How GenAI will transform Management Consulting: Drawing parallels from Software Development (Part 1)

This is Part 1 of a two-part article where I apply observations and learnings from software and product development (my recent many years) to management consulting (my past many years). Comments welcome!

Introduction: The AI Inflection Point

Generative AI (GenAI) is reshaping industries at breakneck speed, and management consulting is no exception. Since the debut of ChatGPT in 2022, AI tools have evolved from novelty to necessity, promising to automate research, draft reports, and even generate strategic recommendations. Yet, as with software engineering, the reality of AI’s impact on consulting is nuanced—fraught with both transformative potential and hard limitations.

For decades, consulting has thrived on human intuition, deep industry knowledge, and the ability to navigate ambiguity. Now, GenAI threatens to disrupt this equilibrium. Headlines warn of AI replacing junior analysts or commoditizing strategy work, but these narratives often miss the mark. The truth lies in a more complex interplay: AI will augment consultants, but only those who learn to wield it as a disciplined collaborator—not a crutch.

This article series maps out AI’s realistic role in consulting, balancing optimism with pragmatism. We explore adoption patterns, pitfalls, and strategies to harness AI while preserving the craft of consultative expertise.

1. How Consultants Should Use AI: “Sprinters” vs. “Strategists”

Two distinct patterns could define AI adoption in consulting:

The Sprinters: Rapid Delivery at Scale

Sprinters leverage tools like ChatGPT, Claude, or proprietary AI platforms to:

• Automate repetitive tasks: Drafting meeting summaries, populating slide decks, or benchmarking competitors.
• Accelerate research: Scraping market data, synthesizing industry trends, or generating SWOT analyses.
• Simulate scenarios: Modeling financial outcomes under varying conditions (e.g., pricing changes, M&A impacts).

Example: A junior analyst at a mid-tier firm might use AI to condense a 300-page regulatory document into a 5-page brief in 20 minutes. The output could be structurally sound but lack nuance, requiring senior oversight to align with client priorities.

The Strategists: Depth Over Speed

Strategists will treat AI as a thought partner for complex problems:

• Challenge assumptions: Stress-testing hypotheses by asking, “What if our core premise is wrong?”
• Identify blind spots: Surfacing overlooked risks in supply chain resilience or geopolitical shifts.
• Enhance creativity: Brainstorming unconventional solutions (e.g., “How might a retail client adopt circular economy principles?”).

Example: A partner at a boutique firm could use AI to generate 15 variants of a market-entry strategy, then refine the top three with team input. The final proposal canthen blend AI’s breadth with human judgment on cultural fit.

The divide: Sprinters prioritize efficiency for well-defined tasks; Strategists focus on augmenting creativity and rigor. A warning - not knowing which mode one is in will create friction—for instance, juniors rushing analyses might deliver superficial insights, while seniors dismissing AI might miss opportunities to scale their thinking.

2. The 70% Problem: AI’s Promise and Peril

A pattern echoes across early AI experiments in consulting: AI gets you 70% of the way there—then the real work begins.

Hypothetical Case Study: The Illusion of Completeness

A team is using AI to draft a post-merger integration plan. The output include timelines, synergy targets, and cultural alignment steps—all structurally sound. But it misses critical nuances:

• A key stakeholder’s resistance to layoffs (revealed only in offline conversations).
• Regulatory hurdles unique to the target’s Southeast Asian operations.
• Overly optimistic savings estimates from shared services.

The team could spend more time fixing these gaps than what was saved by AI. In hindsight, they’ll likely note: “The AI gave us a generic playbook. Our job was to make it their playbook.”

Why the Last 30% Matters

The final 30% is where consulting’s value lives:

• Contextualization: Tailoring frameworks to a client’s culture, politics, and constraints.
• Judgment calls: Balancing quantitative models with qualitative risks (e.g., leadership dynamics).
• Ethical guardrails: Ensuring compliance and avoiding AI hallucinations (e.g., invented data).

This phase remains firmly human — at least for now.

3. What Could Work: Practical Patterns for AI-Augmented Consulting

From talking to several practitioners from the MBB firms, three patterns emerge:

Pattern 1: “AI First Draft, Human Final Cut”

• Let AI generate initial drafts of reports, models, or slides.
• Senior consultants review, contextualize, and pressure-test outputs.
• Why it works: Frees juniors from grunt work; focuses seniors on high-value edits.

Pattern 2: “Bidirectional Prompting”

• Treat AI as a dialogue partner, not a search engine. Example:

- Consultant: “What are risks of entering the Brazilian fintech market?”

- AI: Lists regulatory, currency, and competition risks.

- Consultant: “Now assume the client’s CMO opposes this move. Reassess.”

• Why it works: Mimics Socratic dialogue, uncovering deeper insights.

Pattern 3: “Trust but Verify”

• Use AI for speed, but validate all critical outputs:

- Cross-check data sources.

- Flag conflicts of interest (e.g., AI recommending a vendor it’s trained on).

- Stress-test conclusions against industry veterans.

In part two, we dive deeper into the implications for consultants themselves. How will AI impact the roles of senior and junior consultants? What new skills will be required, and how will the very nature of consulting evolve? We'll also examine the ethical considerations and the future outlook for AI-savvy consultants.

This section will explore the "Knowledge Paradox," the rise of (buzzword alert!) "Agentic Consulting," the return to core consultative skills, and the evolving demand for expertise in an AI-driven world.