Seizing the Machine-Learning Opportunity How to define and implement your AI strategy

Transcription

1 Seizing the Machine-Learning Opportunity How to define and implement your AI strategy Tulevaisuuden tuotekehitys Helsinki, May 24, 2018 Ulla Kruhse-Lehtonen CEO, Co-founder Tel Dain Studios Dain 2016 Studios 2016

2 Ulla Kruhse-Lehtonen CEO, Founding Partner, DAIN Studios Vice President, Consumer Analytics and Insights, Sanoma Director, Consumer Analytics, Nokia Management Consultant, Accenture, XLENT, Nexus Economist, Labor Institute for Economic Research PhD, Economics, Helsinki School of Economics Information Leader of Year 2013, Finland One of the 32 coolest and most influential women in Nordic tech by Business Insider Nordic,

3 DAIN Data + AI+ INsight From AI Strategy to Execution Team of Data Scientists / Engineers 8 PhD 2 years old 3 Studios Helsinki Berlin Munich 20+ Clients 14 Industries 5 Countries Own AI products Travel AI Smart Recruit Dain Studios 2018

4 Data Scientist. The Sexiest Job at Sanoma. 4 Source: Sanoma Strategy 2013 Dain Studios 2018

5 Growth provides challenges and opportunities for all market players

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7 Going beyond the hype...

8 Machine-learning applications across industries

9 Leverage data for existing business optimization as well as for new business New Business Data Partnerships Data as a Business Collaborate with external partners. Exchange data to enable new offerings or business models which would not be possible alone Provide 3 rd parties with access to your data assets, insights, and/or analytical capabilities to enable them to grow and improve their business (e.g. Data Market Platform) External Data Internal Data Utilize external data sources to enhance your own data asset to enable further optimization of business processes Combine internal data to further optimize existing business and processes to enable new offerings Business Optimization Business Optimization Current Business

10 Data, Analytics, and AI play a significant role in the development of intelligent products and services Source: Eric Rice, 2011

11 Data and AI change the way how you develop products Example self-driving cars EXAMPLES Tesla way of development More than cars deliver data BMW way of development Close to <50 cars deliver data

12 Unleash operational inefficiencies EXAMPLES

13 Business models are changing EXAMPLES From: To: Source: Siemens Mindsphere

14 Foundation of Smart Cities, Factories, Ecosystems EXAMPLES

15 It starts with a vision It all starts with a vision What are the business goals want to achieve? Where do I want to go with my business? What are my prioritized use cases to get there?

16 Define the ambition level for data Example Moderate Use data for the optimization of your current business Ambitious Data used for current business, product development, and new business areas Data seen as an enabler Mainly internal data used Focus on core business No/limited commercialization of internal data Data seen as a strategic asset Use internal and external data for differentiation Own market seen widely APIs enable data as a business and data partnerships

17 KPIs Data Potential Objectives Touchpoints Identify, define, and prioritize the AI opportunities EXAMPLE Customer Journey / KPIs / Use Case Mapping AI & Data Opportunities Discover Consider Prefer Get started Use Extend use Upgrade Appeal Guide Reassure Serve Support Enable Ensure longevity Quick wins Always on, always serving our customers, and continuously engaging the market with the Nokia brand and products. Most lucrative brand Desired products Best shopping experience. No hassle onboarding Best ownership experience Best customer care Brand advocacy high Efficient Consistent omnichannel experience conversion profiles engage users Optimize Capture user Understand and Retention, upsell Upgrade, resell marketing Efficient in sales and marketing, continuously collecting valuable customer insights for product design & our partners # Reach (organic, paid) # conversion # activation # active devices #uniques (own sites) # basket value # NPS # usage index* * # Engagement # accounts (ecom) # conversion* # NPS # Likes # NPS store # conversion* # newsletter registrations # accounts (Care) Dimensions: Overall, Country, touch point, Device model # number of care cases # sentiments Data Potential + User Volume Total Data Value Data Potential is driven by the following: Relevance of data for energyservice provisioning High business impact potential Utilization in many use cases The quality of the data is high User Volume is driven by the following: Number of accessible users and/or devices (out of total customer base) Number of user/device records The Total Data Value describes the data value opportunity assuming that value creation is realized via analytics and automation, and the use cases are successfully integrated into relevant business processes low low Data Integration Effort* high

18 Assess your company s analytics maturity level and set a target state EXAMPLE Source: Davenport & Harris, 2007

19 Embedding analytics into business processes Analytics/AI Modeling Spearheads Service Roll-Out It is easier to roll out purely technical data products (e.g. recommendation engines) than products that involve people having to change their way of working (e.g. marketing automation).

20 Leadership, governance, and a right incentive scheme drive the change toward a data-driven organization Leadership Set the vision and drive direction ensure continuation Thought leadership Data is a strategic asset for future business Governance Drive and steer implementation of vision and strategy across the company Resolve conflict of interest or trade-offs Incentive Provide motivation for whole organization to head towards common direction Measure progress along defined KPIs Incentivize data-driven innovation

21 Data success is not only about data, analytics, and technology: How to drive an analytics culture? Break down silos Lead from the top AND the middle Sharpen the business strategy Execute effectively Silos are culture killers Ensure data capabilities are used in the most beneficial areas for the company Most impactful analytics requires cross functional collaboration Requires full leadership attention Build passion for data-driven decision making Upskill workforce in analytics Derive AI strategy from business strategy Define common metrics and incentives for whole organization No opt-out allowed for strategic targets Utilize defined KPIs for systematic implementation Take action - learn and iterate BUT understand that analytics is a journey

22 Company Personas 22

23 We have identified six company personas (A highly unscientific presentation) Black Box Optimist Details, Details Pessimist No Rush Covering our Backs Smart

24 The Black Box Optimist Company rationale: We are behind and need to do something quickly Machine learning is so complicated Let s get a tool where we can dump our data in and get insights out it will cost money, but then it s done Challenges: Overestimate the possibilities of technology Underestimate the impact on organization, required competences, process changes

25 Details, Details Company rationale: First, we need to have the business strategy, digital strategy, technology strategy, and marketing strategy in place We need detailed business case calculations for the next 5 years in place before we can start with execution All roles and the organizational structure need to be defined and approved Challenges: Drowning in complexity Stagnate and lose time by answering questions you only will be able to address when you get going

26 The Pessimist Company rationale: We don t have anything (no people, no data,...) These kinds of projects always fail ( the project that shall not be named ) Let s start with something very small that doesn t disturb our core business Challenges: Hard to make a business difference as use cases siloed, manual, and small Company-level transformation not happening

27 No Rush Company rationale: At some point, we will probably need to ramp up our data capabilities, but our business is going well so let s not rush anywhere Anyway, how would someone from the outside be able to tell us what to do? We are the experts of our business... Challenges: Past success is no guarantee of future results; total unpreparedness is dangerous Individuals fear that data and analytics will challenge their role / position

28 Covering our Backs Company rationale: Let s hire McKinsey/BCG/IBM/Accenture/ because they know what to do If it fails anyway, we don t get the blame as we used the big, expensive consultants Challenges: Trying to outsource the thinking (and execution) instead of getting own hands dirty and leverage own domain knowledge Not learning as an organization

29 Smart Company rationale: We want to be leaders in digital transformation Our products and services need to be intelligent and adoptive Our sales and marketing processes need to be based on solid customer understanding We are not afraid of change; we embrace the journey Challenges: Take the whole organization onto the journey Satisfy the stock market

30 Key takeaways 1 Don t outsource thinking you need to define your Business Opportunities according to business priorities. 2 AI is a journey you need to build a analytics culture across the whole company led by the leadership to make a difference. 3 People first you don t need to know exactly where to start but get a couple of good people and place them wisely to start specifying things

31 Recruiting Data Scientists (and other data people) 31

32 AI and Big Data mean new roles for a company Business / Data Science Legal Technology Analytics Strategist Data Scientist Consumer Data Privacy and Protection Officer Data Steward/ Custodian Solution Architect Data Architect Big Data Engineer Database Developer

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34 Do s and Don ts in AI Execution - Summary DO Invest in people: recruit key persons and improve the analytics skills of business people Build passion for analytics at every level of the organization Find common ways of working together across organizational silos ensuring end-toend delivery of results Define common metrics that guide strategic and operational decisions DON T Expect immediate ROI. Analytics transformation is a long-term effort Do not believe that technology resolves the business questions of your company Outsource data and analytics to one company function Allow individual departments to opt out of strategically important implementation targets

35 Ulla Kruhse-Lehtonen CEO DAIN Studios Finland, Co-founder Mobile: Helsinki Berlin - Munich