Leading AI transformation: Essential skills for modern leaders

The seismic shift brought about by artificial intelligence is compelling organisations worldwide to undergo a profound transformation.

Consulting giants McKinsey report that in 2025 78 per cent of companies globally are making use of AI capabilities in at least one business function. That is compared to 55 per cent in 2023.

This change demands not only implementing new technologies but, more critically, a new calibre of leadership. And leading digital and AI transformation effectively is going to be vital for companies to stay competitive – whoever does it best will be the winners in the new AI age.

Modern leaders must possess a blend of technical acumen, strategic foresight, and human-centric skills to successfully navigate their organisations into an AI-empowered future.

Understanding AI transformation

AI transformation is the comprehensive and strategic overhaul of an organisation's processes, business models, and culture, driven by the adoption and integration of AI technologies.

It moves beyond simple digitisation to fundamentally alter how value is created, delivered, and captured.

The significance lies in its potential to unlock unprecedented levels of efficiency, innovation, and competitive advantage. In a fast-moving global economy, failing to embrace this transformation is a risk to long-term viability.

Key drivers of AI transformation

The decision to embark on a costly and complex AI transformation must be grounded in clear commercial objectives.

The impetus for change is multifaceted, but key drivers consistently emerge in C-suites globally, with the primary drivers are firmly rooted in commercial realities. The core financial and customer-centric pillars that drive this change are:

• Reducing costs: AI’s ability to automate repetitive, high-volume tasks directly translates into significant operational cost savings, freeing up capital for strategic investment
• Productivity gains: Beyond simple cost reduction, AI tools augment human capability, dramatically increasing the speed and output of knowledge work across innovation, product development, operations administration and strategic decision-making
• Value creation for the customer: AI enables the invention of entirely new products and services, such as hyper-personalised recommendations or predictive maintenance, delivering value that was previously unattainable
• Enhanced customer experience: This often acts as the sharp end of the transformation spear. Whether through intelligent chatbots, frictionless service interfaces, or predictive personalisation, AI elevates the customer journey, fostering loyalty and driving revenue growth.

These four drivers must be the ‘North Star’ for any AI initiative, ensuring technology is never implemented for its own sake, but always in service of measurable business outcomes.

Digital maturity assessment

AI technologies are not plug-and-play solutions. Their success is entirely contingent on the underlying digital and data readiness of the organisation. Therefore, a rigorous 'digital maturity assessment' is the mandatory first step, where leaders objectively evaluate the organisation's current state across several dimension:

• Data infrastructure: Assessing the existence of fragmented data silos, the scalability of cloud infrastructure, and the capability for high-speed data ingestion and processing
• Talent and skills: Identifying the current level of AI literacy among employees and leaders, and the availability of specialised skills like data science and machine learning engineering
• Process automation: Gauging the extent to which current business processes are standardised and digitised, which is a crucial precursor to effective AI deployment.

Developing an effective AI strategy

A clear, well-articulated strategy shifts the focus from technology acquisition to value realisation. It acts as the compass for the entire organisation. An effective AI strategy must be fundamentally ‘use-case’ first, not tech first. The tech is there to build the use-case into practice.

A strategy, therefore, should articulate:

• Value-centric use cases: A portfolio of specific problems or opportunities. For example, predicting machine failure in the supply chain or reducing call centre handling time. These need to directly tie back to the four key drivers
• Ethics and governance framework: A clear, published commitment to responsible AI, detailing protocols for bias detection, model explainability, data privacy, and compliance
• Measurable outcomes: Explicit metrics that define success. Is it an increase in revenue or the cost you are trying to minimise? Without a clear metric, a pilot will remain stuck in the lab.

An AI strategy development framework helps organisations systematically plan, implement, and scale AI initiatives to align with their broader business goals. Such frameworks consider ethics, and compliance to manage risks and ensure responsible AI use.

This includes establishing clear accountability structures, defining ethical principles for data use and algorithmic decision-making, and implementing robust oversight mechanisms to detect and mitigate bias, privacy breaches, and unintended consequences.

It should have a clear vision and identify strategic objectives, followed by assessing the organisation's readiness in terms of data, talent, and infrastructure. From there, companies prioritise high-impact use cases, develop a robust data strategy, and select the right technologies and tools to support AI deployment. Talent development and fostering a culture of innovation are also key to ensuring long-term success.

Change management in AI adoption

The ultimate success factor is the workforce’s willingness and ability to embrace AI, which requires effective change leadership.

Leaders must champion a culture of continuous learning and adaptability, where fear is replaced with fascination.

The narrative around AI should shift from the idea that it replaces jobs to the understanding that it augments human capabilities. By repositioning AI as a tool that removes repetitive tasks and enhances roles with more creative and strategic responsibilities, organisations can foster a more optimistic and empowered workforce.

Additionally, experimentation should be actively encouraged, with teams given both the psychological safety and the resources to explore new AI tools without fear of failure.

AI literacy must also become universal across the organisation, ensuring that all departments have a basic understanding of how AI works, its limitations, and how to effectively use tools like generative AI in their daily workflows.

Resistance to AI adoption, often rooted in concerns about job security, is best addressed through transparency, involvement, and clarity.

Involving and empowering employees is equally important. By allowing them to select, test, and champion AI tools that address their specific challenges, AI becomes a source of personal and professional empowerment rather than a top-down directive.

Also, reskilling efforts should be tailored to each department, ensuring that training is directly relevant to the new AI-augmented workflows employees will encounter.

Upskilling and reskilling represent the most sustainable, long-term solution to the growing AI talent shortage. Leaders must treat this not as a discretionary training expense, but as a core operational investment essential to future-proofing their workforce.

The approach should be twofold, addressing both technical and human capabilities. On the technical side, employees need training in areas such as prompting large language models (LLMs), understanding data governance, and effectively collaborating with AI platforms - skills that are increasingly relevant across all roles.

In an experiment involving consultants from Boston Consulting Group run by a group of researchers including Warwick Business School’s Hila Lifshitz, Professor of Management, alongside Harvard and Wharton Business Schools, they found that AI helped close the gap between lower performing workers with their stronger peers. Those using AI outperformed those without by 40 per cent, but on a strategic-decision making task those using ChatGPT performed worse. And yet their AI-written presentation was more convincing.

Also, those using AI on a creative task produced many similar ideas. Those without it didn’t create any better ideas but they were much more diverse.

As Professor Lifshitz says “the optimum way to use AI will remain unclear for the near future. Therefore, the onus will be on managers to keep experimenting with the technology as it evolves”.

Thus, equally as important for workers is the development of soft skills that AI cannot replicate, such as critical thinking, complex problem-solving, negotiation, empathy, and relationship building. These human-centric abilities will become even more valuable in an AI-augmented workplace.

Continuous AI learning

Leaders should create systems that allow learning to be shared and formalised, such as internal AI research groups, dedicated innovation hours, and recognition for employees who become internal AI champions.

This is especially important so knowledge about the pitfalls, hallucinations and biases of LLMs like ChatGPT is spread across the organisation. In new research, Professor Lifshitz and WBS PhD Candidate Steven Randazzo, who is also a Visiting Research Fellow at Harvard Business School, discovered that generative AI is programmed to be very persuasive.

In an experiment they found when professionals tried to validate the LLM's answer it attempted to persuade them to accept its preliminary output. The more they validated it by fact-checking and pushing back, the more it increased the intensity of its persuasion. The researchers labelled the generative AI's behaviour “persuasion bombing”.

The ultimate goal of AI leadership is to structure the organisation for the future, not just the present. AI is poised to deepen its impact across industries and drive a fundamental restructuring of value chains.

In the creative industries, AI will serve as the ultimate co-pilot by managing content generation and iteration, so that human creatives can focus entirely on vision, storytelling, and strategic direction.

Equally, the research community is actively looking to protect the intellectual property of artists with tools such as Nightshade that protects their work by subtly altering pixels in images, which the human eye can't notice, but confuses AI models.

The future of work with AI

Preparing teams for an AI-empowered workforce requires establishing a new operating model built on human-AI collaboration. Rather than focusing solely on automation, leaders must prioritise redesigning jobs to enhance human contributions alongside AI.

This begins with clearly defining the new ‘human’ tasks, which are likely to be those that rely on strategic thinking, emotional intelligence, and sound judgement.

Teams must also be trained to collaborate effectively with AI, learning how to prompt, validate, and trust AI-generated outputs to create a seamless, augmented workflow.

Employees should also be encouraged to engage in role-playing with AI tools, assigning them specific functions, such as acting as a marketing writer, so they can orchestrate AI as part of a hyper-efficient virtual team.

In this augmented future, the most essential skill for any leader is the capacity to envision, articulate, and execute a strategy that harmonises human talent with the exponential power of AI.

It is the art of leading not just people, but a seamlessly integrated cognitive enterprise.

Further reading:

Working on the jagged frontier: How companies should use generative AI

The new AI 'prediction products' and the risks they present

No shortcut to upskilling: Why managers can't rely on Gen Z on generative AI

Multidexterity: How Lego and Netflix achieved digital success

Neha Gupta is Assistant Professor of Information Systems Management and Analytics at Warwick Business School. She teaches Artificial Intelligence for Business on the BSc International Management and BSc Management, BSc International Management, and BSc Accounting and Finance. She also lectures on Digital Frontiers on the Global Online MBA. 

Learn more about harnessing AI to give your organisation a competitive advantage on the two-day Executive Education course AI Leadership programme at WBS London at The Shard.

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