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Crafting the AI Strategy
Crafting the AI Strategy
A well-thought-through and articulated strategy aligns the entire organisation to enterprise-wide deployment and a reassessment of each individual’s role in the future organisation. This shift is necessitated by the fact that almost all organisations have stated aspirations to deploy AI, with substantial use already initiated in many organisations. The strategy is also important because in most organisations, there are only a handful of use cases reported and the value being derived is rarely quantified, while the stated board or leadership goal is full deployment in a couple of years.
To start the journey towards realising these soaring ambitions, the foundations have to be very strong in infrastructure, data management, ecosystem development and governance. The IT department of the organisation will be well positioned to provide a status on the infrastructure, calling out outdated systems and both hardware and software that needs to be upgraded or replaced. Data is a complex issue since many organisations, even some medium to large ones, have not really worried too much about the data quality, particularly when data is ingested from external sources. This assessment may need help from external experts in data engineering and the design of data warehouses and marts and a well-defined process of storage, dissemination and conversion to information. Business process re-engineering is a valuable precursor to new systems deployment.
The enabling ecosystems of demand generators, data suppliers and process managers must be brought together to re-imagine any business process that can benefit through technology touch points.
As an integral part of the strategy development process, security assessments for systems and data are essential. The Digital Personal Data Protection Act, which is a mandate for all Indian corporations, and implementation of strong cybersecurity solutions for data and systems are essential. All this needs to be under the cover of a well-designed governance framework, which will reassure the board of directors and the entire organisation that the strategy is robust and all control systems are in place.
Leaders must recognise and articulate that the success of game-changing AI will depend on strong data foundations and streamlined data flows, strategic partnerships with competent vendors, deployment of the right tools and a strong preference for use cases that go beyond point solutions to system-wide implementation that targets and achieves sustainable competitive advantage for the organisation. Notably, maintaining data quality as it moves between models and applications, particularly for sources and destinations outside the organisation, is part of the tracking of lineage. Eliminating non-automated data handling, ensuring data liquidity by creating seamless access with the right tools and combining multi-source data for comprehensive analysis are key focus areas for leaders, and they also need to ensure that data management is made more efficient through the creation of a metadata layer that enables ready contextualisation for all users.
The continuous need to balance innovation and risk and choose between first-mover advantage and safety and security of proven methodologies is the real role of the leader. Many large enterprises cite governance concerns as the biggest deterrent in their transformation journey, which is understandable, given their accountability to a large employee and shareholder base. This also emphasises the imperative to embrace an AI-powered organisation under the overall control of intelligent humans. This is particularly important as the organisation’s AI journey moves from using traditional databases to LLMs and from GenAI to AI agents and autonomous AI agents that learn user preferences over a period of time and can make independent decisions based on the unique situation and context. With all the data, information and knowledge that this journey will generate, the wisdom of the leader will be called on to make appropriate choices and move to a phase of dual intelligence.
练习题
Which statement best explains why an organisation needs a clearly articulated AI strategy at the start of its AI journey?
An organisation has ambitious board-level goals for full AI deployment but only a few unquantified use cases today. What should it prioritise first according to the section?
Which responsibility is most directly assigned to the IT department in preparing for AI deployment?
Which actions are presented as important for creating reliable data foundations for AI? Select all that apply.
Which elements are described as part of leadership’s role in moving beyond isolated AI pilots toward sustainable competitive advantage? Select all that apply.
Business process re-engineering is described as a useful precursor to deploying new systems.
The enabling ecosystem for AI process redesign should include only technology vendors, because demand generators, data suppliers, and process managers can be added later.
A governance framework is mainly cosmetic; it is not central to reassuring the board or maintaining control systems during AI transformation.
As organisations move from traditional databases to LLMs, GenAI, AI agents, and autonomous AI agents, the section argues that intelligent human control becomes less important.
Maintaining data quality as data moves between models and applications, especially across external sources and destinations, is part of tracking data ___.
Creating seamless access to data with the right tools is described as ensuring data ___.
Why does the section connect governance concerns with the need for intelligent human control in AI-powered organisations?
Explain how the current section’s idea of a governed AI strategy connects with the prior idea that leaders must choose AI deployment areas wisely and consider ethical implications.
What is meant by moving toward a phase of dual intelligence, and why does leadership wisdom matter in that phase?
A company wants to move from a few isolated AI pilots to enterprise-wide deployment while still keeping humans in control. Which action best reflects a sound strategy based on both organisational transformation and responsible AI principles?
Which actions should leaders take when crafting an AI strategy for scalable deployment across the organisation? Select all that apply.
Because advanced AI systems can process large amounts of data, leaders should avoid making decisions until nearly all facts are available and can then hand final judgement to AI systems.
When organisations combine multi-source data, maintain quality as data moves across models and applications, and keep humans in control of AI systems, they are pursuing a approach to AI transformation.
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