way toward minimizing resistance (sometimes even sabotage) and toward ultimate acceptance.
  • Where AI is being used in an expert advisory context (e.g., sales, service, manufacturing, forecasting), ensure impacted staff understand that the tools are being deployed to help them do a better job, increase their productivity and value, and increase customer satisfaction, which will, in turn, increase employee satisfaction and retention.
  • Of paramount importance, where AI applications are being supported by big data, especially if the data is personal, absolutely ensure that information security and data privacy policies, procedures, methods and tools are employed to protect the data from breach or unintended use. The combination of big data and AI can be extraordinarily powerful, and ways and means to protect it need to be commensurate.
  • Spend quality time with your executive team, C-suite peers and staff to reflect upon potential ethical or moral implications of new AI-based products or applications, including how data is collected, stored, retrieved and utilized. This is especially important for those involved with medical or public infrastructure applications where lives may literally be at stake and/or significant economic or social disruptions could occur when unintended consequences happen — you know that this one is a ‘when,’ not an ‘if.’


Peter Sondergaard (@PeterSonderg), Senior Vice President & Global Head of Research at Gartner, predicts, “Proprietary algorithms that solve specific problems that translate into actions — will be the secret sauce of successful organizations in the future. The next digital gold rush will be focused on how you do something with data, not just what you do with it. This is the promise of the algorithm economy.”[5] I predict the algorithm economy will be powered by cognitive computing systems because they will prove to be the most adaptive technology available and, thus, will be able to serve the AI needs of all of the enterprise not just a few departments.


[1] Paul Sallomi, Bob Dalton, and David Schatsky, “Artificial Intelligence Goes Mainstream,” The Wall Street Journal, 29 July 2015.
[2] Tractica Press Release, “Artificial Intelligence Technologies Are Quietly Penetrating a Wide Range of Enterprise Applications, According to Tractica,” BusinessWire, 19 August 2015.
[3] Venture Scanner, “The State of Artificial Intelligence in Six Visuals,” Medium.com, 12 August 2015.
[3] Harvey Koeppel, “AI technology: Is the genie (or genius) out of the bottle?” TechTarget, 3 September 2015.
[5] Peter Sondergaard, “Big Data Fades to the Algorithm Economy,” Forbes, 14 August 2015.

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