- AI is changing consulting by raising client expectations and putting pressure on the old time and materials model, so firms need to redesign how they sell, deliver, and prove value instead of just speeding up routine work.
- The biggest problem in consulting is not lack of data, it is fragmented context, which is why the blog argues for an AI coworker that connects knowledge across systems and helps teams move from finding information to taking action.
- AI creates the most value when it supports the full consulting lifecycle, from proposals and discovery to delivery, retention, and learning, while keeping security, trust, and measurable outcomes at the center.
AI is reshaping the consulting industry, from the way firms work to the expectations of their clients. Accenture recently reported $5.9B in GenAI bookings, PwC said it was actively engaged in GenAI with 950 of its top 1,000 US clients, and total AI spend worldwide is expected to reach $632B by 2028.
But this shift is doing more than creating demand. It is also putting pressure on the traditional consulting model. Clients are questioning time-and-materials billing, whether firms truly have differentiated AI expertise, and whether outside partners are learning on the client’s dime. One of the strongest signals from the market? Customer leaders quoted in industry research said consultants had overpromised on real AI use cases and were often no better than internal teams.
However, firms are experiencing real friction that AI can solve for. Partners are still stitching together proposals and executive narratives from fragmented context. Engagement managers are losing hours every week to status updates, prior deliverables, and workstream coordination. Associates are still spending large parts of their day searching for slides, definitions, examples, and subject-matter expertise instead of moving the work forward.
That is why getting AI transformation right matters so much for consulting right now. Not as another isolated chatbot, and not as a novelty layered on top of old workflows. The real opportunity is to turn a firm’s proprietary knowledge, delivery history, client context, and internal expertise into a usable system of action — one that helps teams sell better, deliver faster, prove value more clearly, and scale what they know across the business.
What exactly is GenAI for consulting?
GenAI for consulting can do more than just accelerate deliverables. At its best, it acts more like an AI coworker; bringing together firm knowledge, client context, and workflow intelligence so teams can move smoothly from search to decision-making to execution.
That matters, because consulting has always been a knowledge business. Firms monetize expertise, pattern recognition, proven methods, and the ability to turn insight into outcomes. Traditionally, that advantage lived across decks, shared drives, partner networks, playbooks, project teams, and institutional memory. AI changes the game by making that knowledge more accessible, more actionable, and more scalable across every role.
Why now: The consulting model itself is changing
Most firms still operate around a familiar structure: business development and scoping, discovery and strategy, project implementation, and post-delivery retention and change management. All of this is supported by standardized methods, reusable IP, and a revenue model still built around time and materials.
That model is now under pressure from both sides.
On the client side, buyers want faster answers, stronger evidence, and clearer ROI from AI work. On the delivery side, GenAI is automating many of the tasks that once required junior consultants and justified large project teams, offering an accelerated path to ROI. Internal industry research points to a broader shift away from the classic consulting pyramid toward new role structures built around AI facilitators, engagement architects, and senior client leaders. PwC’s 2026 AI predictions make a similar point: companies are moving toward focused, top-down AI programs, more agentic workflows, and a growing need for AI-savvy generalists who can orchestrate work instead of simply executing narrow tasks.
The implication is clear: the winning consulting firms will not just use AI to trim low-value work. They will use it to redesign how the firm sells, delivers, learns, and proves impact.
Why consulting needs shared context, not more point tools
Consulting firms aren't lacking for data. They're lacking context.
Critical knowledge is spread across prior proposals, case studies, CRM records, market research, staffing histories, meeting notes, delivery artifacts, outcome reports, and learning materials. Every stage of the consulting lifecycle depends on quickly finding, interpreting, and applying that context. Yet, most teams still piece it together manually.
That is why another department-level copilot is not enough. The real opportunity is not just faster answers, but an AI coworker that can carry context across systems and move work forward. Firms need a horizontal approach that can connect knowledge across the business, work securely over existing permissions, and support both assistants and agents in the same system. Otherwise, they risk creating new islands of automation, low adoption, and weak ROI.
How AI creates value across the consulting lifecycle
Business development and project scoping
AI can help teams locate relevant case studies and prior proposals, summarize active deal status and stakeholder risk, ingest an RFP alongside public and internal account context, and create thought-leadership content. More importantly, it can collaborate alongside teams to develop branded proposals; turning firm knowledge into polished drafts and reusable assets.
Discovery and strategy
During discovery, teams can surface the right SMEs, pull forward lessons learned from similar engagements, synthesize internal client data with external market signals, generate initial hypotheses, and build scenarios with explicit assumptions and tradeoffs. Instead of spending cycles just gathering context, consultants can spend more time shaping the client recommendation.
Project implementation
Once the work begins, AI can help keep delivery on track by finding relevant deliverables across systems, recommending staffing and skill matches, generating handoff notes, summarizing decisions and action items, updating RAID logic, and surfacing schedule or quality risks earlier.
Post-delivery evaluation, change management and client retention
After delivery, firms can use AI to verify benefits against promised KPIs, summarize account health from multi-channel signals, generate benefits realization reports, flag renewal risk, and turn outcomes into reusable proof points and advocacy assets.
Learning and development
Because consulting is still a people business, AI can also support role-based learning, 30/60/90 plans, adaptive curricula, verified knowledge creation, and better onboarding across the firm. That becomes increasingly important as firms rethink the old apprenticeship model and look for ways to help junior talent ramp faster with better context.
Together, these use cases map directly to the firm outcomes that matter most: revenue per consultant, cost per engagement, utilization, time-to-staff, on-time/on-budget delivery, benefit realization, and renewal and expansion.
What to look for in an AI platform for consulting
A capable AI platform for consulting workflows needs more than strong models—they need the right operating layer with these components:
- A platform that can unify enterprise knowledge across connected applications and make it easy to retrieve relevant context without forcing a rip-and-replace of the existing stack.
- A centralized repository with assistants and agents in one plane, so teams can move from finding information to taking action without bouncing between disconnected tools.
- Built-in security and governance features. Trust isn’t optional, it’s foundational. Client data has to remain permission-aware, auditable, and protected from model-training misuse.
- Value measurement frameworks that can measure AI performance and results. AI transformation is increasingly about focused bets, proof points, and workflows redesigned for outcomes, not scattered experiments with vague productivity claims.
Why Glean for consulting
Glean helps re-imagine the client experience and create an operational advantage by making a firm’s knowledge base more usable across business development, delivery, and retention workflows.
It also helps firms pivot towards a new business model: delivering AI-enabled outcomes, shifting from a pyramid to a diamond structure, and building credibility as an AI-first partner for their own clients.
Most importantly, Glean helps firms do three things at once: deliver excellence, build trust, and close the impact gap. As an enterprise-ready AI coworker, Glean turns enterprise context into action, protects client data through permissions-aware access, and helps teams move from insight to execution faster.
A customer example: phData
phData offers a strong example of how AI can create value inside a technical services firm. As a data and AI consultancy, phData adopted Glean to break down knowledge silos and create a secure, scalable AI layer across the business. What started as a better way to connect knowledge has expanded into broader use of Glean Assistant, Agents, and Code Writer across teams.
That matters because the challenge is not just finding documents faster. It is helping technical teams get up to speed quickly when they are onboarding, starting a sprint, understanding a codebase, or catching up on past customer conversations. By connecting context across systems, phData can make the knowledge behind delivery work easier to access and easier to act on.
phData is also using Glean to bring AI more directly into day-to-day engineering workflows. Instead of forcing teams to piece together context across tools, Glean helps them move from question to action inside the systems they already use — whether that means surfacing the right information, generating code, or helping kick off follow-on work in tools like Jira and GitHub. The result is less friction, less tool switching, and a smoother path from understanding to execution.
That impact shows up in customer-facing work too. phData has used Glean to reduce the time engineers spend on research and providing solutions to customers from days to hours — a strong example of how better access to enterprise context can improve both internal productivity and external delivery. For consulting and technical services firms, that is the bigger lesson: the value of AI is not just faster output. It is giving teams the context they need to move faster, work with more confidence, and deliver better outcomes for customers.
From time and materials to measurable outcomes
The consulting firms that win in the next phase of AI adoption will be the ones that redesign work around shared context, human judgment, and measurable impact.
In practice, that means more than making consultants faster at producing deliverables. It means enabling teams with an AI coworker that can operationalize what they already know — turning past proposals, case studies, client context, and delivery knowledge into an advantage teams can actually use. The result is better-informed junior consultants, engagement leaders with clearer visibility into delivery risk and staffing, and go-to-market teams that can respond faster with more repeatable, higher-quality work.
That is where AI becomes strategically valuable for consulting firms: not as a point tool for isolated productivity gains, but as a way to make expertise more accessible, execution more consistent, and outcomes more measurable across the lifecycle of an engagement.
Ready to see how Glean can help consulting firms turn knowledge into client impact? Get a demo to explore how Glean supports business development, discovery, delivery, and post-engagement workflows across the consulting lifecycle.
