SSP London, 5 th July 2018

Transcription

1 Image created by MullenLowe for MassArt campaign Defining an Artificial Intelligence strategy SSP London, 5 th July 2018 Isabel Thompson Senior Strategy Analyst, Holtzbrinck Publishing Group

2 Scholarly Communication doesn t have an AI problem It has a strategy problem

3 Problems Competing investment priorities Unclear use cases Unknown ROI Not enough data Poor quality or siloed data Un-agile organizational set-ups A lack of data scientists and tech talent

4 Outline 1. What is AI? 2. What can it do? 3. What should I do?

5 1. What is AI?

6 AI is The field of computer science dedicated to solving cognitive problems commonly associated with human intelligence, such as learning, problem solving, and pattern recognition - Source:

7 AI is an intuition machine

8 Submarines don t swim

9 2. What can it do?

10 Human-in-the-loop No human-in-the-loop Adaptive Hard-wired Assisted Intelligence Help perform tasks quicker or better Augmented Intelligence Augment human intelligence, and learns from interactions Automation Manual, repetitive tasks Automation Intelligence Can adapt and act autonomously Adapted from source:

11 Natural Language Audio & Speech Machine Vision Navigation Visualization Sensory Layer Robotic Process Automation Deep Question & Answering Machine Translation Collaborative Systems Adaptive Systems Behavioural Layer Knowledge Representation Planning & Scheduling Reasoning Machine Learning Deep Learning Cognitive Layer Statistics Econometrics Optimization Complexity Theory Computer Science Game Theory Foundational Layer Adapted from source:

12 Non-industry example Non-industry example Scholarly Comms example Classification Anomaly detection Clustering Continuous estimation

13 Non-industry example Non-industry example Scholarly Comms example Recommendations Ranking Other optimization Data generation

14 3. What should I do?

15 Don t invest in AI! Invest in a business problem

16 Use in the core parts of value chain Predict future trends, sales, R&D Produce products more efficiently Promote right products, people, & time Provide an enriched, personalized UX Editorial & Production Sales, Marketing, Discoverability

17 Top line effects Changing sources of value Bottom line efficiencies

18 Competitive advantage Data

19 Partnerships Will need to be more creative and open Because most AI talent sits with vendors Requiring different relationship structures

20 Buy or build? How essential the data is for competitive differentiation Build Buy How unique your data set is vs vendors

21 McKinsey s 5 aspects of a successful AI transformation 1. Have a business case 2. Build a data ecosystem 3. Get the right tools and techniques 4. Integrate throughout your workflows 5. Adopt an open culture and reskill the workforce

22 What if we think of AI as an AI-lien pentopus?

23 Get the right business case

24 Build a data ecosystem

25 Make sure you have the right tools, people, and techniques

26 Integrate throughout your workflows

27 Adopt an open culture and reskill the workforce

28 Summary 1. AI can do a lot of stuff 2. But you need a business case, and AI is not always the answer 3. We need to think differently about competitive advantage, partnerships & vendors 4. And change our organizations structure, people, processes, data 5. Because the way businesses and research work is going to look very different

29 Thank you! Isabel Thompson