A strategic roadmap for financial services leaders ready to move beyond pilots to enterprise transformation

As financial services leaders, we're at a pivotal moment in the AI revolution. While 88% of organizations are now regularly using AI in at least one business function—up from 78% just a year ago—the gap between adoption and value capture remains significant. McKinsey's latest "State of AI in 2025" report provides critical insights that every financial services executive should understand as they plan their AI strategy for the year ahead.

At Vantage Point, we work exclusively with wealth management firms, banks, insurance companies, and fintech organizations to transform their operations through AI-driven CRM solutions. Based on McKinsey's comprehensive research and our hands-on experience, here's what financial services leaders need to know about AI in 2025—and how to position your firm among the high performers.

The Current State: Wide Adoption, Limited Scale

Most Organizations Are Still Experimenting

Despite widespread AI adoption, McKinsey's survey reveals that nearly two-thirds of respondents say their organizations have not yet begun scaling AI across the enterprise. The breakdown is telling:

• 32% are experimenting with AI (early testing and exploration)
• 30% are piloting AI (implementing first use cases)
• 31% are scaling AI (growing deployment across the organization)
• Only 7% have fully scaled AI across their enterprises

For financial services firms, this presents both a challenge and an opportunity. While many institutions have invested in AI tools—particularly in knowledge management, marketing and sales, and IT operations—most have not yet embedded these technologies deeply enough into workflows to realize material enterprise-level benefits.

Larger Firms Are Leading the Way

Size matters when it comes to AI scaling. Nearly half of respondents from companies with more than $5 billion in revenue have reached the scaling phase, compared with just 29% of those with less than $100 million in revenues. This disparity reflects the significant investment required not just in technology, but in change management, workforce development, and organizational transformation.

For mid-sized financial institutions, this creates both competitive pressure and a clear imperative: developing a strategic approach to AI that goes beyond point solutions to create enterprise-wide transformation.

The Rise of AI Agents: From Hype to Reality

Understanding Agentic AI

One of the most significant developments in 2025 is the emergence of AI agents—systems based on foundation models that can act autonomously in the real world, planning and executing multiple steps in a workflow. This represents a fundamental shift from AI as a tool that requires human direction for each task to AI as a collaborative partner that can handle complex, multi-step processes.

In the financial services context, agentic AI has immediate applications in:

• Service desk management in IT operations
• Deep research in knowledge management
• Customer service automation in contact centers
• Compliance monitoring in risk and legal functions

Current Adoption Landscape

McKinsey's research shows that 23% of respondents report their organizations are scaling an agentic AI system somewhere in their enterprises, with an additional 39% experimenting with AI agents. However, most organizations scaling agents are doing so in only one or two functions, and in any given business function, no more than 10% of respondents report scaling AI agents.

Among industries, technology, media and telecommunications, and healthcare sectors are leading in AI agent adoption, with financial institutions showing moderate but growing adoption, particularly in IT, knowledge management, and risk/compliance functions.

As Michael Chui, Senior Fellow at McKinsey, notes: "AI agents have been the subject of intense buzz and excitement... the use of agents is not yet widespread. This gap highlights the contrast between the great potential that manifests in a 'hype cycle' and the current reality on the ground."

What This Means for Financial Services

For financial services leaders, the message is clear: AI agents represent significant potential, but successful implementation requires careful planning, workflow redesign, and a willingness to iterate. The firms that start experimenting now—with realistic expectations and a focus on specific, high-value use cases—will be best positioned to scale as the technology matures.

AI as a Catalyst for Innovation, Not Just Efficiency

Beyond Cost Reduction

While 80% of respondents say their companies set efficiency as an objective of their AI initiatives, the companies seeing the most value from AI often set growth or innovation as additional objectives. This distinction is critical for financial services firms.

Respondents report the following qualitative improvements from AI use:

• 64% say AI is enabling innovation at their organizations
• 45% report improvement in customer satisfaction
• 45% cite improvement in competitive differentiation
• 45% see enhancement in employee satisfaction

However, when it comes to bottom-line impact, only 39% of respondents attribute any level of EBIT impact to AI, with most reporting less than 5% of their organization's EBIT attributable to AI use.

Function-Specific Benefits

For financial services firms, AI is delivering measurable returns in specific functions:

Cost reductions are most commonly reported in:

• Software engineering (56% report cost decreases)
• IT (54% report cost decreases)
• Service operations (51% report cost decreases)

Revenue increases are most frequently seen in:

• Marketing and sales (67% report revenue increases)
• Strategy and corporate finance (65% report revenue increases)
• Product or service development (62% report revenue increases)

These findings align with what we see at Vantage Point: financial services firms that strategically deploy AI in client-facing functions and integrate it with their Salesforce Financial Services Cloud or HubSpot platforms are seeing significant improvements in advisor productivity, client engagement, and revenue growth.

What Separates High Performers from the Rest

The Profile of AI High Performers

McKinsey defines "AI high performers" as organizations where respondents attribute EBIT impact of 5% or more to AI use and report seeing "significant" value—representing about 6% of survey respondents. These organizations share several distinguishing characteristics that financial services leaders should emulate.

1. Transformative Ambition

High performers are more than three times as likely as others to say their organization intends to use AI to bring about transformative change to their businesses. Half of these high performers intend to use AI to fundamentally transform their operations, not just incrementally improve them.

Tara Balakrishnan, Associate Partner at McKinsey, observes: "What stands out most about the high performers is their level of ambition. Their AI agendas go beyond driving incremental efficiency gains: High performers are setting out to fundamentally reimagine their businesses."

For wealth management firms, this might mean reimagining the advisor-client relationship through AI-powered insights and automation. For banks, it could involve transforming the entire customer journey from onboarding to ongoing service delivery.

2. Workflow Redesign

High performers are nearly three times as likely as others to fundamentally redesign their workflows in their deployment of AI. This is one of the strongest contributors to achieving meaningful business impact among all factors tested in McKinsey's research.

This finding reinforces a core principle at Vantage Point: successful AI implementation isn't about layering new tools onto existing processes. It requires rethinking how work gets done, what tasks humans should focus on, and where AI can add the most value.

3. Strategic Investment

More than one-third of high performers commit more than 20% of their digital budgets to AI technologies—4.9 times the rate of other organizations. These resources enable them to:

• Scale AI technologies across multiple functions
• Invest in necessary infrastructure and data architecture
• Provide comprehensive training and change management
• Iterate and improve AI solutions over time

4. Leadership Engagement

High performers are three times more likely than their peers to strongly agree that senior leaders demonstrate ownership of and commitment to AI initiatives. This includes actively championing AI across the organization, role modeling its use, providing continued funding, and engaging in regular budget reprioritization.

Alex Singla, Senior Partner at McKinsey, emphasizes: "The companies reporting EBIT impact tend to have progressed further in their scaling journeys. All business leaders are seeking to make their companies more efficient, but the real results emerge when leaders are also able to use technology to innovate."

5. Best Practice Implementation

High performers consistently implement a comprehensive set of management practices across six dimensions:

Strategy:

• Define clear AI vision and strategy aligned with business goals
• Create detailed road maps with specific initiatives and use cases
• Establish processes for determining when model outputs need human validation
• Ensure top leadership understands how AI creates value

Talent:

• Develop strategic workforce plans that incorporate AI-driven changes
• Create effective talent strategies for recruiting and integrating AI expertise
• Provide role-specific learning journeys to build critical AI skills

Operating Model:

• Establish agile product delivery organizations with defined processes
• Enable rapid development cycles characterized by quick decision-making
• Create centralized teams that coordinate AI efforts across the organization

Technology:

• Build infrastructure and architecture that enables latest AI technologies
• Ensure platforms can support core AI initiatives at scale

Data:

• Create reusable, business-specific data products
• Establish iterative processes for building and improving AI solutions
• Implement appropriate guardrails and development approaches

Adoption and Scaling:

• Embed AI solutions effectively into business processes
• Redesign frontline employee workflows and create intuitive user interfaces
• Secure active senior leadership engagement in driving adoption

As Bryce Hall, Associate Partner at McKinsey, notes: "Companies that effectively deliver across six primary elements (strategy, talent, operating model, technology, data, and adoption and scaling) are the ones reporting significant value creation from their AI investments."

6. Broader AI Agent Adoption

In most business functions, AI high performers are at least three times more likely than their peers to report scaling their use of agents. They are also using AI in more business functions overall, with particular emphasis on marketing and sales, strategy and corporate finance, and product development.

The Workforce Impact: Varied Expectations

Current and Expected Changes

As organizations expand AI use, perspectives on workforce impact vary significantly. Looking across business functions where AI is deployed:

• 32% of respondents expect workforce decreases in the coming year
• 43% expect no change in workforce size
• 13% expect workforce increases

These expectations represent a notable shift from observed changes in the past year, where smaller percentages reported actual workforce changes. The functions where respondents most commonly expect workforce reductions include human resources (31% expect decreases), service operations (39% expect decreases), and supply chain management (33% expect decreases).

The Human-in-the-Loop Imperative

One of the most important findings for financial services leaders is the critical role of human judgment in successful AI implementation. Among the top practices that distinguish high performers is having defined processes to determine how and when model outputs need human validation to ensure accuracy.

Bryce Hall emphasizes: "AI is rarely a stand-alone solution. Instead, companies capture value when they effectively enable employees with real-world domain experience to interact with AI solutions at the right points. The combination of AI solutions alongside human judgment and expertise is what creates real 'hybrid intelligence' superpowers."

For financial advisors, relationship managers, and compliance professionals, this means AI should augment their expertise, not replace it. The most effective implementations create seamless workflows where AI handles data analysis, pattern recognition, and routine tasks, while humans focus on relationship building, complex decision-making, and strategic thinking.

Practical Implications for Financial Services Leaders

Based on McKinsey's research and our work with financial services firms, here are actionable steps leaders should take:

1. Set Transformative, Not Just Incremental, Goals

Don't limit your AI strategy to cost reduction or efficiency gains. Challenge your team to identify how AI can:

• Create new revenue streams
• Fundamentally improve client experiences
• Enable advisors and relationship managers to work in entirely new ways
• Differentiate your firm in crowded markets

2. Invest in Workflow Redesign

Before implementing AI tools, map your current workflows and identify opportunities for fundamental redesign. Ask:

• What tasks should AI handle autonomously?
• Where do humans add the most unique value?
• How can we create seamless handoffs between AI and human expertise?
• What processes need to be reimagined entirely, rather than just automated?

3. Commit Adequate Resources

If you're serious about AI transformation, allocate sufficient budget—the high performers are investing 20%+ of their digital budgets in AI. This includes:

• Technology infrastructure and platforms
• Data architecture and integration
• Talent acquisition and development
• Change management and training
• Ongoing optimization and improvement

4. Start Experimenting with AI Agents

Don't wait for perfect clarity on agentic AI. Identify 2-3 use cases where AI agents could handle multi-step workflows:

• Client onboarding processes
• Compliance monitoring and reporting
• Research and due diligence
• Service desk management
• Document analysis and summarization

Start with pilot programs, measure results rigorously, and iterate based on learnings.

5. Ensure Leadership Engagement

AI transformation cannot be delegated entirely to IT or innovation teams. Senior leaders must:

• Actively champion AI initiatives
• Role model the use of AI tools
• Participate in regular reviews and budget prioritization
• Remove organizational barriers to adoption
• Communicate the vision consistently

6. Implement a Comprehensive Practice Framework

Don't cherry-pick individual best practices. Successful AI transformation requires coordinated execution across strategy, talent, operating model, technology, data, and adoption. Consider working with specialized partners who understand both AI technologies and the unique requirements of financial services.

7. Focus on Human-AI Collaboration

Design your AI systems with clear human-in-the-loop protocols. Determine:

• Which decisions require human oversight
• How to present AI insights for human validation
• What training your team needs to work effectively with AI
• How to maintain compliance and risk management standards

The Vantage Point Perspective

At Vantage Point, we've seen firsthand how financial services firms can successfully navigate their AI transformation journeys. Our approach aligns with McKinsey's findings on what distinguishes high performers:

Strategic Planning: We work with leadership teams to develop comprehensive AI strategies that align with business objectives—whether that's enhancing advisor productivity, improving client engagement, or accelerating growth.

Salesforce & HubSpot Expertise: Our implementations go beyond technology deployment to fundamentally redesign workflows, ensuring AI tools are embedded into daily operations rather than bolted onto existing processes.

AI-Driven Solutions: From predictive analytics and personalization to intelligent automation and agentic workflows, we help firms leverage the latest AI capabilities within their CRM ecosystems.

Compliance-First Approach: We understand that financial services firms operate in highly regulated environments. Our solutions incorporate appropriate guardrails, audit trails, and human oversight to maintain compliance while capturing AI's benefits.

Managed Services: Successful AI implementation isn't a one-time project—it requires ongoing optimization, user training, and technical support. Our managed services ensure your AI investments continue delivering value over time.

Looking Ahead: The Path to AI Leadership

The financial services industry stands at a crossroads. AI adoption is widespread, but true transformation remains rare. The gap between those experimenting with AI and those capturing significant value is widening.

The good news: the playbook for success is becoming clearer. Organizations that combine transformative ambition with disciplined execution—redesigning workflows, investing adequately, engaging leadership, and implementing comprehensive best practices—are seeing meaningful returns.

The question for every financial services leader is: Will your firm be among the experimenters, or the transformers?

As you plan your AI strategy for 2025 and beyond, remember that the journey from pilot to enterprise-wide impact requires more than technology. It demands vision, commitment, and the willingness to fundamentally reimagine how your organization creates value.

At Vantage Point, we're committed to helping financial services firms navigate this transformation successfully. If you're ready to move beyond experimentation to capture real value from AI, we'd welcome the conversation.

Source

Full Report Citation: The State of AI in 2025

Singla, Alex, Alexander Sukharevsky, Lareina Yee, and Michael Chui. "The state of AI in 2025: Agents, innovation, and transformation." QuantumBlack, AI by McKinsey, November 2025.

This blog post analyzes key findings from McKinsey's comprehensive global survey of 1,993 participants across industries, with specific applications and recommendations for financial services leaders.

About Vantage Point

Vantage Point is a specialized Salesforce and HubSpot consulting firm dedicated exclusively to financial services. We partner with wealth management firms, banks, insurance companies, and fintech organizations to transform their operations through AI-driven CRM solutions, strategic implementations, and managed services. With over 150 satisfied clients, 400+ completed engagements, and a 95% client retention rate, we bring deep financial services expertise and technological innovation to every engagement.

Learn more at vantagepoint.io or contact us to discuss how we can help your firm capture real value from AI transformation

About the Author

David Cockrum is the founder of Vantage Point and a former COO in the financial services industry. Having navigated complex CRM transformations from both operational and technology perspectives, David brings unique insights into the decision-making, stakeholder management, and execution challenges that financial services firms face during migration.

• • • Email: david@vantagepoint.io
    • Phone: (469) 652-7923
    • Website: vantagepoint.io