Future of AIOps Summit, February 2022

1. AIOps systems integrate big data, machine learning, and analytics to improve IT operations by providing proactive and personal insights through monitoring, automation, and service desk tasks, while also allowing the usage of numerous data sources and data gathering methods. In theory, AIOps can reduce the expenses associated with IT concerns by providing faster remedies to outages and other performance issues. Big data and machine learning are combined in AIOps to provide predictive outcomes that help speed up root-cause analysis (RCA) and reduce mean time to repair (MTTR). Your ITOps can improve over time by providing intelligent, actionable insights that support a better level of automation and cooperation, saving your company time and money. AIOps solutions decrease the flood of alerts that inundate IT teams by learning over time which sorts of warnings should be given to which teams, decreasing redundancy and, as a result, boosting an IT organization’s capacity to be a valuable business partner. An IT operations platform with built-in AIOps capabilities can assist IT operations in proactively identifying and correcting possible issues with the services and technologies it provides to a business.

2. High Performance Operations with Human Centered Artificial Intelligence, HCAI Jose de Francisco, Chief Designer I Nokia CNS, Cloud & Network Services AI is not only revolutionizing commonplace enterprise operations and fostering digital transformation, but also transforming AI’s own design principles, and agile development and delivery practices in the process. Human Centered Artificial Intelligence, HCAI, purposely sets-up nimbler interdisciplinary teams for success, so that brands can efficiently perform at greater scale, broader scope and unprecedented speed with customer centric practices. Principled HCAI addresses highly adaptive operativeness, observability, traceability and explainability, progressive closed feedback loop optimization, all coupled with the behaviors that elevate governance and ethics to the forefront.

3. © 2022 Nokia Driving AI with QXbD Quality eXperiences by Design Jose de Francisco, Chief Designer Distinguished Member of Bell Labs Head, Chicago Innovation Center The Future of AIOps February 15, 2022 Nokia CNS, Cloud & Network Services NOKIA VDS VENTURE DESIGN STUDIO

6. At Nokia, we create technology that helps the world act together. As a trusted partner for critical networks, we are committed to innovation and technology leadership across mobile, fixed and cloud networks. We create value with intellectual property and long-term research, led by the award-winning Nokia Bell Labs. Adhering to the highest standards of integrity and security, we help build the capabilities needed for a more productive, sustainable and inclusive world.

8. LET’s ideate & WHITEBOARD ‘The future of AIOp s ’ together

9. “Massive R&D spending goes towards (…) frontier technologies: the metaverse, autonomous vehicles, health care, space, robotics, fintech, crypto and quantum computing. Artificial intelligence, AI, is now so ubiquitous that we are not counting it as a frontier in itself.” “AI development platforms, specifically, their AutoML features (…) are becoming easier to use, primarily through the extensive provision of graphical model-based tools and a ‘configuration over code’ philosophy (…). Automation projects no longer need to be realized completely by hard-pressed, overworked IT departments (…) low-code or no-code tools enable dispersed, multidisciplinary teams to collaborate quickly and iteratively.” “The top three use cases are service-operations optimization, AI-based enhancement of products, and contact-center automation, with the biggest percentage-point increase in the use of AI being in companies’ marketing-budget allocation and spending effectiveness.” “The companies seeing the biggest bottom-line impact from AI adoption are more likely to follow both core and advanced AI best practices, including MLOps; move their AI work to the cloud; and spend on AI more efficiently and effectively than their peers.” Chui, Michael et all. The State of AI in 2021. McKinsey. December 8, 2021 Big Tech’s Private Passions. The Economist, Technology and Innovation. January 10, 2022 Ward-Dutton, Neil. The Future of Work in The Age of Cloud and Low-Code Automation. December 2020

10. CC BY-SA 4.0, Attribution-ShareAlike 4.0 International Digital transformation W H I T E B O A R D Nokia VDS QXbD: Digital Transformation Portfolio Mapping ALPHA 2022 TECH market Greenfield Brownfield Red ocean Blue ocean EMERGING legacy existing uncontested MOONSHOT LONG TERM HORIZON DESIGN FORESIGHT: • Guiding vision – future OPTIONS & states • Future proofing – pathways to get there • leadership – TREND SETTING, Leapfrogging • Obsolescence avoidance - derisking • Backengineering – rightsizing start state Remediation design: • Rework • Refactor • rethink Products & services R&D INVESTMENT (SIZE)

11. CC BY-SA 4.0, Attribution-ShareAlike 4.0 International AUTOMATICS High touch AUTONOMATION Low touch AUTONOMICS Zero touch SILOED Domains LABOR intensive +NO CODE (INTENT) +SELF-SERVICE +interdisciplinary +ANALYTICS +PROGRAMMABILITY +COMMAND & CONTROL RIGID DYNAMIC ADAPTIVE C A P A B I L I T Y L E V E L S C A P A B ILITY LEV ELS System capability modeling & maturity level canvas W H I T E B O A R D Nokia VDS QXbD: HMS Capability Modeling & Maturity Level Canvas ALPHA 2021 MACHINE HUMAN Products & services R&D INVESTMENT (SIZE)

12. CC BY-SA 4.0, Attribution-ShareAlike 4.0 International Nokia VDS QXbD: Decision Support Systems Continuum ALPHA 2021 Decision systems continuum Quality DATA Insightful ANALYTICS Actionable OPTIONS RAW MATERIAL FUEL GEARS ENGINE Resolution Relevance Recency Robustness FOUR Rs’ ACID TEST in Quality assurance Value creation feedback loop explainable DECISIONS W H I T E B O A R D

15. § “Enterprises have started adopting AIOps platforms to compete with and replace some traditional monitoring tool categories. For example, monitoring IaaS and observability is being done entirely within AIOps platforms, especially if the enterprise has its entire IT footprint in the cloud.” § “Enterprises are increasing their use of AIOps across various aspects of IT operations management (ITOM) and maturing their use cases across DevOps and SRE practices.”

16. CC BY-SA 4.0, Attribution-ShareAlike 4.0 International Nokia VDS QXbD: AIOps / MLOps Construct ALPHA 2021 W H I T E B O A R D AIOps MLOps CXOps BiZOps ITOps Customer centric AI for the enterprise IT for machine learning Value oriented Data driven Driving data quality is a function of value both value and quality are contextual & evolving human considerations Ml is a subset of ai Deep learning is a subset of ML

17. CC BY-SA 4.0, Attribution-ShareAlike 4.0 International Nokia VDS QXbD: AI/ML Landscape Mapping ALPHA 2021 W H I T E B O A R D applications platforms infrastructure services Artificial intelligence Data science Machine learning Deep learning Information technologies algorithms heuristics Business logic analytics Calibrate & optimize Gather & EXTRACT Store & ANALYZE Transform & PREPARE Distill & synthesize Experiment & LEARN Dynamic flows [S] [U] S: supervised U: unsupervised

18. CC BY-SA 4.0, Attribution-ShareAlike 4.0 International Nokia VDS QXbD: AI/ML Landscape Mapping ALPHA 2021 W H I T E B O A R D applications platforms infrastructure services Artificial intelligence Data science Machine learning Deep learning Information technologies algorithms heuristics Business logic analytics Calibrate & optimize Gather & EXTRACT Store & ANALYZE Transform & PREPARE Distil & synthesize Experiment & LEARN Dynamic flows & CHALLENGES [S] [U] S: supervised U: unsupervised OPEN LOOP? UNBALANCED DATA SETS? DATA & MODEL DRIFTING? FIT, RECENCY & RESOLUTION? BIASED RESULTS? RELEVANCE & ROBUSTNESS?

19. CC BY-SA 4.0, Attribution-ShareAlike 4.0 International QXbD DESIGN PRINCIPLES W H I T E B O A R D Nokia VDS QXbD: AI Design Principles ALPHA 2022 CONSISTENTLY SAFE, SECURE, FAIR AND ETHICAL OUTCOME ORIENTED, VALUE DRIVEN & HIGHLY EFFICIENT OPERABLE, OPTIMIZABLE, SERVICEABLE & SUSTAINABLE RESPONSIVE, RESPECTFUL, RESPONSIBLE & RESILIENT OBSERVABLE, TRACEABLE & EXPLAINABLE COMPLIANT, AUDITABLE & ACCOUNTABLE L O W MATURITY LEVEL H I G H MATURITY LEVEL DEFICIENT OPTIMAL Quality experiences by design

20. CC BY-SA 4.0, Attribution-ShareAlike 4.0 International AIOPS W H I T E B O A R D Nokia VDS QXbD: AIOPS Strategy Canvas ALPHA 2022 § Prospective Experimentation § Predictive foresight § Prescriptive / generative § Responsive resolution § Continuous improvement § Business continuity / resiliency § Time to value / acceleration § … 360 ops excellence as a SOURCE OF VALUE CREATION DESIGNOPS DEVOPS DATAOPS MLOPS BIZOPS DEVSECOPS CXOPS CEI, Customer experience INDEX Sentiment analysis Journey mapping Adaptive interaction NPS, Net promoter score … BI, Business intelligence FINTECH / BMI, business model innovation ROV, Real options valuation BIA, BUSINESS IMPACT analysis BEV, Brand equity value ... SA, service assurance (SLA) SIA, Service impact analysis (SIA) RCA, Root case analysis (RCA) VSM, VALUE STREAM MAPPING … Customer centricity Business of business Technology EFFICACY W H Y W h o + w h e r e - H O W + K P I ‘x”OPS W h a t § ON DEMAND § Self-service § SUBSCRIPTION § Smart § Secure § scalable § Agile / adaptive § Lean / efficient § Serviceable § sustainable § … XaaS Design principles +/- +/- +/- +/- +/- +/- p u r p o s e

21. CC BY-SA 4.0 W H I T E B O A R D MLOPS S y s t e m c a p a b i l i t y A n a l y t i c s s c o p e p e r f o r m a n c e o p e r a b i l i t y M A T U R I T Y 4 integral 3 pervasive 2 SCALABLE 1 emerging v a l u e competency Broad IMPACT EFFICACY CHANGE AGENT Nokia VDS QXbD: MLOPS Strategy Canvas ALPHA 2022 I N V E N T O R Y STAKEHOLDERS PERSONAS USE CASES BUSINESS LOGIC TECHNOLOGIES DATA METHODS MODELS CODE – CI/CD INFRASTRUCTURE - SRE GOVERNANCE M o d e l i n g ops excellence & success factors

22. <Document ID: change ID in footer or remove> <Change information classification in footer> © 2022 Nokia VDS I Venture Design Studio USE CASE 3/8 – dynamic clustering visualization, command & Control + closed feedback loop automation

23. CC BY-SA 4.0, Attribution-ShareAlike 4.0 International Nokia VDS QXbD: Analytics Design & Cognition Dimensionality Mapping ALPHA 2021 analytics design & COGNITION dimensionality mapping AIMING far FORESIGHT DRILLING deep Going wide hindsight insight GOING BACK ZOOMING IN/OUT (ABSTRACTION LEVELS) SINGLE PANE OF GLASS oversight

24. CC BY-SA 4.0, Attribution-ShareAlike 4.0 International Nokia VDS QXbD: Analytics Design & Cognition Dimensionality Mapping ALPHA 2021 analytics design & COGNITION dimensionality mapping AIMING far FORESIGHT DRILLING deep Going wide hindsight insight GOING BACK ZOOMING IN/OUT (ABSTRACTION LEVELS) SINGLE PANE OF GLASS oversight W H I T E B O A R D Predictive analytics Prospective models (WHAT-iF) decisioning Prescriptive analytics & Generative design Network effects & externalities Root cause & heuristics Descriptive analytics Correlation Covariance causation Observability & explainability Deductive Inductive abductive Monitoring analytics Auditing analytics Predictable patterns Hidden patters Outliers Anomaly detection Confidence & risk levels Metacognition backlog Digital twin simulation Options analysis Learning & unlearning Who What what-if Why Why-not How when

25. <Document ID: change ID in footer or remove> <Change information classification in footer> © 2022 Nokia VDS I Venture Design Studio USE CASE 4/8 – dynamic interdisciplinary correlation, inference & generative design

26. https://interactions.acm.org/archive/view/july-august-2019/toward-human-centered-ai

27. Human-Machine System Governance & Closed Loop Cognitive Decision Systems HC AI, HUMAN CENTERED ARTIFICIAL INTELLIGENCE User Modeling & Adapted Interaction (UMAI) Explainable AI (xAI) Graph Theory & Visual Analytics Generative Design Recommender Systems (RecSys) & Collaborative Filtering Human In/On The Loop Computing Natural Language Processing (NLP) Sentiment Analysis - Affective Computing Multimodal UI, Chatbots & Computer Vision Intent Based & No-Code Programming Human Centered AI © 2022 Nokia

28. Human-Centered AI (HCAI) is an emerging discipline that aims to create AI systems that amplify and augment human abilities and preserve human control in order to make AI partnerships more productive, enjoyable, and fair With an emphasis on diversity and discussion, we explore research questions that stem from the increasingly wide-spread usage of machine learning algorithms across all areas of society, with a specific focus on understanding both technical and design requirements for HCAI systems, as well as how to evaluate the efficacy and effects of AI systems. Use Cases & Experiences with AI Systems Evaluation Fairness & Bias Privacy Transparency, Explainability, Interpretability… and Trust User Modeling & Adaptive Interaction (UMAI) Human - AI Collaboration Governance Values & Ethics of AI Design Frameworks for Human Initiative and AI Initiative

29. Front & back Service stages collaborating ASSISTING ALERTING prescribing REPORTING PREDICTING COMMAND & CONTROL PROGRAMMING EXPERIMENTING interrogating communicating communicating auditing Learning / unlearning SELF-ORGANIZING HMS Informatics Modeling matrix Front & back Service stages Front & back-end technologies Self-service streamlining Protecting & securing MACRO COGNITION I N I T I A T E S Front & back-end technologies H2H M2H H2M M2M CC BY-SA 4.0, Attribution-ShareAlike 4.0 International Nokia VDS QXbD: HMS Informatics Modeling Matrix ALPHA 2021 SELF-SERVICING W H I T E B O A R D

30. CC BY-SA 4.0, Attribution-ShareAlike 4.0 International Nokia VDS QXbD: HMS Design Dimensions ALPHA 2022 W H I T E B O A R D psychological Physiological sociological Emotional intelligence Sentiment cognitive load Situational awareness (SA) Sense making (SM) ergonomics Form factors PHYSICAL environment Analytical skills Team macrocognition culture Organizational behaviors interfaces Decision making memory learning biomechanics biometrics prosthetics exoskeletons Value SYSTEM identity Sensory systems perception teamwork artifacts … +/- … +/- … +/-

31. CC BY-SA 4.0, Attribution-ShareAlike 4.0 International Nokia VDS QXbD: HMS Informatics Operating & Performance Models ALPHA 2022 W H I T E B O A R D PERFORMANCE ASSESSMENT PREDICTION POSITIVE NEGATIVE OUTCOME POSITIVE NEGATIVE PRECISSION SENSITIVITY SPECIFICITY ACCURACY POSITIVE PREDICTIVE VALUE NEGATIVE PREDICTIVE VALUE ACTIVE ERROR ERROR latent ACTIVE latent dissonance fluid precision certain uncertain Procedures conditions Machine led HUMAN GUIDED MACHINE ASSISTED Human led Operating model MATRIX PAST CURRENT INTERIM FUTURES Model stability / drifting levels True positive True negative

32. <Document ID: change ID in footer or remove> <Change information classification in footer> VDS I Venture Design Studio INTENT BASED

33. <Document ID: change ID in footer or remove> <Change information classification in footer> © 2022 Nokia VDS I Venture Design Studio USE CASE 6/8 –form factors & human-machine interaction in operations management

36. W H I T E B O A R D “The only way to discover the limits of the possible is to go beyond them into the impossible” Arthur C. Clarke “in order to attain the impossible, one must attempt the absurd” Miguel de Cervantes “the best way to predict your future is to create it” Abraham Lincoln “it always seems impossible until it’s done” Nelson Mandela

37. Driving AI with QXbD Quality eXperiences by Design The Future of AIOPs February 15, 2022 Jose de Francisco is the Chief Designer at Nokia CNS, Cloud & Network Services, and Head of Nokia’s Chicago Innovation Center, a leading R&D facility integrating all of the company’s business groups. His professional experience encompasses interdisciplinary leadership responsibilities in strategy, product management, research, design, new ventures and product marketing. Award-winning designer and a Distinguished Member (DMTS) of Bell Labs for work on next generation mobile platforms and applications, Jose holds several active patents and an extensive design portfolio featuring 20+ global brands. He has served with the Advisory Boards for MIT’s Institute of Data, Systems and Society (IDSS) and Illinois Tech’s Entrepreneurship Center. Jose is currently engaged with the think tank behind the premier Design Thinking conference series in the United States. He holds several professional certificates in Design and Data Science from MIT, earned an MBA in International Marketing and Finance from Chicago’s DePaul University as a Honeywell Europe Be Brilliant Scholar, and is the recipient of postgraduate degrees in Human Factors Engineering and Business Administration from BarcelonaTech (UPC) and Ireland’s University College Dublin (UCD) respectively. He started his academic life in the Industrial Design program of Barcelona’s Massana Art & Design Center as an Epson Scholar. Passionate about innovating to create new value, Jose co-authored the ‘Human Factors Engineering Manifesto’ and believes in the exceptional value that comes with consistently delivering ‘Quality Experiences by Design.’ His endeavors can be followed on Innovarista.org.

Future of AIOps Summit, February 2022

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