Future Trends in Forrester's 2016 Webinar: Mobile, Micro Moments, AI, and Consumer Data

2. WEBINARForrester Futurology: Trends For 2016 And Beyond Carrie Johnson, Senior Vice President, Research Michael Facemire, Principal Analyst J.P. Gownder, Vice President, Principal Analyst Fatemeh Khatibloo, Principal Analyst Diego Lo Giudice, Vice President, Principal Analyst Jeffrey S. Hammond, Vice President, Principal Analyst March 14, 2016. Call in at 11:55 a.m. Eastern time

4. Agenda • The Future Of Mobile • Converged Micro Moments • Artificial Intelligence • Cognitive Automation • Consumer-Controlled Data And Emotional Context

5. The Future Of Mobile Michael Facemire, Principal Analyst mfacemire@forrester.com

6. The future of mobile • Stage 1: The barriers between web and mobile fall. • Stage 2: Mobile platform experiences • Stage 3: Virtual agent experiences • Stage 4: Blended ecosystem experiences

7. Mobile construction evolution Static assemblyon a device Dynamic assemblyof blended experiences (anywhere) App extensionson platforms Apps and web

8. Converged Micro Moments Jeffrey S. Hammond, Vice President, Principal Analyst jhammond@forrester.com

9. From mobile moments . . . . . . to micro moments Get phone Real world Get lost in app Real world Action Alert

10. Traditional methods of I/O are unbalanced Punchcard, Keyboard, Mouse, Finger Tactile Paper, Screen Visual Physical Digital Aural Speaker, Headphones Olfactory Gustation

11. New methods of I/O balance inputs Advanced tactile Tactile Computer vision Visual Physical Digital Aural Audio processing Olfactory Gustation

12. Micro moments + computer vision

13. Augmented vision + autos

14. Autonomous obstacle avoidance at 30 MPH

15. Designing for micro moments • What is the “minimum viable information”? • Visual output should use lightweight fonts, transparency. • From “screens” or “pages” to “conversations” • Offer inputs with minimal tactile or audible choice. • Think about designing state machines. • There is no “back button” — just a timeline.

16. Key challenges • N-UX experiences are evolving separately. • Mixed and multimode interactions (fluid voice, vision, and touch) • Power, CPU, and battery • Uncanny valley — machine proactivity • Different UX patterns based on location, context

17. Key enablers • Cloud processing for NLP • Increasing success of neural nets • Costs are falling fast • MVP trials by Google, Samsung, and Microsoft • Public conditioning for narrow-intelligence AIs (Watson, Alpha Go, driverless cars, and drones)

18. Artificial Intelligence Diego Lo Giudice, Vice President, Principal Analyst dlogiudice@forrester.com

19. What makes AI credible this time around . . . 2. ML, deep learning algorithms 1. Computing power 3. Big data 5. Huge investments Source: “Artificial Intelligence Can Finally Unleash Your Business Applications' Creativity” Forrester report 4. Age of the customer/ digital demand

20. What makes up an AI system? Great improvements The new opportunity: bigdata Today: GPUs Ground breaking Far out: The Machine, SyNAPSE, Graphene, Quantum devices. Source: “Artificial Intelligence Can Finally Unleash Your Business Applications' Creativity” Forrester report

21. AI — deep learning algorithms What it is An area of artificial intelligence which enables learning over massive data, with more precision than (most) conventional machine learning (ML) models Deep learning algorithms bring AI and ML to the next level, as they remove manual steps from the training process to discover patterns, abstraction, and representations (unsupervised learning). Various deep learning “tribes” exist; the most popular are based on neural networks (connectionism), which superficially resemble the way the brain works. Examples: TensorFlow (Google)

22. AI — deep learning algorithms Why a big impact on business With deep learning, AI will bring all industries to the next level of unprecedented automation. Google, Baidu, and Facebook are among some using deep learning. Many AI startups too (Skymind, Numenta, and Vicarious) Breakthrough results have been achieved in several domains such as video/image recognition and classification, speech recognition, voice search, text understanding, and translation.. . .

23. AI — deep learning algorithms Why a big impact on future business Today, AI is capable of learning like a three- to four-year-old child. In 10 years . . . ? Examples and broad business application. Customer engagement: natural language process, text understanding, image recognition, and voice speech Complex and fast decision-making: recognizing complex patterns in data to support decision-making process Cyber-security (e.g., D-instinct applies deep learning for military-level cyber security defense) Fraud detection Google: Face recognition in three-days training (same algorithm would recognize cats), driverless cars

24. AI — deep learning algorithms All industries Deep learning combined with other AI techniques will help address more new business operational problems. Dreaming a bit, but not too much Robots in branches for tasks that either would take too long to train existing staff or hire and skill up new staff (e.g., Bank of Tokyo Mitsubishi for 2020 Olympics). Improving natural language and image recognition to a level that beats human precision Improve investments, stock picking, mutual funds, and fraud

25. Cognitive Automation J.P. Gownder, Vice President, Principal Analyst jgownder@forrester.com

26. Automation technologies continue to take over human job tasks Source: “The Future Of Jobs, 2025: Working Side By Side With Robots” Forrester report

27. Cognitive automation is increasingly taking on intellectual job tasks The Wordsmith natural language generation platform from automated insights automates content creation for the Associated Press (AP), writing 4,300 financial reporting articles a quarter (compared with 300 by humans). Vision: “Instead of writing 1 article seen by a million people, we want to write 1 million articles tailored to each of those million people.” Source: “The Future Of Jobs, 2025: Working Side By Side With Robots” Forrester report

28. Software robots and cognitive systems replace human labor IBM Watson, digital reasoning in cognitive computing. Blue Prism for customer service. SyntBots (Syntel) for IT operations. Software Robot Source: “The Future Of Jobs, 2025: Working Side By Side With Robots” Forrester report

29. X.ai offers a virtual scheduler that fools humans Source: “The Future Of Jobs, 2025: Working Side By Side With Robots” Forrester report

30. Where will cognitive automation lead? • Job losses: Certain workflows (like contact centers, other customer service agents, and scheduling) will be disrupted by narrowband intelligences. • Job transformations: More complex workflows (like IBM Watson’s work in oncology) lead to a situation of “working side-by-side with robots,” in which cognitive automation becomes a coworker. Source: “The Future Of Jobs, 2025: Working Side By Side With Robots” Forrester report

31. Consumer-Controlled Data Fatemeh Khatibloo, Principal Analyst fkhatibloo@forrester.com

32. The data ecosystem is broken • Companies rely on third-party data to infer intention, interests, and needs of their customers. • This data is often static, outdated, incorrect, or dirty . . . • . . . so companies make marketing and business decisions based on bad data. • Data about individuals is collected, aggregated, used, and sold without regard for the individual herself. • Online data collection happens surreptitiously and without the user’s permission. • The data is then used with little transparency, so customers can’t opt-out or fix what companies know about them. • First-party data is often badly protected, especially as it gets moved around, increases the risk of breaches. • Both problems are only getting worse with the proliferation of “connected everythings” and “big data.”

33. For example . . . imagine you’re buying a car • As you research vehicles online, a dossier is being built about you by ad tracking companies

34. For example . . . imagine you’re buying a car • When you finally buy your car, you fill out dozens of forms — loan applications, DMV registrations, insurance applications — without knowing where the data’s going, how it’s protected, or who has access to it.

35. For example . . . imagine you’re buying a car • Each time your car gets serviced, hundreds of new data points about both the car itself and your driving habits are generated and collected.

36. The current model is broken. It’s wasteful and risky for everyone.

37. Enter: personal identity and data management • People “collect” their personal data in secure “lockers” that are hosted by trusted third parties. • Companies request access to specific types of data, and the individual grants or denies the request via an “authorization manager.” • The authorization manager can “learn” the users’ preferences, favorite brands, etc., and automate the approval process, too. • The company gets access to fresh and accurate data. • The individual’s data is safe and used for her benefit. Source: “Personal Identity And Data Management” Forrester report

38. Examples are already emerging Autograph.me lets users build their own ad targeting profile, anonymously. Brands gain access to an accurate psychographic model, and eventually, if the user trusts them, can gain access to her identity. Personal.com is a data locker (personal and enterprise) that enables sharing between users. Meeco lets users create their “private web” by browsing inside the app. Then, they can “signal” to participating brands about their interests and intentions.

39. Emotional Context: The Internet Of Me Fatemeh Khatibloo, Principal Analyst

40. Customer experience is all about context • Companies are already using location data, behavioral data, and social signals to infer a user’s context. • Now, facial detection software, gait analysis, and footfall patterns are providing important cues to an individual’s emotional context. • Facial detection software can identify smiles, frowns, furrowed brows (concern), and more. • Gait analysis can detect hunched postures, aggressive strides, and more. • Foot traffic patterns can indicate what kinds of displays a person is drawn to, whether they’re moving purposefully through a space, or if they’re wandering. • Soon, as wearables and embeddables become ubiquitous, we’ll also be able to send signals about our emotional context via metrics like heart rate, galvanic skin response, even hormone levels!

41. Getting emotional context right will be tricky • Preferential, dynamic pricing • Content that’s perfectly tailored to my interests • Mood-manipulation to improve receptivity to a brand or offer • Emotional state analysis that can reduce fraud • Prejudicial, discriminatory pricing • Content that limits discovery of new ideas and products • Mood-manipulation that backfires and causes emotional distress • Emotional state analysis that can easily be gamed vs . vs . vs . . vs

42. Customer experience is all about context • Companies are already using location data, behavioral data, and social signals to infer a user’s context. • Now, facial detection software, gait analysis, and footfall patterns are providing important cues to an individual’s emotional context. • Facial detection software can identify smiles, frowns, furrowed brows (concern), and more. • Gait analysis can detect hunched postures, aggressive strides, and more. • Foot traffic patterns can indicate what kinds of displays a person is drawn to, whether they’re moving purposefully through a space, or if they’re wandering. • Soon, as wearables and embeddables become ubiquitous, we’ll also be able to send signals about our emotional context via metrics like heart rate, galvanic skin response, even hormone levels!

43. Creating the Internet of Me is the only way we can reach the full potential of individualization.