The AI Manifesto For Executives &#038; Why It Should Be A  Strategic Imperative
In the previous article of this series, I discussed how AI is at the heart of the digital transformation, can reap benefits across the value chain for industries spanning 3 pillars Redefine the Business ModelRe-evaluate the Value ChainRe-imagine the Customer Journey However, while AI is the current “ industry buzzword” for industries, the actual enterprise wide AI roll out across industries is quite low. That brings us back to the question : Where should an organisation get started on its AI journey? What should be the approach to scale up AI capabilities in order to become an “AI driven Enterprise “ and reap the maximum benefits out of it? There are 4 guiding principles that will help executives formulate their enterprise wide AI manifesto to enable a smooth adoption and prepare them for the road ahead 1) An executive should sponsor and drive AI adoption: Becoming an AI driven enterprise requires executive commitments at a time when the ROIs are still unclear. In most organisations there is a lack of clarity on who will be responsible and accountable for AI rollout, adoption and capability building. In most cases it is limited to the CIO or Chief Data and Analytics officer, with most of the initiatives being conducted in a siloed manner within his/her team. A majority of   these initiatives fail because of inadequate buy-in from the entire organisation. AI has the potential to bring in strong returns on investment if it is at the heart of all the transformation initiatives. The executive needs to align the AI initiatives with the organisation’s laid down strategic objectives e.g. Optimise processes to improve effectiveness and efficiencyBecome the “clear choice” for customers by providing superior digital customer experienceBecome a leader in employee engagement, etc. 2) Define FAST goals to successfully execute pilot projects:  Small sets of priorities should be identified to brainstorm on the first few initiatives for AI pilot in order to test the efficiency, efficacy and applicability of AI in the organization. The executives should build a steering committee team to drive the identified priorities by forming small, cross-functional agile teams to execute the pilot. Independent AI advisors, typically from organisations, who have been successful in AI adoption, should be a part of the steering committee in order to guide the organization on the best practices. 3) Define Strategy & Execution Roadmap: Defining an all-encompassing AI strategy which will meet the strategic priorities will not be possible till the organization understands the lessons learned and challenges faced from the pilots. Some of the key factors that need to be considered while defining AI strategy are : Identify ways to curate data and create “Data as an Asset” to enable the AI strategy Instead of trying to do everything, channel your focus on AI use cases Disruptive : Rethink core products and offer breakthrough solutions to existing business problems through AI Non-Disruptive : Identify and seize new opportunities using AI Devise a short term/long term execution roadmap. Prioritise initiatives based on benefits realized, complexity of implementation and potential risks , with a comprehensive benefit realization framework to assess the success of the initiatives.Ensure alignment and complete buy-in of the AI strategy from the Senior and Middle Management.Identify AI use cases to develop in house and the ones to re use by building a sustainable partner ecosystem with AI startups.Develop consistent standards for up-skilling employees and defining KRAs for AI adoption across all levels of the organization. 4) Build Enterprise wide AI Capabilities External AI coaches/advisors, typically from the organizations who have successfully embraced AI to create business value, should be engaged to train cross-functional teams. Customized training plans should be developed based on roles and responsibilities e.g. Portfolio Owners/ Product Owners, Delivery Managers, Data Engineers, Data Scientists/AI Experts etc. The training programme should comprise the following broad areas : Concepts and processes required to structure and execute an AI project successfully, including the roles and responsibilities of different members in the teamDetailed Hands-on Technical training to understand AI, Machine Learning, Deep learning and Process Automation algorithmsHands-on training on the available AI tools in the market Last Word The AI manifesto should create simple and effective guiding principles for formulation of strategy and execution built within it . The empowered agile teams should have enough knowledge and flexibility to do what is needed to execute and achieve the desired outcomes of the strategy without any significant overruns. We will discuss in detail about strategy formulation and capability building in the subsequent articles.
In the previous article of this series, I discussed how AI is at the heart of the digital transformation, can reap benefits across the value chain for industries spanning 3 pillars Redefine the Business Model Re-evaluate the Value Chain Re-imagine the Customer Journey However, while AI is the current “ industry buzzword” for industries, the […]
Where should an organisation get started on AI journey and what should be the right approach to scale up AI capabilities to become an AI-driven enterprise
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["Go", "machine learning", "AI", "digital transformation", "Git", "automation", "deep learning", "analytics", "GAN", "R"]
["AI", "machine learning", "deep learning", "analytics", "R", "Go", "Git", "GAN", "digital transformation", "automation"]
Upasana is an Assistant Manager in the Digital Consulting Practice of the Management Consulting Division of KPMG .