Executive Summary
The next wave of value in financial services will come from reinventing how the business works, with artificial intelligence (AI) built into the everyday workflows that drive revenue, client experience, and cost. Roughly 95% of wealth and asset management firms have already scaled generative AI across multiple use cases. Across financial services, the share of firms using AI in operations has risen from about one third to roughly 80% in a single year.
This paper will examine how financial services firms can move from a set of promising pilots to a transformation that reaches production and stays there, using a real-world example of how Coforge enabled a leading US wealth and asset management firm to make that shift.
Together, we moved from a long list of AI possibilities to a focused, executable transformation built around five initiatives and three sources of business value. What follows is the story of how the work took shape, and the repeatable approach behind it: A six-step blueprint for turning AI ambition into lasting business change.
Reframing the Opportunity: A Business Reinvention Anchored in AI
Many AI conversations begin as procurement exercises. Deploy a model, automate a process, upgrade a platform. This one began differently. From the first conversation, we treated the opportunity as a business reinvention anchored in AI: a chance to rethink how sales, wealth, and operations teams create value, with AI as the connective tissue across them, rather than a feature added on the side.
That framing changes what success means. A technology upgrade is judged by delivery milestones, but a transformation is judged by business outcomes, such as stronger pipeline conversion, more productive advisors, and a lower cost to serve. The distance between those two definitions is where most AI programs lose momentum.
Successful organizations will treat AI as a change to the operating model, not a technology experiment confined to a lab.
The real opportunity was business reinvention, with AI as the anchor.
The Four Stages of Successful AI Transformation
The work unfolded in four stages, each building on the one before it:
Together, this represents an effective route from scattered ideas to developing repeatable capabilities that businesses can run on.
Stage One: From a Long List of Ideas to a Focused Opportunity
The work began where most enterprises begin, with abundance. A long list of AI value streams had surfaced across the client's business lines, far more than any organization could sensibly pursue at once.
The important move was not generating more ideas. It was bringing discipline to the ones already on the table. We applied a structured prioritization framework to rank and qualify each opportunity against business impact and feasibility, then narrowed the field to roughly eight high-value use cases for the initial pitch. They clustered around four areas:
- Sales intelligence, for sharper account and pipeline prioritization
- Trade process automation, to reduce manual effort in core operations
- Transfer agency insights to surface value from operational data
- Advisor experience, to put timely, personalized intelligence in advisors' hands
The change that mattered here was one of mindset: We helped the client shift away from an open-ended catalog of AI possibilities and toward a focused set of use cases tied to business impact. That is the difference between an innovation backlog and a real transformation agenda.
Stage Two: Designing for Transformation, Not Just Technology
This is where the work became most valuable. Rather than answer the opportunity with a conventional proposal, we reframed it. An early AI workshop and a clear “North Star” set the direction from the outset, anchoring the conversation on three things a use-case list can never deliver on its own:
- Enterprise-wide transformation, in place of isolated point solutions
- Cross-business value pools that span functions and P&Ls
- Measurable outcomes tied to the business, not to technology delivery
As a strategic partner, we did not pitch AI. We worked alongside the client's leaders to shape a shared ambition, elevating the conversation from “What can AI do?” to “What should this business become?”
That was the exact moment the work moved from a proposal into transformation design.
We did not lead with AI. We shaped an ambition with the client.
Stage Three: Co-Creating the Path to Execution
Ambition only counts if it survives contact with reality. The third stage turned the North Star into a concrete plan and into a shared way of working.
From proposal to work plan. We built a full transformation workplan that carried each opportunity from business case to pilot to production scaling, organized into more than eight workstreams. The center of gravity moved from deciding what to do to working out how to make it real.
Deep co-creation with the business. Progress came through close, day-to-day engagement with stakeholders across sales, wealth, and investment manager services, alongside subject matter experts and transformation leaders. We refined use cases against real business data and expert input, and grounded them in the realities of the underlying platforms, including customization limits and operational complexity.
AI transformation happens in the messy details, not in slides.
That lesson sits at the heart of this stage. The value was not created in a boardroom presentation. It was created in the iterative, sometimes untidy work of shaping each value stream around how the business actually runs.
Stage Four: AI Embedded in the Business
The result was not a sale. It was a transformation that grew deliberately, beginning with an initial idea, sharpening into a defined use case, taking shape as a work plan, and arriving at live execution. It took concrete form as a portfolio of five AI initiatives:
- An advisor experience and next-best-action engine
- Agency accounts intelligence
- Trade process automation
- A transfer agency sales data analysis solution
- Sales data analysis for pipeline and account prioritization
Just as important, these initiatives were organized around three sources of business value rather than technical categories:
- Client experience, through advisor intelligence and personalization
- Revenue growth, through better pipeline and account prioritization
- Cost and efficiency, through automation of trade operations
What defines the outcome is that AI is built into core business workflows, not running alongside them. That’s the difference between a tool that the organization owns and a capability the business now runs on.
Inside the Delivery Engine: A Structured, Phased Workplan
Discipline in delivery is what separates ambition from results. Every use case moved through a structured, four-phase workplan, from insight to proving value, to review, to rollout, with clear milestones and governance checkpoints instead of an open-ended build. A single opportunity, for illustration, runs on a tight cadence:
| Scope element | Milestone | Milestone |
|---|---|---|
| Stakeholder intel | Map the current workflow and key personas; engage the business to identify pain points. | Week 1 |
| Business value | Define core problems and user needs; quantify impact and success metrics. | Week 1 |
| Scope definition | Set scope boundaries, key deliverables, and engagement focus areas. | Weeks 1-2 |
| Technical solution | Identify priority value streams and current architecture; define the future-state design. | Week 2 |
| Sponsor sign-off | Validate the business case and target-state solution with the sponsor. | Week 2 |
| MVP and operating model | Build the MVP; define the operating model, team structure, milestones, and delivery approach. | Week 3 |
| Internal review | Review with the pod owner and incorporate feedback. | Week 4 |
| MVP rollout | Kick off rollout and consolidate cross-pod inputs into an integrated executive transformation. | Week 4 |
Illustrative timeline for a single opportunity. Governance is maintained through layered checkpoints, with PMO reviews at each phase and senior-executive checkpoints at the insight and rollout stages.
A Six-Step Blueprint for Scaling AI Across the Enterprise
This engagement was not a one-off. Its lasting value lies in the fact that the approach can be repeated.
We distilled the key learnings into a six-step blueprint that can be replicated for any enterprise. It reflects how we work as a strategic partner: Orchestrating AI-led business transformation that goes beyond simple delivery and traditional consulting.
| Step 1: Surface and structure the opportunity | Gather the full landscape of AI possibilities across business lines, then apply a rigorous prioritization framework to shortlist the highest-value, most feasible use cases. |
| Step 2: Set the North Star | Anchor the program in an enterprise ambition, not a feature list, framing cross-business value pools and measurable outcomes before any build begins. |
| Step 3: Co-create with the business | Work shoulder to shoulder with business owners, subject-matter experts, and transformation leaders, refining each use case against real data and the realities of the underlying platforms. |
| Step 4: Build the transformation workplan | Translate ambition into an executable plan that carries every initiative from business case to pilot to production scaling, organized into clear workstreams. |
| Step 5: Prove value through phased delivery | Run each use case through structured phases with defined milestones and governance checkpoints, proving business value on a tight cadence before scaling. |
| Step 6: Embed and scale into core workflows | Move beyond isolated tools by building AI directly into the workflows that drive client experience, revenue, and efficiency, then extend the pattern across the enterprise. |
Conclusion
AI adoption is now table stakes. AI-led transformation is the differentiator. The most successful firms are the ones that resist the easy gravity of the pilot and instead reshape their operating model around AI, deliberately, in partnership, and rooted in the operational detail where value is actually created.
The story above shows what this approach looks like in practice: moving from a long list of possibilities to five initiatives embedded in the business and delivering against three sources of value. The six-step blueprint makes it repeatable, and Coforge provides orchestration, co-creation, and delivery discipline to turn AI ambition into real business change.
About the Author
Global Head, Strategic Solutions and Business Transformation Consulting
Pedro Silva works with client executives and Coforge’s technology and advisory partners to co-author transformation agendas – translating enterprise priorities into board-level value cases, and into the operating and commercial models that make them executable. With experience across strategy and corporate finance, management consulting, enterprise transformation, and principal investing, Pedro brings deep expertise in large-deal origination, commercial model design, and AI-enabled value creation. He works with executives who have moved past AI experimentation and need it to show up in the numbers.