BTWS 2027 | Why Financial Services Transformation Stalls: The CTrO's Challenge of Aligning the Enterprise

Chapter headingChapter subheadingTushar Rao, Group Chief Transformation Officer at JMMB Group, explores how CTrOs can break down organizational silos, lead change across functions, and build the accountability needed to deliver lasting transformation in financial servicesContents

Thank you for reading Infographics | Landing Page Template

25-27 January 2027 | Hilton Orlando Lake Buena Vista

Tushar Rao

Group Chief Transformation Officer

JMMB Group

WHY FINANCIAL SERVICES TRANSFORMATION STALLS:

The CTRO's Challenge of Aligning the Enterprise

Introduction

Financial services organizations are under pressure to transform faster, control costs, manage regulatory complexity and meet rising customer expectations. Yet investing in new technology or launching more initiatives does not guarantee meaningful change.

The challenge is making transformation work across the entire organization. Strategy, operations, technology, risk, and people must all move in the same direction – with clear priorities, shared accountability, and a consistent focus on business outcomes.

For Tushar Rao, Group Chief Transformation Officer at JMMB Group, this means looking beyond individual projects to address the organizational conditions that

determine whether transformation succeeds.

From aligning stakeholders and balancing innovation with governance to securing employee adoption and measuring value, CTrOs must connect the moving parts of the business to turn strategic ambition into lasting results.

In this interview, Tushar shares his perspective on breaking down organizational silos, prioritizing the changes that matter and building an enterprise-wide approach to transformation in financial services.

We create an environment with guardrails that allows innovation, not uncontrolled experimentation. We establish approved sandboxes using masked or synthetic data, risk tiers, time-boxed tests, and predefined exit criteria. Low-risk internal use cases move quickly; customer-facing, credit, advice, or autonomous decisions receive stronger review.

Risk, compliance, legal, cybersecurity, data, and business owners join at the design stage, rather than approving at the end. Every experiment has accountable ownership, human oversight, traceable data, monitoring and a production pathway.

This approach treats governance as an accelerator: teams know the boundaries, reusable controls reduce repeated reviews, and promising ideas can scale without weakening privacy, resilience, fairness, or customer trust.

Enabling experimentation in this controlled lab environment will allow for experimentation without significant risk. By providing a clear path for concepts in the experimental zone to graduate to real use, with guardrails and protections assessed on promotion.

Experimentation requires a consistent environment within the enterprise-defined platform and data at the foundation; as people determine adoption, experimentation will thrive.

Financial services have some of the strongest controls and governance requirements of any industry. How do you create enough room to experiment with AI and emerging technology without compromising the controls that protect the business?

Where do you see financial services organizations getting the balance between AI innovation and AI governance wrong today? Are they moving too slowly because of risk – or too quickly without understanding the consequences?

The mistake is treating innovation and governance as sequential choices.

Moving slowly often means committees, unclear accountability, and controls applied equally to every use case. Moving too quickly means deploying tools without data, model inventories, testing, human oversight, or vendor scrutiny.

The better approach is proportional governance: classify uses by customer, financial, regulatory, and operational impact, then match assurance to risk. Establish non-negotiable principles but make approval paths fast and transparent. Leaders should measure speed and safety, including time to production, incidents, override rates, drift, and realized value.

Good governance enables scaling; weak governance creates either paralysis or unmanaged exposure. The key will always be the availability of data (test/masked data initially) early and governed; it does not need to be fully right, but how do we access and scale data?

The most important thing is to get the data right first – including test data. Until you have that, you’re just playing kickball at recess. Innovation with AI requires the use of valid data sets (in test format, masked, etc.), and the enterprise use of AI is unlocked within the data context.

Everyone is talking about AI investment, but the harder question is where that investment actually creates value. How do you decide which AI opportunities deserve funding – and which should be stopped before they become expensive experiments?

We fund problems worth solving, not technologies. Each opportunity must connect to an outcome such as client experience, revenue, fraud reduction, compliance effectiveness, processing time, or capacity released.

We assess value size, data readiness, workflow fit, control complexity, adoption effort, integration cost, and scalability.

Tests should prove the riskiest assumptions first, using baseline measures and thresholds. Funding should be released in stages, with business owners accountable for benefits, not technology teams alone.

Stop initiatives when the use case lacks a committed owner, trusted data, measurable outcomes, economics, or a credible production path. Disciplined termination protects capital and redirects talent toward higher-value opportunities.

What does a credible AI business case look like in financial services today? What evidence do you need before you are willing to move from pilot to enterprise deployment?

A credible business case begins with a specific, defined business problem, baseline performance and a named owner. It quantifies benefits across revenue, cost, risk, client outcomes, and capacity, while including data preparation, integration, controls, change, vendor, and operating costs.

Before enterprise deployment, we need to establish evidence from a representative pilot: measurable improvement against the baseline, acceptable accuracy and error rates, user adoption, control effectiveness, resilience, explainability where required and no material customer or conduct concerns.

The use case must also demonstrate scalability, accountable model ownership, clear monitoring, retraining and fallback procedures.

Production approval follows evidence of sustainable value, not an impressive demonstration. Use experimentation environments to prove the assumptions on the business case and establish measures early in the cycle.

How are you thinking about the ROI of AI when the value is not always immediately visible in revenue or cost reduction? For example, better decisions, faster processes, reduced risk or greater organizational capacity?

My preference is to leverage a value scorecard rather than just focusing on cost-savings. Hard benefits include revenue uplift, loss avoidance, reduced handling time, and lower spending.

Equally important are leading indicators: improved decision quality, adoption, and release frequency that led to better customer outcomes. Each measure needs to be baselined, have a target, ownership and measurement frequency.

We also need to track total cost of ownership, adoption, model performance, and risk. Where benefits are indirect, we translate them into economic parameters, such as hours released, losses avoided, or cases detected earlier.

ROI becomes a portfolio discipline, continuously reviewed after deployment. These overall direct and indirect benefits are monitored by a team tracking value realization and validated by finance.

Transformation doesn't fail simply because the technology doesn't work; it fails when people don't change how they work.

In a highly regulated environment, how do you bring people with you through transformation while overcoming resistance, maintaining business-as-usual performance and ensuring new ways of working actually stick?

Change Management is critical in transformation. People adopt change when it solves friction, and they are involved in designing it. Involve teams early, map how roles and decisions will change, and make leaders accountable for adoption. Protect business-as-usual through phased releases, parallel controls, clear cutover criteria, and support.

Training should be role-based and practical, using scenarios and supervised practices rather than generic demonstrations. Team leaders should then reinforce the new process through objectives, routines, and performance conversations.

You need to monitor adoption, exceptions, workarounds, service levels, and control outcomes, then correct quickly. Change only sticks when policies, measures, incentives, technology, and everyday management all support one operating model.

Many financial services firms lack visibility into the true cost of their current operations and processes. Calculating and providing transparency to these costs leads to a motivator for change within the teams – provide the challenge to the teams – with cost, time and quality metrics on problematic areas; encourage them to take an owner perspective.

For instance, if they were building a new company doing the same business today, and knowing the power of modern platforms and AI, how would they approach it?

Transformation often fails long before the technology fails. From your experience, what are the early warning signs that an AI or transformation initiative is heading in the wrong direction?

The earliest warning signs are organizational, not technical. The initiative has no accountable business owner; success is described as deployment rather than an outcome; baseline measures are missing; data issues are deferred; and risk teams are not embedded.

Other signals include scope expansion or change, unresolved integration dependencies, low participation, weak adoption, manual workarounds, and benefits that cannot be traced to financial or customer results. There is a need to avoid pilots that impress but lack production scalability, operating funding, monitoring or support.

The response is decisive: reset scope, assign ownership, validate value and controls, or stop. Continuing without evidence only converts uncertainty into cost.

Transformations are noisy, messy affairs – establish systems of engagement with delivery teams and encourage open feedback in safe spaces for the teams. An early sign of the organization losing steam is these systems of engagement going quiet. Transformations require standardized platforms and data with people driving adoption, not tech.

Financial services leaders are being asked to transform while simultaneously managing regulation, cost pressure, changing customer expectations and an accelerating technology landscape. How do you decide what genuinely deserves your organization's attention?

Use a portfolio lens anchored in client value proposition, regulatory necessity, resilience, economics, and execution capacity. Mandatory remediation and material risk exposures come first, but there is a need to distinguish obligations from discretionary complexity.

Growth and efficiency initiatives are prioritized where they improve customer journeys, simplify processes, strengthen data foundations or create reusable enterprise capabilities and enhance client experience.

Every initiative is scored for value, urgency, risk reduction, dependency, readiness, and resource demand. Sequence foundational investments before advanced automation and reserve capacity for threats.

Continuous portfolio reviews accelerate or reshape initiatives based on evidence, so make sure to focus on the discipline of choosing deliberately.

If we look three years ahead, what will separate financial institutions that successfully capture value from AI from those that simply accumulated a large portfolio of pilots?

The winners will be those who will be able to democratize data. However, we need to experiment with test and masked data in a controlled fashion to test the hypothesis. This enables the winners to build an operating system for AI capabilities, not a collection of demonstrations.

They will have trusted enterprise data products, integration, reusable platforms, product ownership and risk-based governance embedded in delivery. Leaders will own outcomes, while multidisciplinary teams redesign workflows rather than layering AI onto broken processes.

Institutions will industrialize monitoring, security, model lifecycle management, human oversight, and vendor controls. They will also develop workforce capability, measure adoption and reinvest benefits.

They will concentrate resources on a limited portfolio of scalable use cases with measurable customer, risk and economic value. Others will accumulate pilots and technical debt. The foundation starts with the platform and data while people drive adoption.

What made you want to be part of Business Transformation World Summit 2027, and why do you think it is important for transformation and operational excellence leaders to be in the room? What do you hope attendees will take away from the conversations at the event?

I wanted to join the Business Transformation World Summit 2027 because transformation leaders need practical conversations about converting ambition into measurable outcomes.

Financial services are balancing AI acceleration, regulatory expectations, legacy constraints, cost pressure, and changing customer needs.

No institution has every answer, so sharing approaches, failures, and trade-offs strengthens the profession. The room matters because operational excellence, technology, risk, finance, and people leaders must solve these challenges together.

I hope that, as a speaker and as an attendee, we can share our prioritization approaches, governance patterns, and build genuine peer connections.

25-27 January 2027 | Hilton Orlando Lake Buena Vista

Better technology alone will not solve the challenges facing financial services transformation leaders. Lasting progress depends on aligning the entire organization – from business strategy and operational processes to risk, technology and the people responsible for making change happen.

For CTrOs, that means looking beyond individual initiatives to understand how decisions in one part of the business affect execution across the whole enterprise. It means setting shared priorities, establishing clear accountability, bringing employees along and ensuring transformation delivers measurable value without compromising regulatory obligations or business performance.

But achieving that level of alignment is complex, particularly when financial services leaders must balance competing priorities, legacy constraints, cost pressures and evolving customer expectations.

The Business Transformation World Summit, 25-27 January 2027, Hilton Orlando Lake Buena Vista, Orlando, Florida, brings together senior leaders across business transformation, operational excellence, change, AI and the Chief Transformation Officer community.

The summit offers an opportunity to exchange practical approaches, challenge established thinking, and learn how organizations are connecting strategy, people, processes and technology to deliver transformation at scale.

Join your peers at BTWS 2027 to explore how to break down silos, align the enterprise around shared outcomes and turn transformation strategy into sustained business impact.

DOWNLOAD THE 2027 AGENDA

BECOME A SPONSOR