WEBINAR The Promise And Potential Peril Of AI

2. WEBINARThe Promise And Potential Peril Of AI Martha Bennett, Principal Analyst Matthew Guarini, Vice President, Research Director March 15, 2017. Call in at 10:55 a.m. Eastern Time

4. Agenda • Introduction: Why is AI such a hot topic today? • How does Forrester define “AI”? • What are some of the key application areas for AI? • Which are the main pitfalls you should avoid? • How should you drive your organization’s AI strategy?

5. How much AI have you interacted with today?

6. Artificial intelligence (AI) isn’t one single technology • Like “cloud” and “big data,” it means different things to different people and consists of a wide variety of different technologies. • The technologies under the AI umbrella are at different stages of maturity. • Source: TechRadar™: Artificial Intelligence Technologies And Solutions, Q1 2017 Forrester report

7. Examples of pragmatic AI include: • Speech recognition and natural language processing and generation • Alexa, Siri, Google Assistant, and Cortana • Chatbots on websites and in mobile apps • Automated translation • Machine learning and knowledge engineering • Lots and lots of application areas, ranging from reducing churn, predicting propensity to buy, personalization on the fly, recommendations, fraud detection, predictive maintenance, etc. • Image recognition combined with machine learning and deep learning • Detecting microscopic flaws and differences in manufactured goods • Prioritizing brain scans for examination by a clinician

8. Examples of pragmatic AI, continued: • Advanced discovery techniques: • Machines are far superior to humans when it comes to data crunching — try reading millions of pages of specialist literature in seconds . . . • Flagship example: IBM Watson for Oncology • Robotics and self-driving cars • Self-driving cars aren’t about to take over, even though they’ve already proven to have lower accident rates than humans. • Robotics isn’t just about machines taking over jobs — a key application area is supporting human workers.

9. True “cognitive” systems are as far away as ever, but companies can leverage AI technologies today. Source: Artificial Intelligence: What's Possible For Enterprises In 2017 Forrester report

10. Interest in AI is high, adoption is in the early stages Source: The Promise And Potential Peril Of AI Forrester report

11. Q: “How much has your team done to date with AI?” Poll Actively using Implementing Piloting Studying Nothing

12. Q: “What areas of your organization are leading or evaluating the investment and adoption in AI systems?” Current AI investment is customer-focused. Base: 418 business and tech professionals; “don't know” answers excluded from analysis; Source: Forrester’s Q2 2016 Global State Of Artificial Intelligence Online Survey

13. Q: “In areas where your organization has piloted, tested a POC, or deployed AI, what results have you achieved?” Early adopters are seeing results. Base: 394 business and tech professionals; “don't know” answers excluded from analysis; Source: Forrester’s Q2 2016 Global State Of Artificial Intelligence Online Survey

14. Many AI scenarios revolve around augmenting human expertise, not replacing it.

15. There are times when machines perform better than humans . . .

16. . . . but there are also many scenarios where humans and robots enhance each other.

17. Robots can help address labor shortages. Head chef: robot Andrew flips a pancake in the Henn-na restaurant at the Huis Ten Bosch amusement park in Sasebo, Nagasaki — The Asahi Shimbun/Getty Images Source: Financial Times, February 1, 2017

18. But it’s not all brightness and sunshine Key pitfalls include: • Training bias and overfitting. • Use of inappropriate techniques. • Underestimating complexity and skills requirements. • Not considering how a system could be subverted. • Compliance/ethics violations.

19. When things go wrong, it can be funny . . .

20. . . . extremely awkward and embarrassing . . .

21. . . . distinctly unfunny, . . .

22. . . . or even lethal.

23. But it’s not all brightness and sunshine Key pitfalls include: • Training bias and overfitting. • Use of inappropriate techniques. • Underestimating complexity and skills requirements. • Not considering how a system could be subverted. • Compliance/ethics violations. AI isn’t magic — it’s a lot of hard work!

24. Recommendations • Drive your organization’s AI strategy — how can it benefit? • If you don’t, your competitors probably are. • Develop a road map for AI — start small, but think big. • Incremental benefits or business transformation? • Build capability in your team to be the go-to-resource of AI. • Otherwise, business decision makers will go elsewhere. • Make sure you take an end-to-end process view, and also look across processes. • Beware of unintended consequences. • Start the conversation about legal and ethical issues at board level. • Your organizations want to be in the headlines for the right reasons!

25. Further reading • The Promise And Potential Peril Of AI Forrester report • TechRadar™: Artificial Intelligence Technologies, Q1 2017 Forrester report • Artificial Intelligence: What’s Possible For Enterprises In 2017 Forrester report • Artificial Intelligence Revitalizes BPM Forrester report

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27. Martha Bennett +44 7768 896 540 mbennett@forrester.com Twitter: @martha_bennett Matthew Guarini +1 630-862-4940 mguarini@forrester.com Twitter: @guarini12