How AI Has Changed the Role of Advisors and Consultants
A contemporary analysis of technology, human judgement and evolving advisory practice
1. Introduction
Artificial intelligence has emerged as one of the most transformative technologies of the modern era, fundamentally altering how organisations access, process, and utilise information. What was once the domain of specialised technical teams is now widely accessible, enabling individuals and organisations to generate insights, automate workflows, and structure decision-making processes with unprecedented speed. This shift raises an important and increasingly relevant question: in a world where information is abundant and analytical capability is widely distributed, what is the continuing role of advisors and consultants?
Historically, consulting has been built on the premise of expertise—the ability to access, interpret, and apply knowledge that was not readily available within organisations. However, as AI reduces the scarcity of information, the challenge has shifted. Organisations are no longer constrained by a lack of data or insight; instead, they are faced with an overabundance of both. As Anthony (2026) reflects, the consulting profession has always evolved alongside changes in organisational needs and technological capability. The emergence of AI represents not a disruption that invalidates consulting, but an inflection point that redefines its value.
This paper examines that transformation, arguing that while AI has changed how advisory work is conducted, it has reinforced—rather than diminished—the importance of human-led advisory.
2. From Expert Authority to Adaptive Partnership
The traditional model of consulting was rooted in expertise and authority. Advisors were engaged to analyse organisational challenges, propose solutions, and often validate internal perspectives that lacked consensus. In many cases, organisations were not seeking entirely new insights, but rather confirmation of ideas that were already understood internally. As Anthony (2026) observes, clients often required “somebody from the outside to say what we’re saying already,” enabling action through external validation. This model relied on a number of assumptions: that expertise was scarce, that best practice could be transferred between organisations, and that problems could be addressed through structured, repeatable methodologies. Under these conditions, consultants could develop solutions in one environment and apply them—with limited adaptation—in another. However, this approach has become increasingly insufficient. As Anthony (2026) notes, the era of “copy and paste” consulting has effectively disappeared. The complexity of modern organisations, combined with rapidly changing external environments, has rendered standardised solutions less effective. Even within similar industries, organisations differ significantly in culture, structure, and strategic priorities, requiring more bespoke approaches.
In response, the role of the advisor has evolved. Rather than acting solely as an expert delivering solutions, the modern advisor operates as a collaborator—working alongside organisations to define problems, explore options, and co-create outcomes. Anthony describes this role as that of a “critical friend,” reflecting a relationship that combines challenge with support, and expertise with partnership. This shift represents a fundamental redefinition of advisory work. The value of the consultant no longer lies solely in providing answers, but in facilitating better questions and enabling more effective decision-making.
3. AI as a Continuation of Technological Transformation
Artificial intelligence should not be viewed in isolation, but as the latest phase in a broader continuum of technology-enabled change. Organisations have long engaged with automation, outsourcing, and digital transformation as mechanisms for increasing efficiency and reducing operational burden. Riat (2026) situates AI within this trajectory, emphasising that while the concept itself is not new, its accessibility and capability have significantly expanded its impact. What distinguishes AI from previous technologies is its ability to process unstructured information, generate outputs, and interact with users in ways that resemble human reasoning. This has fundamentally changed how knowledge work is performed.
In practice, AI is already reshaping aspects of consulting. Patel (2026) highlights its application in proposal and bid writing, where AI can synthesise large volumes of expertise and generate structured responses rapidly. Similarly, Riat (2026) notes that processes which once required input from multiple stakeholders can now be streamlined, reducing both time and resource requirements. However, these applications reveal an important pattern. AI is not being used as a replacement for human advisors, but as a tool to enhance their effectiveness. The role of the advisor remains central—guiding, refining, and validating outputs to ensure they are contextually relevant and strategically sound.
4. The Limits of Artificial Intelligence in Advisory Practice
Despite its capabilities, AI presents a number of limitations that constrain its role in advisory contexts. These limitations are not merely technical, but structural, reflecting the nature of organisational challenges themselves. Firstly, AI lacks the ability to fully engage with multi-dimensional complexity. Organisational challenges often involve an interplay between strategy, people, processes, and environmental factors. Riat (2026) argues that current AI tools are not equipped to navigate these layers simultaneously, particularly when human dynamics are involved. Secondly, AI outputs are dependent on underlying data patterns, which may be incomplete or biased. Anthony (2026) highlights the risk that AI can reproduce inaccuracies based on collective inputs, raising concerns about reliability in analytical and strategic contexts. Thirdly, AI lacks accountability. While it can generate recommendations, it does not bear responsibility for the consequences of decisions. This requires human oversight, particularly in high-stakes environments where judgement and risk evaluation are critical.
Perhaps most importantly, AI cannot replicate the relational dimension of advisory work. Advisors do not operate in isolation; they engage with individuals, teams, and leadership structures. As Anthony (2026) observes, leadership can be an inherently isolating experience, and advisors often serve as trusted partners who provide both support and challenge. This relational role—grounded in trust, empathy, and communication—remains beyond the reach of artificial systems.
5. The Emergence of Augmented Advisory Practice
The integration of AI into consulting has not eliminated the need for advisors; rather, it has introduced a new model of practice—one that is increasingly augmented by technology. AI enhances efficiency by automating repetitive tasks and accelerating information processing, enabling advisors to allocate more time to higher-value activities. Patel (2026) emphasises that while AI can generate outputs rapidly, it requires human direction to ensure those outputs are meaningful and relevant. This dynamic introduces new skill requirements. Advisors must now be capable not only of interpreting information, but of structuring interactions with AI effectively. Anthony (2026) identifies prompt engineering as a critical capability, highlighting the need for deliberate and precise input to generate useful outputs. At the same time, the relationship between speed and trust remains complex. While AI accelerates processes, its outputs are not yet trusted to operate without oversight. As Anthony (2026) notes, organisations frequently apply extensive validation to AI-generated outputs, effectively inspecting all results before use. This creates what might be described as a “speed–trust paradox”: the technology enables faster production, but the need for verification limits the extent of that efficiency. In this environment, the advisor plays a critical role as an interpreter and validator of AI-generated insight, ensuring that outputs are contextually appropriate and strategically aligned.
6. Organisational Transformation and the Role of Human Interpretation
The adoption of AI is reshaping organisational structures, capabilities, and decision-making processes. However, this transformation is neither uniform nor straightforward. Large organisations tend to approach AI with caution, prioritising governance, control, and risk management. Riat (2026) notes that corporate environments often require structured frameworks to manage AI usage, reflecting concerns around data integrity and compliance. In contrast, smaller organisations and startups often adopt AI more rapidly, using it to reduce costs, increase agility, and accelerate growth. This divergence highlights a broader tension between innovation and control, requiring careful navigation. Simultaneously, AI is challenging traditional approaches to skills development and assessment. Anthony (2026) observes that conventional methods—such as coursework and written assessments—are increasingly vulnerable to AI-assisted completion, raising questions about how competency should be evaluated. This has prompted a shift toward more experiential and discussion-based evaluation methods, which focus on demonstrated understanding rather than produced outputs. At a broader level, the widespread use of AI has amplified the need for human interpretation. As information becomes more accessible and outputs more abundant, organisations require individuals capable of contextualising and applying that information effectively. In this sense, AI does not reduce the need for expertise—it redefines it. Expertise becomes less about generating information and more about making sense of it.
7. The Enduring Role of Advisors in an AI-Enabled World
Despite the transformative impact of AI, the core functions of advisory work remain intact. However, they are increasingly concentrated around uniquely human capabilities. Organisational decision-making is inherently complex, involving trade-offs, uncertainties, and competing priorities. As Riat (2026) highlights, these challenges cannot be fully resolved through algorithmic analysis alone. Advisors provide judgement grounded in experience, enabling organisations to navigate ambiguity with greater confidence. Equally important is the role of relationships. Advisory work is built on trust, requiring advisors to engage with stakeholders, influence decision-making, and facilitate alignment. This relational dimension cannot be replicated by AI.
Finally, advisors provide accountability. They are responsible for the guidance they offer and the outcomes it produces, a responsibility that remains fundamentally human. As Patel (2026) suggests, AI may enhance capability, but it does not replace the need for expertise, discernment, and human connection.
8. Conclusion
Artificial intelligence has undeniably changed the landscape of advisory and consulting work. It has accelerated processes, expanded access to information, and introduced new capabilities into organisational decision-making. However, its impact has not been to replace advisors, but to redefine their role. The modern advisor operates in a space shaped by complexity, uncertainty, and abundance of information. In this environment, their value lies not in possessing unique knowledge, but in interpreting, contextualising, and applying knowledge effectively. As Anthony (2026) reflects, the consulting profession must continue to adapt, moving beyond technical expertise to focus on relational, strategic, and judgement-based capabilities. AI has changed how advisors work. It has not changed why they are needed. Indeed, as organisations continue to navigate an increasingly complex and dynamic world, the importance of human-centred advisory may be greater than ever.
References:
Anthony, S., Riat, A.S. and Patel, M. (2026) The Uncommon Advisor Podcast: Episode 1 – The Role of Advisors Today. [Podcast].
Anthony, S., Riat, A.S. and Patel, M. (2026) The Uncommon Advisor Podcast: Episode 2 – AI Won’t Save You. [Podcast].