EY Leaders Push Back on AI Anxiety, Say Junior Consultants Hold Strategic Advantage

Artificial intelligence is reshaping consulting’s operating model, and entry-level professionals are often portrayed as the most exposed. Tasks that once defined the apprenticeship phase of a consulting career — building slide decks, conducting market scans, synthesizing data — can now be completed in seconds by generative AI tools.

Yet senior leaders at Ernst & Young (EY) believe that the narrative of displacement misses a deeper shift underway in professional services.

Dan Diasio, EY’s global consulting AI leader, said the firm sees junior staff not as redundant labor in the AI era but as strategic assets. In his view, a lack of entrenched habits is precisely what gives early-career professionals an edge. Without years of ingrained assumptions about how work “should” be done, younger consultants are better positioned to redesign workflows from first principles and integrate AI natively into problem-solving.

Errol Gardner, EY’s global head of consulting, reinforced that message, arguing that graduates entering the firm today have more scope to reshape the organization than their predecessors. He described junior consultants as digitally fluent and creatively adaptable — traits that align with an industry pivoting toward AI-augmented delivery.

Rethinking the Consulting Pyramid

The debate reflects a structural question facing the consulting industry. The traditional pyramid model has long relied on large cohorts of junior staff handling research, modelling, and presentation development, while senior partners focused on client relationships and strategic framing.

If AI agents can automate much of that “assembly” work — drafting proposals, synthesizing documents, generating financial analyses — the economic logic of the pyramid could change. Fewer junior hires might be needed if output per consultant increases materially.

There are early signals of that recalibration elsewhere in the Big Four. PwC has reduced graduate hiring targets in the United States, citing AI among the contributing factors, according to internal materials previously reported.

EY, however, maintains there has been no material change to its graduate recruitment strategy as a result of AI adoption. Gardner said hiring continues in line with growth ambitions in consulting. The firm’s latest annual report noted more than $1 billion in annual investment in AI-first platforms and products, with AI-related revenue rising 30 percent in its 2025 financial year. That growth trajectory suggests the firm sees AI not as a cost-cutting lever alone, but as a revenue expansion engine.

Both leaders described the shift less as labor substitution and more as role evolution. Consultants, they argue, will spend less time compiling information and more time interpreting insights, designing solutions, and guiding organizational change.

Diasio cautioned against what he described as an overly simplistic view of knowledge workers being replaced. AI systems, he said, can produce outputs that are technically polished but lack contextual judgment. Without human expertise, AI risks generating what he termed “statistical sameness” — content that is fluent but strategically shallow.

This framing positions AI as an amplifier rather than a replacement. In this model, junior consultants who master AI tools can increase their leverage early in their careers, moving up the value chain faster than prior cohorts who spent years performing manual analytical tasks.

Talent Mix Is Shifting

Even as EY emphasizes continuity in graduate hiring, the broader consulting talent model is shifting. Firms across the industry are increasing recruitment of engineers, data scientists, and AI specialists. The capability mix within consulting practices is tilting toward technical depth alongside traditional strategy and operations expertise.

This hybridization may redefine what “entry-level” means. Graduates with coding, data engineering, or AI model deployment skills could command disproportionate influence relative to traditional generalist profiles.

At the same time, client demand is changing. Organizations are not only asking how to cut costs with AI, but how to redesign business models, restructure workflows, and govern emerging technologies responsibly. That advisory space requires creativity, communication skills, and the ability to manage organizational change — competencies that remain distinctly human.

Gardner argued that while tools will evolve rapidly, the core mission of consulting — delivering client value, enabling team capability, and implementing change — will remain stable through 2030 and beyond. Technology implementation, he said, continues to encounter human resistance, behavioral complexity, and cultural friction.

That reality preserves a role for consultants as translators between technical systems and organizational realities. AI may accelerate analysis, but execution still depends on persuasion, trust, and leadership alignment.

Anxiety vs. Opportunity

For junior professionals weighing a consulting career, the anxiety is understandable. Automation is encroaching on tasks that historically served as the training ground for developing analytical rigor.

Yet EY’s leadership perspective suggests a reframing: AI reduces low-value repetition and increases the premium on judgment, creativity, and technological fluency. Graduates who treat AI as a collaborator rather than a competitor may find themselves with broader influence earlier in their careers.

The competitive advantage, in this framing, lies not in resisting automation but in orchestrating it — combining algorithmic efficiency with contextual intelligence. In that environment, entry-level consultants are not obsolete. They are the first generation expected to build careers with AI embedded from day one.