What do Financial Services firms need from their partners in the age of AI?
Artificial intelligence will eliminate many low-value consulting tasks. But it will increase demand for firms that combine domain expertise, engineering capability and regulatory understanding. Financial services organisations increasingly need consulting partners who can help them implement AI safely and at scale.
For the past two years, financial services firms have been bombarded with two conflicting narratives about AI.
On one side is the breathless excitement: automated analysis, instant code generation, synthetic data, faster transformation, lower costs. On the other is the existential dread: if AI can do so much of the thinking, designing, drafting and analysing, what does that mean for the armies of consultants who traditionally deliver these services?
The comparison often made is Blockbuster – too slow to adapt, disrupted overnight. But the truth is much closer to what the original article argued: AI threatens consultants, but consulting will not go the way of Blockbuster.
If anything, the financial services sector is entering a new era, one where the value of consulting becomes clearer, not weaker, and where firms like Broadgate and Ortecha are already reshaping their services to ensure they deliver the right blend of technical capability, strategic judgement and executional support that AI alone cannot.
This is the fundamental reason why we made the decision to merge our firms combining the power of technology, data and AI capabilities, with business focused knowledge, to empower our client’s capability to deliver more successful outcomes.
The real shift is not that AI will replace consulting. It is that AI will require organisations to evaluate partners differently and will expose which consulting firms are adding real expertise and which were simply adding labour.
Is AI Already Transforming Financial Services Consulting?
Financial services has long been a data-rich sector, and the arrival of generative AI has hit the industry with unusual speed. Risk teams are experimenting with model-assisted validation. Data teams are accelerating pipeline and documentation work. Developers are quietly tripling throughput. Compliance teams are trialling automated monitoring, summarisation, and synthesis.
And yes, AI can now do many tasks that used to be billed out at $800/day.
- First-draft business requirements
- Basic data profiling
- Architecture diagrams
- Policy and control mappings
- MI report generation
- Even “explain this regulatory text and how it applies to us”
Every consultancy knows this. Every client knows this. And the smart ones are adapting fast.
But here is the critical point: automation of low-value tasks does not remove the need for high-value advisory. It simply exposes which firms were relying on junior labour and PowerPoint templates, and which firms actually solve problems.
Financial Services organisations don’t need bodies. They need outcomes.
AI removes the noise and leaves the signal.
Will Consulting Follow the Same Pattern as Accountancy?
If we can draw a comparison with the Accountancy industry, despite waves of automation since the 1980s, the industry grew. Why? Because:
- Regulation became more complex, not less.
- Automation shifted work toward higher-value oversight and judgement.
- Clients increasingly outsourced specialised thinking, not routine tasks.
The same pattern is emerging in AI consulting in financial services.
AI will eliminate:
- Bloated workstreams
- Manual document production
- Junior-heavy slide factories
- Generic “best practice” rehashing
But it will increase demand for:
- Strategic AI adoption advice
- Data governance design that reflects real-world constraints
- Operating model reengineering
- Human-AI workflow design
- Model risk and control frameworks
- Vendor selection and integration
- Change management rooted in behavioural science
- Technical validation of AI outputs
These are not tasks ChatGPT will replace. These are tasks consultancies with this specific expertise will thrive on.
As AI raises the bar for what consulting needs to deliver, the expectations financial institutions have of their partners are also changing.
What Financial Services Firms Are Actually Asking For
Over the past year we have spoken with more than 50 Chief Information Officers, Chief Data Officers, Chief Risk Officers and transformation leaders across banks, insurers, investment & wealth managers and fintechs. Despite different business models, their needs are remarkably aligned.
1. Help turning AI ambition into something real
Most firms have “AI strategy slides”. Fewer have delivery. Even fewer have guardrails, governance, controls or ways to scale safely.
They don’t want hype.
They want: “What can we implement quickly, using the data and systems we actually have?”
2. Clarity on risk, regulation and responsible adoption
The UK, EU and US are all taking different regulatory positions.
AI creates new model-risk categories, new operational risks, and new audit expectations.
Firms want partners who understand:
- PRA & FCA expectations
- Digital operational resilience
- Data privacy interactions
- Model governance
- Vendor and cloud risk
AI capability without regulatory literacy is a liability.
3. Real engineering and data capability, not slideware
This is one of the biggest shifts. Clients want consultants who can:
- Build pipelines
- Integrate LLMs
- Evaluate outputs for accuracy and bias
- Implement guardrails
- Automate workflows
- Deploy solutions in production
FS firms have lost tolerance for the “advise but don’t implement” model.
4. Leaner, outcome-driven teams, not inflated programmes
This is where the combined Broadgate & Ortecha model fits perfectly.
Clients want:
- Small senior teams
- Fast iteration
- Clear deliverables
- No unnecessary bodies
- No long-winded discovery phases
- Measurable impact within weeks
AI has accelerated expectations. People want results fast.
What Differentiates Consultancies in the AI Era?
The firms that will disappear are those whose value proposition was based on labour, not expertise.
The firms that will win have three things:
1. Judgment and experience
AI can generate 20 solutions.
Only a seasoned consultant can tell you which one won’t bankrupt you later.
2. Real technical depth
If a consultancy can’t build, test or validate AI-driven systems, it will lose relevance quickly.
3. The ability to integrate AI inside their own delivery model
This is the credibility gap growing across the industry.
We’ve rebuilt our delivery approach to:
- Use AI for drafting, analysis, synthesis and documentation
- Reduce project overhead for clients
- Let our consultants focus on design, strategy, facilitation, architecture and risk
- Deliver more value per pound spent
Clients can see the difference immediately.
How Financial Services Firms Should Evaluate AI Consulting Partners
As financial services firms move from AI experimentation to real implementation, many are reassessing the consulting partners they work with. The question is no longer simply who can advise on AI strategy, but who can help organisations design, build and govern AI safely in a regulated environment.
When evaluating existing or potential partners, several capabilities tend to matter most.
1. Can they translate AI ambition into a practical roadmap?
Many organisations already have AI strategies. The challenge is turning those ambitions into something deliverable.
Strong partners should be able to assess your data, architecture, people, controls and regulatory exposure, and translate that into a roadmap grounded in the systems and constraints you actually operate within.
2. Do they understand how AI changes operating models?
AI does not simply introduce new tools. It changes how work flows through organisations.
Effective partners help design operating models across key functions such as risk, data, engineering, compliance and operations so that human expertise and AI capabilities work together safely and efficiently.
3. Can they move beyond advice into engineering delivery?
One of the biggest shifts in the market is the expectation that consulting partners should be able to build and deploy solutions, not just recommend them.
That means capabilities across data engineering, LLM integration, workflow automation and production deployment.
4. Do they understand model risk and responsible AI?
For financial services firms, governance is not optional.
Partners should be able to help design guardrails, validation approaches and assurance frameworks that align with regulatory expectations around model risk, operational resilience and data governance.
5. Can they identify where AI genuinely reduces cost?
AI often promises efficiency, but real savings only emerge when automation is applied to the right processes.
Experienced partners should be able to identify where AI can meaningfully reduce manual effort across reporting, documentation, underwriting, risk analysis and customer operations.
6. Do they support organisational adoption?
Even well-designed AI systems fail if people do not trust or understand them.
Successful AI programmes must therefore include project streams for change management, communication and training, to ensure new tools are understood, adopted and used effectively, with confidence.
Conclusion: AI Isn’t Killing Consulting. It’s Killing Bad Consulting.
Financial services firms are not looking for more PowerPoint decks. They want partners who can help them:
- Move fast
- Stay compliant
- Reduce cost
- Increase capability
- Deploy usable technology
- And make AI safe and scalable
As AI becomes embedded in financial services operations, the way organisations evaluate consulting partners will inevitably change. The focus is shifting away from labour-heavy programmes and toward firms that combine domain knowledge, engineering capability and real delivery experience.
AI is forcing the consulting industry to evolve, and the demand for expertise has never been higher.
The consultancies that succeed will be those that combine deep financial services knowledge, strong technical capability and human judgement.
That is exactly the thinking behind the Broadgate and Ortecha combination, bringing together data, AI and domain expertise to support financial institutions as they move from experimentation to scaled adoption.
