Something structural is shifting in consulting - and the firms paying attention are rethinking how they staff, price, and deliver client work as a result.
Clients are pushing back on people-heavy, time-and-materials engagements. They're asking harder questions about what they're actually paying for, and in some cases they're building internal capabilities rather than renewing multimillion-dollar consulting contracts. The era of charging by the hour for work that AI can now accelerate dramatically is under visible pressure - and the consulting industry is feeling it.
Nowhere is this tension more acute than in financial services technology delivery, where data migration sits at the center of nearly every major transformation program. It's the workstream that consumes the most analyst hours, carries the most project risk, and is most likely to determine whether a client engagement ends with confidence or – in the worst case – with a lawsuit.
The firms finding a path forward are the ones investing in AI-powered delivery capabilities - not as a marketing claim, but as a genuine operational shift that changes what they can promise and reliably deliver.
The Billing Model Under Pressure
The numbers behind the shift are striking. Business Insider reported in November 2025 that McKinsey disclosed roughly a quarter of its global fees now come from outcomes-based arrangements - a notable departure for an industry where traditional time-based billing has dominated for decades. EY's leadership has openly acknowledged the same pressure, with executives suggesting that AI could push consulting toward a "service-as-software" model where clients pay for results rather than labor. PwC, meanwhile, reduced its global headcount by more than 5,600 in 2025 - a signal that the people-heavy delivery model is already under structural strain.
The underlying tension is straightforward: AI makes consultants dramatically more productive, but most revenue models still depend on billable hours. A task that once required 60 hours can now be completed in 6. If firms deploy AI aggressively, they either earn less revenue for the same work or they have to fundamentally rethink how engagements are scoped and priced.
Buyer expectations are shifting - clients increasingly want to pay for results. The pressure is real and it's intensifying. Consulting firms that once relied on junior teams to churn through data-heavy work are now discovering that clients can replicate that output with an off-the-shelf AI tool and a couple of their own analysts - and they're asking why they should keep paying consulting rates for it.
When Delivery Fails, the Stakes Are High
The pressure on consulting firms isn't only coming from pricing conversations. It's coming from clients who are done absorbing the cost of programs that don't deliver.
In September 2025, Zimmer Biomet filed a $172 million lawsuit against Deloitte Consulting over a botched SAP S/4HANA implementation. The complaint alleged that Deloitte misrepresented its capabilities, assigned undertrained and constantly rotating offshore teams, and concealed system defects before a July 2024 go-live that left the company barely able to ship products, issue invoices, or generate basic sales reporting. The total damages sought included $94 million in fees paid to Deloitte, $15 million in additional remediation invoiced by Deloitte itself, and $72 million in Zimmer Biomet's own post-go-live costs.
The case is still working through the courts. But regardless of outcome, it illustrates a broader dynamic: clients are no longer absorbing failed technology programs quietly. They are quantifying the damage and pursuing accountability. And for the consulting firms delivering these programs, the risk profile of a poorly managed implementation has grown considerably.
In financial services -- where a data error doesn't just cause operational disruption but can trigger regulatory scrutiny, client relationship damage, and audit findings - the consequences of delivery failure are even more pronounced. A migration that goes wrong at a bank or asset manager isn't just a project problem. It's a systemic risk event.
Where Financial Services Delivery Is Uniquely Hard
Financial services technology programs put consulting teams in a particular bind. The work is genuinely complex, the data is dense, and the tolerance for error is narrow - yet the pressure to compress timelines and control costs is as high here as anywhere.
Consider what a typical data migration engagement looks like in this space. A bank modernizing its legacy infrastructure, an asset manager consolidating data after an acquisition, or an insurance carrier migrating off a legacy policy administration system - each arrives with decades of client data stored in formats that weren't designed for portability. Position histories across multiple asset classes. NAV records from prior administrators. Interest calculations embedded in COBOL modules that haven't been touched since the 1990s. Counterparty hierarchies full of historical exceptions and overrides.
The consulting team's job is to move all of that accurately, quickly, and in a way that satisfies both the client's operational requirements and the regulatory frameworks that govern their data. BCBS-239 for global systemically important banks. ORSA and Solvency II for insurers. The compliance dimension means that reconciliation isn't just a technical milestone - it's an evidence-gathering exercise that regulators will review.
And yet, this is precisely the work that has traditionally been done manually: analysts comparing schemas side by side, writing transformation rules by hand, iterating with target systems through slow feedback loops. It's time-intensive, expertise-dependent, and difficult to scale.
The Legacy System Problem No Standard Playbook Solves
A significant share of financial services programs involve migrating data off legacy systems - mainframes running COBOL, AS/400 environments running RPG, or custom platforms whose original developers retired years ago. For consulting teams, this creates a structural challenge that sits upstream of everything else: the source system is a black box.
The business logic governing how data is calculated, transformed, and stored in these systems was often never externally documented. It lives in the code - in tens of thousands of COBOL modules, in conditional branching logic written to solve a specific business problem and never touched again. When a consulting team needs to understand why a risk calculation produces a particular result, or how two legacy fields need to be combined before they can map to a target schema, they often have no reliable starting point.
The traditional answer has been to engage the institution's mainframe specialists - a small, typically overburdened group who are simultaneously managing live operations and fielding questions from the migration project. Analysis that should take days can take weeks. And when those specialists retire, the institutional knowledge goes with them.
Contextual data lineage changes this calculus entirely. AI-powered platforms can parse thousands of COBOL or RPG modules and surface the calculation logic, data flows, field relationships, and branching conditions embedded in legacy code - in minutes rather than months. For consulting teams, this means arriving at the analysis phase with a structured, searchable map of what the legacy system actually does, before a single record is moved.
That foundation changes everything that follows. Learn more about what contextual data lineage reveals in legacy financial systems.
How AI Changes the Delivery Economics and Predictability
For consulting firms navigating the shift toward outcomes-based pricing, AI-powered data migration tooling offers a concrete path to better margins and better delivery - simultaneously.
The efficiency gains are measurable and meaningful. Business analysts on AI-assisted migration projects work up to 6x faster. Migrations complete up to 80% faster overall. and the work that once required senior technical resources increasingly flows through analysts with the right platform behind them.
In a financial services context, these gains show up in specific, high-stakes ways:
- Early data profiling gives consulting teams a clear picture of source data quality - completeness rates by field, currency distributions, unique values, anomalies -- before execution is underway. For a senior project manager, this visibility is the difference between a defensible timeline and one that keeps slipping.
- Predictive field mapping eliminates the blank-spreadsheet start. AI-generated mapping predictions -- ranked by confidence, ready for review -- compress what is typically the most expertise-heavy step into a validation exercise. Business analysts can own the work end-to-end, without requiring a senior SME to perform what is fundamentally an analyst-level task.
- AI-assisted transformation handles the precision that financial data demands: standardizing identifiers, reformatting currency codes, reconciling accounting bases, applying calculation logic consistently across millions of records. Work that previously required a systems engineer can be completed by a business analyst -- which directly affects how engagements are staffed and priced.
- A single, governed platform replaces the sprawl of spreadsheets, email threads, and siloed tools that typically fragment a migration engagement. Every mapping decision, transformation rule, and data file lives in one place -- visible to every teammate, maintained in a consistent state, and managed through a structured workflow rather than tribal knowledge. For consulting firms, this is what makes delivery governable at scale: the engagement doesn't live in an analyst's head, it lives in the platform.
For consulting firms, the deeper advantage is structural. Zengines is the single source of migration truth -- where every decision is made, every rule is stored, and every teammate works from the same live picture. Profiling feeds mapping. Mapping feeds transformation. Transformation feeds testing. The engagement lives in the platform, not in any one person -- which means it's scalable, transferable, and consistently deliverable regardless of who is staffed on the next one.
From Effort to Outcomes: Making the Transition Real
The shift to outcomes-based delivery isn't just a pricing conversation - it's an operational one. Firms can't credibly commit to delivery outcomes on fixed-fee or risk/reward structures if their underlying methods are still dependent on manual, labor-intensive processes that are inherently unpredictable.
This is the core reason why AI tooling matters so much for consulting firms right now. It's not about replacing consultants - it's about giving delivery teams the infrastructure to make commitments that they can keep. When field mapping is AI-assisted, reconciliation is automated, and data quality is profiled upfront, project timelines become far more predictable. And predictability is the prerequisite for outcomes-based pricing.
Firms building these capabilities are finding that they compete differently. They can take on fixed-fee engagements with genuine confidence rather than aggressive contingencies. They can staff programs leaner without sacrificing quality or pace. They can have more credible conversations with financial services clients who have been burned before and are scrutinizing methodology more carefully than they used to.
The Big 4 and major systems integrators are all investing in AI platforms - EY's AI Agentic Platform, Deloitte's Zora AI, KPMG's and PwC's respective investments - but rolling out new tooling across thousands of staff, multiple service lines, and global operations takes time.
The firms moving fastest are the ones being strategic about where AI solves the most acute delivery problems first. In financial services technology programs, that means data migration and legacy system analysis.
What This Means for How Firms Compete
Financial services clients have long memories when it comes to failed implementations. Many have lived through at least one program where data issues surfaced late, caused delays, and required expensive remediation. They ask harder questions in proposal stages now, and they're paying close attention to how prospective partners describe their methodology - not just their credentials.
Consulting firms that can demonstrate AI-powered migration capabilities as a concrete, operational practice - not just a line on a capability slide - are differentiating themselves in a market where the work is increasingly scrutinized and the pricing conversation is shifting. That differentiation translates directly into faster delivery, lower cost, reduced probability of late-stage surprises, and more defensible outcomes for clients whose data environments are regulated and complex.
The firms that navigate this moment well won't be the ones that simply talk about AI. They'll be the ones that have embedded it where delivery risk is highest - and in financial services technology programs, that starts with data.
For more on the specific challenges that make legacy financial system migrations difficult to de-risk without the right tooling, see Why It's So Hard to Leave the Mainframe.
Delivering a financial services technology program that involves data migration or legacy system analysis?
Zengines partners with consulting firms and systems integrators to accelerate data migration delivery, unlock legacy system business logic, and produce the audit-ready documentation that financial services clients and regulators require. Schedule a demo to see how it works, or explore our resources library for more on AI-powered data conversion and contextual data lineage.
