A Legacy Reimagined: The Evolving Narrative of Kearney in the Age of AI

1. The Genesis of a New Mission: A Legacy of Operational Excellence

The story of Kearney's engagement with the AI revolution is not a sudden, modern pivot, but a continuation of a narrative that began nearly a century ago. Its roots are deeply embedded in the origins of modern management consulting itself, a legacy that provides the philosophical and practical bedrock for its contemporary work. The firm's journey began in Chicago in 1926 when James O. McKinsey founded his firm of "accountants and management engineers".¹ A pivotal moment arrived three years later with the hiring of Andrew Thomas "Tom" Kearney as the firm's first partner in 1929.¹ This partnership laid the groundwork for a distinct approach to business challenges, one founded on a blend of analytical rigor and tangible, operational practicality.

Upon McKinsey's untimely death in 1937, the firm underwent a crucial schism.¹ Disagreements among the remaining partners led to a split into three separate organizations, with Tom Kearney leading the Chicago consulting office under the name McKinsey, A.T. Kearney, and Company.¹ This period of independence cemented the firm’s focus on practical, on-the-ground problem-solving. In 1947, a final separation occurred when Tom Kearney purchased the rights to the McKinsey name from his fellow partner, Marvin Bower, and officially renamed his firm A.T. Kearney and Company.¹ This act of independence solidified the firm’s identity, allowing it to forge a unique path focused on strategic operations rather than pure strategy. For decades, this emphasis on "deep operational expertise" has been the firm's core strength, enabling it to tackle "high-impact strategic and operational challenges faced by businesses, governments, and institutions worldwide".²

This historical context is vital to understanding the firm's current role in the AI landscape. Kearney's embrace of artificial intelligence is not a radical reinvention but a logical and inevitable evolution of its core mission. By leveraging AI to solve problems in areas such as supply chain management, the firm is applying a new, powerful tool to its historical areas of expertise.³ This can be seen in its strategic acquisition of IMTEK Inc., a company focused on services and solutions for supply chain planning and execution.¹ The fact that IMTEK, now "a KEARNEY Company," has produced a whitepaper on "AI in Supply Chain" is telling.³ This is not a random diversification; it is a strategic move that directly leverages Kearney's core competency. The firm's AI practice is positioned as a natural progression, allowing it to offer a "regenerative growth engine" and a "full-stack transformation" that builds on its existing strengths.⁴ This approach frames Kearney as a trusted, long-term partner rather than a flashy, trend-chasing consultancy.

A necessary clarification must be made to avoid any confusion in a research context. The focus of this report is on the global management consulting firm, formerly known as A.T. Kearney and now simply "Kearney." It is distinct from Kearney & Company, which is a CPA and consulting firm that exclusively serves the government sector.⁵ While both share a name and a Chicago origin, their missions and areas of expertise are separate. The global consulting firm Kearney, which operates in more than 40 countries, is the focus of the following analysis.¹

2. The New Frontier: Navigating the Age of AI

Before delving into Kearney's specific offerings and analysis, it is essential to contextualize its work within the broader forces shaping the artificial intelligence landscape. The year 2025 marks a tectonic shift in the adoption and application of AI, moving from a niche technology to an era-defining force.⁴ The most significant trends are redefining how organizations operate and compete, yet they also introduce unprecedented challenges.

The first major trend is the emergence of agentic AI, a paradigm shift beyond simple task automation.⁶ This new frontier involves AI systems that can reason and execute multi-step processes autonomously to achieve a high-level goal, fundamentally altering business processes.⁶ As one example, a high-tech company was able to automate more than 75 percent of its complex Request for Proposal (RFP) process by leveraging an "end-to-end view and leveraging the broader agentic AI toolkit".⁶ The impact potential of this technology is twofold: it can dramatically increase productivity by automating complex tasks, freeing humans to focus on higher-value activities, while also creating entirely new value streams that unlock innovative business models and products.⁶ Autonomous vehicle technology is another prime example, as it is beginning to transform not just the ride-share industry but the entire automobile manufacturing landscape.⁶

Simultaneously, Generative AI (GenAI) has become ubiquitous, moving beyond a novel technology to a deeply integrated part of daily applications and workflows.⁸ The democratization of AI means that it is now accessible to individuals and organizations without deep technical knowledge, and its adoption in the workplace is projected to see a significant surge in investment.⁸ A recent study found that IT leaders project that 20 percent of their tech budgets in 2025 will be devoted to AI, with the majority going to GenAI applications.⁸ This indicates a broad-based move from experimentation to enterprise-wide implementation.

Finally, the advancement of multimodal AI is enabling new levels of complexity and intuition.⁸ Moving beyond models that process only text, multimodal AI can interpret and understand information from different data types, including audio, video, and images.⁸ This technology empowers business leaders to analyze a greater variety of data to make more strategic decisions. The capacity to process and integrate diverse data sources allows for increasingly complex tasks to be performed with less human intervention, further accelerating the pace of transformation.⁸

However, this technological optimism is tempered by a series of significant ethical and practical challenges. The most critical is the opacity of the "black box" problem.⁹ The inner workings of many complex AI systems are not transparent, making it difficult for observers or affected parties to understand the logic behind their decisions.⁹ This lack of transparency makes it challenging to control, monitor, and correct an algorithm, which can be particularly problematic when high-stakes decisions are being made. This opacity is often compounded by the issue of

unjustified actions, where algorithmic decision-making relies on correlation rather than established causality.⁹ Decisions based on spurious or population-level correlations can lead to actions with significant negative personal impact on individuals.

Furthermore, the belief that AI systems are inherently unbiased is unsustainable.⁹ AI systems unavoidably make biased decisions because their design and functionality reflect the values of their designers and the biases present in their training data.⁹ This can lead to

algorithmic bias and discrimination, where systems perpetuate and amplify existing social prejudices.⁹ The challenge is to embed principles of non-discrimination and fairness into AI systems, which is particularly difficult when algorithms have access to linked datasets that can reveal proxies for sensitive attributes like gender or ethnicity.⁹

This foundational tension between the immense promise of AI and the significant ethical and practical hurdles creates a complex landscape for enterprises to navigate. Kearney's value proposition, therefore, is not just to implement technology, but to act as a guide through this tension, mitigating risks while unlocking value. This positions the firm as a mature and responsible advisor rather than a purely technical integrator. The insights from its reports and partnerships consistently address these challenges, framing its structured approach and ethical commitments as a strategic and necessary response to the very real complexities of the AI era.

3. Kearney's Strategic Compass: From Vision to Value

In a world defined by the transformative power and inherent risks of artificial intelligence, Kearney has articulated a clear, strategic compass for its clients. The firm views AI not merely as a set of tools for cost-cutting, but as a "truly era-defining force" with the power to "fundamentally change the way we think about, create, and solve problems".⁴ The firm’s philosophy is centered on leveraging AI to "power new revenue streams," positioning it as a "regenerative growth engine" for the modern enterprise.⁴ This forward-looking approach is about building an organization's "data muscle and monetization mindset to thrive today and evolve for tomorrow".⁴

To turn this philosophy into practice, Kearney has developed a structured, three-stage approach that provides a predictable path to AI transformation. The journey begins with Vision and Strategy, where the data and AI team works "hand in hand" with clients to define their goals, identify high-impact business use cases, and architect a comprehensive data and AI vision.⁴ This stage is about crafting a "value-backed AI strategy" that aligns technology with business objectives.⁴ The next stage,

Design and Build, focuses on rapidly creating prototypes that support enduring solutions and designing an effective operating model.⁴ This is where the theoretical vision begins to take tangible form through hands-on application of advanced analytics and AI techniques.⁴ The final stage,

Scale and Enhance, involves delivering the technology and, crucially, managing the organizational change required to achieve enterprise-wide adoption and unlock full value potential.⁴ This systematic process helps organizations responsibly manage and use data and AI to drive strategic objectives.⁴

A key element of Kearney’s strategy is its recognition that no single firm can possess every necessary capability to drive a full-stack transformation. Instead of attempting to build every technical solution in-house, the firm has cultivated a "global ecosystem of large-scale partners, AI-first companies, and systems integrators".⁴ This network, referred to as the Kearney AI alliance ecosystem, is designed to provide "full-stack capabilities and strategies" to clients.¹⁰ A significant example of this strategy is the partnership with Microsoft in the Middle East & Africa.¹¹ This alliance is aimed at accelerating the rollout and adoption of AI in a region where nearly 70 percent of CEOs view AI as transformative, yet fewer than 40 percent have implemented it at scale.¹¹

The collaboration with Microsoft illustrates a deliberate, high-leverage business model. Kearney brings the "strategic clarity, execution capabilities, operating model design, and responsible governance needed to move AI from boardroom vision to enterprise reality".¹¹ Microsoft, in turn, provides the "scalable, secure adoption through its trusted cloud and AI platforms".¹¹ The partnership is rooted in a shared vision to "bridge the gap between ambition and execution" by addressing challenges in critical industries like the public sector, manufacturing, and financial services.¹¹ This approach frees Kearney to focus on what it does best: providing deep strategic insight and change management expertise, while its partners handle the technical underpinnings. The firm's hands-on approach, where they work "hand in hand" with clients to "build out the toolsets and skillsets you need with those who have done it before" ⁴, suggests a model of co-creation that mitigates risk while accelerating the path to value.

4. Anatomy of an AI Transformation: Stories of Impact and Insight

Moving from theory to practice, a closer examination of Kearney's work reveals the tangible impact of its strategic approach, while also highlighting the significant organizational challenges that must be overcome. The firm's analysis defines the "agentic AI disruption" not merely as a silent revolution but as a fundamental shift from traditional task-based automation to comprehensive process reimagination.⁶ This new wave of AI creates entirely new ways of working that were previously impossible, but it also brings to the forefront the human and organizational factors that determine success or failure.⁶

A poignant illustration of this dynamic is an example of a major North American airline that attempted to optimize its pricing using counterfactual learning techniques.⁶ The pilot tests yielded a remarkable increase of over 5 percent in prices, demonstrating the technical efficacy of the AI solution.⁶ However, the initiative struggled to scale beyond the pilot phase. The problem was not the technology, but the "organizational and process changes required across technology, pricing, marketing, and operations".⁶ The firm's analysis notes that many well-designed AI programs never make it to production because teams cannot secure the necessary cross-functional alignment.⁶ This example serves as a powerful cautionary tale about the primacy of human and organizational hurdles over technical feasibility.

In stark contrast, a large hospitality company provides a compelling success story.⁶ This organization established an "AI control tower chaired by the CEO" to drive a business process re-engineering effort across all major functions.⁶ This top-down leadership and focus on cross-functional alignment led to an expected 2 to 3 percent net increase in revenue and an anticipated 10 percent-plus reduction in back-office operating costs within two years.⁶ This case demonstrates that direct involvement from top leadership and a structured, enterprise-wide approach can overcome the organizational resistance and complexity that often derail AI initiatives.⁶

These case studies are not isolated anecdotes; they validate the findings of a broader study conducted by Kearney and Futurum. The study found that despite the transformative potential of AI, a chasm exists between ambition and execution.¹² Established firms, a significant 73 percent, engage AI-focused consultancies to modernize their entrenched technology stacks and unite siloed data before scaling disruptive AI initiatives.¹² The data further reveals that the leading hurdles for AI adoption are not technical, but organizational: building cross-functional alignment (62%) and adjusting workflows and processes (63%).¹² This analysis points to a "myopic approach" combined with "fragmented data infrastructure and cultural resistance" as the key reasons for the "widening gap between AI's promise and actual business impact and value creation".¹² The core challenge in AI, therefore, is not a lack of technology but a failure of organizational change management—a problem that is precisely in Kearney’s wheelhouse. The airline's failure to scale and the hospitality company's success story are practical illustrations of this causal relationship: lack of cross-functional alignment leads to failure, while a measured, top-down, and systematic approach leads to success.

Given these pervasive challenges, Kearney's commitment to responsible AI is not a mere ethical addendum; it is a strategic imperative that directly addresses the risks of opacity, bias, and discrimination.¹³ The firm’s "promise about the quality of our output" includes six key elements:

●       Conscious selection of approaches and tools: Kearney trains its colleagues on the associated risks of new AI technologies, including unreliability or bias, and follows careful guidelines for development and selection.¹³

●       Human oversight: All client outputs, including the results of AI tools, are validated by senior consultants to ensure accuracy and to maintain human responsibility and judgment.¹³

●       Transparency on sources and methods: The firm discloses material sources of uncertainty, including risks and assumptions related to AI use, and strives to understand model logic to support informed decisions and traceability.¹³

●       Respect for confidential and personal information: Guardrails are in place to prevent unauthorized, unethical, or unlawful use of AI that could compromise privacy or data confidentiality.¹³

●       Promoting inclusion: The firm controls the use of AI for processing sensitive personal information, ensures human supervision of people-related processes, and evaluates output and data to identify potential bias.¹³

●       Advancing AI: Kearney encourages open dialogue on ethical AI use and develops training for both its colleagues and clients to facilitate responsible adoption.¹³

This robust framework demonstrates a mature, long-term perspective on AI's societal impact and reinforces the firm's role as a trusted partner that guides clients through the complexities of a new era.

5. The State of the Art: A Deep Dive into the LLM Landscape

To provide clear, actionable guidance to its clients, Kearney has moved from general strategic advice to specific, data-driven analysis of the AI technology market itself. A key piece of the firm's intellectual property is its proprietary LLM Leaderboard, a tool designed to help clients navigate a crowded and rapidly evolving market.⁴ The leaderboard’s methodology assesses the performance of various Large Language Models (LLMs) against "real-world business needs," providing a critical, business-oriented lens that goes beyond simple technical benchmarks.⁴

The leaderboard’s findings are a guide for researchers and business leaders alike, revealing that there is no single "best" LLM, but rather a specialized market where the ideal choice depends on a client's specific strategic goals, budget, and timeline. The firm’s analysis provides a nuanced breakdown of the leading models:

●       OpenAI's GPT-4.5 and o1 models: These are identified as the comprehensive solutions.¹⁴ They deliver superior performance with minimal implementation risk, making them the clear choice for enterprises that prioritize both capability and operational readiness.¹⁴

●       Sonar-reasoning-pro and DeepSeek R1-671b: These models are categorized as performance specialists.¹⁴ They excel in core business applications such as customer service and data processing, but the analysis points out that they "require a greater organizational investment in compliance and integration infrastructure".¹⁴

●       Amazon Nova Pro: This model occupies a strategic middle ground.¹⁴ It offers strong performance with moderate implementation complexity, making it ideal for organizations seeking near-best-in-class task execution without the premium readiness profile of the top-tier models.¹⁴

●       Mistral's Large v2: This model is highlighted for being a valuable option for cost-conscious organizations.¹⁴ It provides competitive performance while significantly reducing total ownership costs, making it an attractive choice for those with budget constraints.¹⁴

●       Google's Gemini Pro: This model addresses rapid deployment needs.¹⁴ Its exceptional API reliability and global language support make it optimal for organizations that prioritize speed-to-market and international scalability over absolute performance.¹⁴

The following table synthesizes these key findings, providing a clear reference for business and technology leaders.

The creation of this leaderboard demonstrates that Kearney's analysis goes beyond simple technical benchmarks to provide actionable insights tailored to different business needs. The framework rejects the idea of a single "one-size-fits-all" solution. It reinforces the firm's position as an objective advisor that can rapidly match a client's specific strategic goals, budget, and timeline to the appropriate technology. This approach accelerates the crucial "vision and strategy" phase of client engagement and, more importantly, helps mitigate the risk of costly failure that comes from choosing the wrong solution for a given business context.

6. Unlocking Tomorrow's Value: The Path Forward for an AI-First Enterprise

The cumulative analysis of Kearney's work and intellectual output in the AI space reveals a clear, consistent message: for established enterprises, AI is a marathon, not a sprint.¹² The firms that will ultimately succeed in this new era are not those chasing aggressive, catch-up strategies, but those that focus on foundational readiness.¹² This measured approach involves investing in "foundational" AI pilots to build "data readiness and upskill teams," with an eye toward a future where market pressures inevitably rise.¹²

The true value of a partner like Kearney is not in providing the technology itself, but in guiding an organization through the human, ethical, and organizational complexities that stand between vision and value. As the firm's research has shown, success hinges on a "measured, systematic approach" with a "tomorrow-back lens" that prioritizes cross-functional alignment, workforce engagement, and thoughtful rollouts.¹² The era of AI is defined by volatility, uncertainty, complexity, and ambiguity, and the journey forward is one of transformation that requires both strategic clarity and operational discipline.⁴ The story of Kearney in the age of AI is, therefore, a story about the continued relevance of deep operational expertise in a world defined by its speed of change.

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