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Deriving Value From Machine Learning in Business


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Unlike other components to an enterprises’ technology mix, determining the ROI of machine learning is a less-than-obvious process, particularly when solutions are new and little by way of case studies or benchmarks exist.

While we’re far from a world where SMBs (small- and mid-sized businesses) outside of Silicon Valley integrate AI into their regular operations, we will undoubtedly see an explosion of novel uses in industry and enterprise over the next 5 to 10 years, and executives are rightly concerned with how to make the most of those technology, time, and staffing decisions. If you’re a business who’s new to the machine learning scene (and that’s a vast majority), there are more burning questions than answers at present.

This is exactly why TechEmergence set out to ask the question:

“What are the criterion needed for a company to derive maximal value from the application of machine learning in a business problem?”

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Deriving Value From Machine Learning in Business

  1. 1. In this TechEmergence Consensus, we contacted a total of 30 artificial intelligence executives and researchers to ask them about the criterion needed for a company to derive maximal value from the application of machine learning in business problems.
  2. 2. This slide deck displays the major trends of responses as well as some of the most poignant quotes from the recognized experts we spoke with.
  3. 3. Access to complete data sets and all quotes and answers from our Machine Learning in Business Consensus is available for free download as a spreadsheet or Google Sheet in the link below. This series includes: + Machine Learning Industry Predictions + Deriving Value From Machine Learning in Business + Misconceptions in Machine Learning + Applications of Machine Learning >> CLICK HERE Download the complete response set below:
  4. 4. © TechEmergence Consensus July 2016 “What are the criterion needed for a company to derive maximal value from the application of machine learning in a business problem?” Sufficient Data Pick the Right Problem Data Science Talent Percentage of Responses 0 10 20 30 40 50 * Answers from the respondants were submitted in an open ended text format later categorized and sorted after submission by
  5. 5. We’ve selected three quotes from each of the major response categories. Beneath each quote is a link (if available) of our complete interview with this guest on the TechEmergence Podcast. * These consensus answers were recorded seperately from our podcasts interviews, but most podcasts are focused on related topics around the ethical implications of emerging technologies.
  6. 6. SUFFICIENT DATA “Quantity and quality of a company’s business data is vital when it comes to machine learning application. The ability to automate and integrate such applications, once developed, with business processes and workflows is also required.” - Dr. Helgi Páll Helgason VP of Operational Intelligence, Activity Stream
  7. 7. SUFFICIENT DATA “Possession of large amounts of proprietary data relevant to their business is the key criterion for companies wanting to exploit machine learning.” - Dr. Alexander D. Wissner-Gross Founder, President, and Chief Scientist, Gemedy, Inc.
  8. 8. SUFFICIENT DATA “What is needed is an abundance of data, a well defined problem, and a large set of correct answers to the problem. Additionally, ample lead time is needed to train the machine before expecting any value.” - Dr. Pieter J. Mosterman Senior Research Scientist, MathWorks >> CLICK HERE Listen to or read our full interview with Dr. Mosterman at
  9. 9. PICK THE RIGHT PROBLEM “Identify allocation problems that can scale in new ways when automated and optimized, and identify tasks (such as UI/UX, data processing in existing products) that are relevant for success of the product, and have not been touched in the last 24 months: these are very likely to benefit from incorporating recent progress.” - Dr. Joscha Bach Research Scientist, MIT Media Lab >> CLICK HERE Listen to or read our full interview with Dr. Bach at
  10. 10. PICK THE RIGHT PROBLEM “The best setting is an operational loop that is closed under automated processes. A classic example would be product recommendations in e-commerce -- a virtual environment for an AI agent to learn, through experimentation, how to up and cross sell.” - Dr. Edward Challis Co-Founder & CEO, re:infer >> CLICK HERE
  11. 11. PICK THE RIGHT PROBLEM “The most promising opportunities for machine learning applications are those that elude easy codification into rules, but which are nonetheless tedious for a human.” - Dr. Richard Downe VP of Data Science, Casetext >> CLICK HERE
  12. 12. DATA SCIENCE TALENT “A company should count with staff who are ex- perts in all stages of the data analysis pipeline. This mostly includes experts not only in machine learning but also in the sensor and their mea- sures, which will be used as features, e.g. sales forecasting needs a sales expert in the team not only data scientists.” - Dr.-Ing. Aureli Soria-Frisch R&D Neuroscience Manager, Starlab Barcelona SL
  13. 13. DATA SCIENCE TALENT “Companies need to understand the foundations of machine learning, so they don’t apply it naively and get bad results.” - Dr. Bruce MacLennan Author, Associate Professor at University of Tennessee >> CLICK HERE Listen to or read our full interview with Dr. MacLennan at
  14. 14. DATA SCIENCE TALENT “Good theoretical background in data science and artificial intelligence. Knowledge how to apply theoretical frameworks into practical use.” - Dr. Mika Rautiainen CEO, Valossa Labs Oy
  15. 15. If you’ve enjoyed this presentation and you’d like to see the full dataset of responses, the consensus is freely available below: >> CLICK HERE
  16. 16. Thanks for viewing our presentation. If you’d like to stay ahead of the curve about cutting-edge research trends and insights in the field of artificial intelligence, be sure to stay connected on social media by clicking the icons below: | © TechEmergence LLC 2016 All Rights Reserved | Design by J. Daniel Samples