AI... Hvordan kommer vi så i gang?

Transcription

1 AI... Hvordan kommer vi så i gang?

2 Today s Agenda - Find value for AI - Realize value of AI - Make most of the Data Scientist Client Carsten Brøns Andersen Data Scientist Oscar Petersen Consultant Tobias Laursen - Drive the change Director, Business Development Koda IBM Client Innovation Center IBM Client Innovation Center

3 ARTIFICIAL INTELLIGENCE Understand, reason, learn & interact Services, APIs & new tricks To which extent, rather han is it

4 ORGANIZATIONAL STARTING POINTS EXECUTIVES Strategy PRINCIPLE OF VALUE CREATION MANAGERS Use Cases DATA SCIENTISTS Data COST Optimize REVENUE Create WHEN AI COMES IN Volume of data Volume of processing Volume of hands

5 Ideation Prototype MVP Scale AN INCREMENTAL INNOVATION A MODEST INNOVATION A RADICAL INNOVATION

6 Capabilities Culture Sponsorship Bad data Scalability Lack of data Building trust Operating Model Ownership

7 Deal in Days

8 CUSTOMER EXPERIENCE Holistic ANALYTICS approach to AI DATA TECHNOLOGY ORGANIZATION

9 Ideation Prototype MVP Scale AN INCREMENTAL INNOVATION A MODEST INNOVATION A RADICAL INNOVATION

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11 What is Data Science? Expectation Magic Data Money-Making AI Reality Blood, sweat and tears

12 What a Data Scientist can do A Well-Formulated Business Proposition Ability to convert the business proposition to a mathematical problem Data at disposal Magic (But mostly blood, sweat and tears)

13 Translating a business proposition

14 Part of a Bigger Puzzle BUSINESS CONSULTANT DATA ENGINEER DATA SCIENTIST DEVELOPERS END-USER! BUSINESS PROPOSITION RAW DATA INGESTION MODELING DEPLOY VALUE UX DESIGNER ENSURE RELEVANCY & ADOPTION

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16 What I tell people to do Marker billedet for at indsætte alternativt billede Define Design Deliver Business Ambition Ideal Solution Prototype & Anchor

17 What we did Marker billedet for at indsætte alternativt billede Technology Change Scale Validate use-case. Feasibility & Value Win the hearts and minds Build on the good story Max 40 Top Score (30-40) Total Score 32 Med 24 Average (19-29) Min 8 Lowest score (8-18) Measuring Criteria Scoring Weight Score Comments 1 = No experience with use-cases similar to ours Nine recently finished PoC with a very similar use-case which is now being implemented (Danish labour market supplementary pension). Besides from this they have done Level of Experience 3 = 1-2 references with use-cases with similar technology but different use-cases 100% 4 several machine learning projects. 5 = Multiple (+2) use-cases with similar use-cases Nine has assigned a developer with solid experience in a lot of areas, but he has not been a part of the most recent ML project. Besides from the developer, two experts have been assigned in consulting roles. Project manager with solid agile experience. Assigned Team 100% 3 1 = 1 inexperienced junior developer with no practical experience with ML, limited senior support and agile experience Developer: 365 hours 3 = 1 experienced ML developer with agile experience but no experience with a similar use-case and some specialist support Experts: 65 hours 5 = 1 experienced ML developer who has worked on a similar use-case, has agile experience and access to specialist support Project manager: 80 hours 1 = General POV on AI/ML but limited ability to frame it in our context Nine has demonstrated a solid knowledge of not only ML but also supporting and enabling technologies. They work from a perspective of deploying ML as a future Technology Knowledge 3 = Clear understanding of relevant ML technology in our context (and proven track- record) 100% 5 component of a micro-services environment. Nine understand the Polaris societies business models (based on prior engagement with Koda) and understand both the pilot 5 = Clear understanding of relevant technology, POV on suitable alternative and complementary technologies as well as potential future use cases. 1 = +1m DKK DKR (~ ) Price 3 = 750k-1m DKK 70% 5 Nine's price is fair based on full-time on-site development, support from 2 SMEs and at least 1 "shark-tank" workshop with their CTO. The pricing is done based on Nine's 5 = 350k-750k DKK standard rate card. 1 = Conceptual design only. No defined technologies and no defined service model Nine have done a good job of presenting a high-level conceptual solution design. They have suggested a technology stack consisting of Elastic (index search for data), Docker Product and Solution 3 = Conceptual design with suggested technologies that need to be verified based on clearly defined methodology. Loosely 80% 4 for containerization, kubernetes for scaled deployment & management and finally, Microsoft Azure for hosting. The machine learning algorithm itself will be based either Design defined service model design on Microsoft tools or Elastic's X-Pack. Nine is focused on either low-cost or Open Source tools for building a multi-purpose ML solution. 5 = Clearly defined solution as well as parameters that need to be tested. Clearly defined service model 1 = Unclear project activities/steps and unclear or no definition of agile methodology Project Approach 3 = Clear project activities/steps but unclear or no definition of agile methodology (or vice versa) 90% 5 Clear agile pilot plan with 5x2 week sprints and bi-weekly demos. Nine have specified the major activities for all sprints. 5 = Clear project activities/steps and clear agile methodology 1 = No clear definition of future service model Proposed Service Model 3 = Conceptual model with indicative metrics, SLAs and KPIs 70% 3 Nine have outlined general conditions and models that have been used for other customers but they have not proposed a suggested model for future service consumption. 5 = Clear suggested service model including pricing structure, staffing, service management principles and governance 1= No description on how to handle KT Knowledge transfer is incorporated in the pilot plan as a continuous activity with biweekly demos, as well as 1 week handover phase at the end of the pilot period. Nine Knowledge Transfer 3 = High-level mentioning of KT as a component in the pilot 70% 5 envision that both Polaris and Nine team members will take ownership for central parts of the process. 5 = Clearly defined KT activities, learning objectives and check-points 1 = No defined vision for future of the solution Vision for Solution and Nine have demonstrated a clear understanding of what potential future use-cases could look like. Beyond the obvious matching cases (radio, online, etc.), Nine also 3 = A description of the envisioned v2 of the proposed solution 25% 5 Service considers opportunities in the analytics and predictive space. 5 = A visionary outline of both v2 of the proposed solution as well as potential other adjacent and complementary options 1 = No support in DK/NO/FIN Nordic Coverage 3 = Only presence in DK, NO or FIN but with ability and willingness to travel 90% 2 Nine only have presence in Denmark but are willing to travel. 5 = Local presence and relevant capabilities cross-nordic Total Score 32

18 What we gained Marker billedet for at indsætte alternativt billede Hands got dirty Delivery model of tomorrow Began our AI journey

19 Where we re going next Marker billedet for at indsætte alternativt billede Getting Strategic Operating Model Scaling across Business

20 Strategy Organizational Momentum Ops Model Ownership Sponsorship Change Agents New Tech Project Delivery Model Solid ROI 1 Good Use Case

21 1 2 Build Capabilities AI is less about AI