Q1. When is the AISWITCH™ framework most relevant- in contexts of development or post deployment, of enterprise AI solutions?
90% organizations have already done some successful POCs on AI and intelligent, integrated automation. But most of them are facing challenges in scaling them up in production, without uniform runtime governance and management best practices and processes frameworks. AISWITCH offers a lean and comprehensive framework, starting with organizational AI strategy & financial management, AI workforce & CoE management, information/ data and technology management and culture change levers and ultimately the outcomes metrics.
10% of organizations that have moved some of their major AI initiatives into production are also still facing major challenges in terms of scaling up usage and RoI, value & outcomes measurements, culture change, information security and quality issues, workforce upskilling & adoption issues. For these organizations, AISWITCH helps them assess current-state realities across people-process-technology and business dimensions, and help them drive specific initiatives to improve the enterprise AI effectiveness scenario, in critical areas.
Net net, AISWITCH offers a 360 framework for AI-automation management, across tools/techstacks and beyond just tech parameters in development to deployment, integrating technologies and data dimensions to other critical aspects of AI-automation, such as strategy, workforce, culture and most importantly- business value metrics. That way, it's relevant across all the lifecycle phases of enterprise AI-automation solutions.
Q2. If an organization already has platform-specific governance tools and orchestrators, what additional value will AISWITCH enable for them?
The analogy between ITIL and AISWITCH is most relevant in the context of this question.
Before ITIL evolved, every tech-stack across infrastructure and apps management layers had vendor-specific ITSM engines and orchestrators. There were 2 main challenges with that fragmented IT operations approach:
1- There were no uniform processes e.g. to manage IT strategies, financials, service requests, SLAs & OLAs, incidents, changes, capacity, releases, configurations etc. Each vendor was performing each process in their own way. As a result, predictability and controllability were non-existent, and many enterprises had to take a 'Total IT' approach i.e. everything from one/ two vendors, thereby having lock-in's and no flexibility in negotiations, basically falling prey to TSP oligopolies rather than taking best-of-breed advantages from newer, more agile and innovative tech vendors.
2- Consequently, there were no integrated information and control flows between processes and tech-stacks. The entire IT operations landscape across different infra and apps stacks from different vendors, resembled fragmented tech islands that had no common bridges and handshake points.
Current state of AI-automation implementations in organizations resemble pretty much similar situations. Different RPA, IA, AI vendors have created their own islands within enterprises, with their own administrative stacks. It’s next to impossible to ensure and drive uniform process definitions, data definitions, integrated e2e intelligence, controls and visibility, across these islands.
AISWITCH is the common process framework that builds the bridges across the tech-individualistic AI-automation islands, ensuring that all these adjacent tool-stacks function in an integrated, dependency-aware manner.
How does AISWITCH relate to ITIL and DevOps?
Q3. How does AISWITCH™ help when an organization already has an AI COE?
Organizations with an existing AI CoE are obviously better positioned to leverage AISWITCH as they don’t have to start from ground zero. Their AISWITCH journeys will include:·
Q4. Who should be looking towards AISWITCH™? The technology folks or the business/corporate leaders?
Both sides need to play the enterprise AI-automation game as ONE TEAM and not against each other. AISWITCH will enable this collaboration by ·
Q5. I already have an AI team. How and why should I go with AISWITCH™?
Simply for 5 reasons:·
Q6. Will AISWITCH help us frame our AI policies and strategies with example templates?
Yes, these are available as part of the AISWITCH AI-automation Strategy Management Processes.
Q7. How will AISWITCH help us in scaling up adoption and organizational change management and culture change?
The AISWITCH AI Culture Management Processes cover 5 key elements of organizational change levers and culture shift gears, in terms of
1- measurable awareness building,
2- measurable effective communication,
3- organizational structural changes- new roles-skills maps, training & capability maps, RACI matrix, AI-outcomes focussed KRA-KPIs
4- behavioural metrics along with performance metrics - for AI-automation builders, operators and users within the organization and partner ecosystems
5- measurement of effectiveness of the change and culture shift programs.
Examples, templates and best practices are available under each process covering combinations of these 5 key dimensions of organizational change and culture shifts.
Q8. What is the connection between AISWITCH and agile methodologies?
Agile, DevOps and automated, high-speed release management are de facto practices in AI solutions development lifecycle phases. They are equally relevant post deployment too, e.g. for
- across different phases through their runtime lifecycles.
There are already numerous agile and DevOps based tools, technologies, approaches and platforms available to manage AI solution development phases (Kubernetes, Jenkins). But there aren’t any uniform/ consistent, integrated, universal AI-automation run-time management and governance processes available.
The 30 processes of AISWITCH, across the 6 key dimensions of AI strategy, workforce, information, technology, culture and human-AI augmentation management offer such a framework. These processes are:
In all of these 30 processes, speed is a major attribute in all the process metrics definitions, e.g.
All these metrics are agility-focussed. Hence, agile techniques are the most effective approaches towards running all the AISWITCH processes.
Q9. Will AISWITCH make an organization future-ready in terms of AI policies and evolving standards of ethical enterprise AI governance, regulations and compliance audits?
Yes, it will improve preparedness and maturity of an organization in terms of ethical and risk-managed AI usage, as the framework predictively factors in key considerations of evolving AI-IA standards and major government policies across the world.
Q10. Does the AI CoE of an organization have to implement all the 30 processes across 6 dimensions in AISWITCH?
NOOOOO!!!! This is another key difference between AISWITCH and ITIL that, although prescriptive, AISWITCH processes have interrelations not interdependencies, with some key exceptions (e.g. Data & Tech governance mechanisms & processes MUST be there, for the assets and lifecycle management processes to work). Otherwise, how the processes will be prioritized and adopted will entirely depend on the current realities, priorities and challenges of the AI-adopting organization.