AI-SWITCH PRACTICE RESEARCH REPOSITORY

AI STRATEGY MANAGEMENT

AI INFORMATION (DATA) MGT

AI WORKFORCE MANAGEMENT

Best Practices & Templates related to Enterprise AI-automation Strategy: Charter, Partner Selection, Risk-returns

AI WORKFORCE MANAGEMENT

AI INFORMATION (DATA) MGT

AI WORKFORCE MANAGEMENT

Best practices for AI-automation workforce management: Upskilling on non-tech skills like design thinking in AI

AI INFORMATION (DATA) MGT

AI INFORMATION (DATA) MGT

AI INFORMATION (DATA) MGT

Must-do practices & templates for AI-automation data management: Debiasing, relevance, security management

AI INFO MGT (DATA)

AI TECHNOLOGY MGT

AI CULTURE MANAGEMENT

AI INFORMATION (DATA) MGT

Examples of generic architecture templates for AI-automation solutions design

AI CULTURE MANAGEMENT

AI CULTURE MANAGEMENT

AI CULTURE MANAGEMENT

Enabling key culture-change levers for AI-automation: Communication frameworks

AI CULTURE- COMM

AI-HUMAN AUGMENTATION

AI CULTURE MANAGEMENT

AI CULTURE MANAGEMENT

Best practice templates & metrics examples, balanced scorecards, RoI reporting systems

AI-HUMAN AUGMENT

S of AI-[S]WITCH: Strategy Management of AI-automation

The first S of AI-SWITCH (IP) is Strategy- which is the first place to start, for any enterprise embarking on the AI-automation journey. Strategy management includes: Partner assessment, current-state maturity benchmarking, failure mode analysis, AI-automation policy-making & governance models, prioritization of initiatives/ use-cases, cost estimation & RoI projections, and journey-mapping.

AISWITCH_2021 CALM3 AI LEADERSHIP FOR AGILE AI STRATEGY (pdf)

AISWITCH_2021_AI MVS CALM1 TOC CONSTRAINTS (pdf)

AISWITCH_2021 CALM4 AI MVS AGILE STRATEGY (pdf)

AISWITCH_2021 ENTERPRISE AI STRATEGY EXAMPLES (pdf)

AISWITCH_2021_AI STRATEGY MAP TEMPLATE (pdf)

AISWITCH_2020_AI STRATEGY COST FOCUS DIFFERENTIATION (pdf)

AISWITCH_2020_AI STRATEGY HOW TO USE MCKINSEY 7S (pdf)

AISWITCH2020 How to Evaluate AI implementation Partners Objectively with 5 Next Practices (pdf)

AISWITCH2020 Ethical AI Manifesto- 12 Principles (pdf)

AISWITCH2020 AIOps Maturity Model (pdf)

AISWITCH2020 Why AI fails (pdf)

W of AI-S[W]ITCH: Workforce Management for AI-automation

The W of AI-SWITCH (IP) is Workforce- the most critical lever in an organization's AI-automation machinery. Workforce management for AI-automation deals with the challenges of AI-automation CoE planning, organizational structure, conflict management, skill scarcity/ skill-gaps, digital reskilling requirements, training & certification plans, new KRAs & KPIs for both tech roles and business analysts/ business user roles. These are the critical steps within AI-automation workforce management. Following best practice research notes briefly describe these processes & applicable frameworks.

AISWITCH_2020 REVERSE MENTORING MUST FOR AI RESKILLING (pdf)

AISWITCH2020 AI and Design Think- The winning techniques combo (pdf)

AISWITCH2020 EFAN Eliminate First Automate Next (pdf)

NASSCOM Indiaai 21 in 21 book excerpts (pdf)

I of AI-SW[I]TCH: Information (Data) Management for AI

The I of AI-SWITCH (IP) is Information (Data)- which is literally the Eye of the AI storm. Data quality vs. quantity is a major issue- be it process related data- discovered and mined by process mining tools like Celonis, or the training, test and validation datasets for different AI usecases, e.g. structured, parametrics or non-parametric data, to unstructured data like text corpus and image databases, to hybrid data. Apart from the well-researched noisy data and sparse data issues, a critical question of data quality is the presence of multiple types of biases in training & test data. There are also the questions of data relevance and less-data scenarios, e.g. post-COVID, many of the underlying assumptions have changed in case of historical databases and data patterns - from economic forecasts to stock markets to healthcare to pharma to manufacturing to supply chain datasets.

AISWITCH_2021 EVOLVING AI STANDARDS (pdf)

AISWITCH_2020 EXAMPLES OF EVOLVING GLOBAL AI TECH-DATA GOVERNANCE STANDARDS (pdf)

AISWITCH_2020 HOW TO DEBIAS AI- 15 TYPES OF DATA BIAS- AI360 AEQUITAS (pdf)

T of AI-SWI[T]CH: Technology Management for AI-automation

The T of AI-SWITCH (IP) is Technology- the critical underlying tech-stacks, from AI-optimized chip to the APIs, that power and run the AI-automation solutions for businesses. Managing the tech-stacks for AI-automation requires a drastically different view about the key assets, given that the assets include datasets, APIs, dedicated sandbox environments for testing, runtime infrastructure which is often a mix of cloud and on-prem. They require highly secure infrastructure which is often hardcoded at the platform level e.g. INTEL SGX. The architectural dependencies between assets are complex and spaghetti-type. Hence, the AI-automation asset catalogues have extensively different structural requirements. So do the process models for AI-automation major incident management, capacity management, SLM, change & config management, availability, reliability, continuity & security management.

AISWITCH_2021_RESEARCH_BLOG AWSINDIASUMMITSTORY (pdf)

AISWITCH2020 THE GENERIC 7-LAYER ENTERPRISE AI (pdf)

AISWITCH_2021 EVOLVING AI STANDARDS (pdf)

AISWITCH_2020_WHY AI IS NOT IT (pdf)

C of AI-SWIT[C]H: Culture Management for AI-automation

The C of AI-SWITCH (IP) is Culture- which is universally accepted across all major surveys of enterprise leaders, as the Most Difficult Problem that's accentuating the AI-automation adoption & scale challenges. Contrary to how most of us feel, more often than not we the practitioners find that technology is the least of the problem, and Culture eats not just strategy but technology, information, workforce reskilling plans- pretty much everything- for breakfast, lunch & dinner. Changing the organizational culture, through processes and interventions, no matter how good the internal or external Change Masters are, is much easier said than done. But communication plays the most important role as a culture change lever, no one can deny that.

AISWITCH_2020 DESIGN FOR TRUST DfT FRAMEWORK FOR AI (pdf)

AISWITCH2020 CUDA- A Value Communication Framework (pdf)

H of AI-SWITC[H]: Human-AI Augmentation Metrics Management

The last H of AI-SWITCH (IP) is Human-AI augmentation metrics. The ultimate challenge of AI-automation implementations are to predefine the target outcomes and then measure the post-implementation impact metrics and declare the initiatives either as successes or failures. The most effective human-AI augmentation metrics are always focussed more on strategic outcomes, impact and value, than purely based on operational cost reduction. A balanced approach towards the AI-automation outcomes metrics, makes the claims of Wins credible, data-driven and evidence-proved.

AISWITCH2020 BUILD AI-AUTOMATION BUSINESS CASE WITH BALANCED SCORECARDS (pdf)

AISWITCH_2021_AI STRATEGY MAP TEMPLATE (pdf)