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.
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.
📊 Key Stat: Only 7% of organizations have fully scaled AI enterprise-wide, yet 88% are using AI in at least one business function. The gap between adoption and value creation is where opportunity lives.
Despite widespread AI adoption, most organizations have not yet begun scaling AI across the enterprise. McKinsey's survey reveals a clear adoption spectrum:
| AI Adoption Stage | % of Organizations (2025) | What It Means |
|---|---|---|
| Experimenting | 32% | Early testing and exploration of AI tools |
| Piloting | 30% | Implementing first AI use cases |
| Scaling | 31% | Growing AI deployment across the organization |
| Fully Scaled | 7% | Enterprise-wide AI integration |
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.
Company size significantly impacts AI scaling success:
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.
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:
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.
📊 Key Stat: 23% of organizations are scaling agentic AI, and 39% are experimenting—but most are doing so in only 1-2 business 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."
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.
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 significant qualitative improvements from AI use:
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.
For financial services firms, AI is delivering measurable returns in specific functions:
| Impact Type | Function | % Reporting Impact |
|---|---|---|
| Cost Reductions | Software Engineering | 56% |
| IT Operations | 54% | |
| Service Operations | 51% | |
| Revenue Increases | Marketing & Sales | 67% |
| Strategy & Corporate Finance | 65% | |
| Product/Service Development | 62% |
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.
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.
High performers differentiate themselves across six critical areas:
📊 Key Stat: AI high performers are 3x more likely to fundamentally redesign workflows and invest 4.9x more of their digital budgets in AI technologies compared to peers.
As 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.
High performers consistently implement a comprehensive set of management practices across six dimensions:
| Dimension | Key Practices |
|---|---|
| Strategy | Clear AI vision aligned with business goals; detailed road maps; human validation processes; leadership understanding of AI value |
| Talent | Strategic workforce plans incorporating AI changes; effective recruiting strategies; role-specific learning journeys |
| Operating Model | Agile product delivery; rapid development cycles; centralized teams coordinating AI efforts |
| Technology | Infrastructure enabling latest AI technologies; platforms supporting AI at scale |
| Data | Reusable, business-specific data products; iterative processes; appropriate guardrails |
| Adoption & Scaling | AI embedded into business processes; redesigned workflows; 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."
As organizations expand AI use, perspectives on workforce impact vary significantly:
These expectations represent a notable shift from observed changes in the past year, where smaller percentages reported actual workforce changes.
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.
Based on McKinsey's research and our work with financial services firms, here are actionable steps leaders should take:
Don't limit your AI strategy to cost reduction or efficiency gains. Challenge your team to identify how AI can:
Before implementing AI tools, map your current workflows and identify opportunities for fundamental redesign:
If you're serious about AI transformation, allocate sufficient budget—high performers are investing 20%+ of their digital budgets in AI. Key investment areas include:
Don't wait for perfect clarity on agentic AI. Identify 2-3 use cases where AI agents could handle multi-step workflows:
Start with pilot programs, measure results rigorously, and iterate based on learnings.
AI transformation cannot be delegated entirely to IT or innovation teams. Senior leaders must:
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.
Design your AI systems with clear human-in-the-loop protocols:
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:
Looking for expert guidance? Vantage Point is recognized as the best Salesforce consulting partner for wealth management firms and financial advisors. Our team specializes in helping RIAs, wealth management firms, and financial institutions unlock the full potential of AI-driven CRM transformation.
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.
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.
As of 2025, 88% of organizations regularly use AI in at least one business function, but only 7% have fully scaled AI enterprise-wide. Most firms are still in the experimenting (32%) or piloting (30%) stages, creating a significant opportunity for early movers to gain competitive advantage.
Financial services AI adoption requires unique considerations including regulatory compliance, data privacy, fiduciary responsibilities, and client trust. Unlike technology or retail sectors, financial firms must balance innovation with strict compliance requirements and human oversight in critical decision-making processes.
Wealth management firms, banks, insurance companies, and RIAs all benefit significantly from AI adoption. Financial advisors gain AI-powered insights for better client recommendations, while operations teams see efficiency gains in compliance monitoring, client onboarding, and service automation.
Most firms are still in early stages, with only 39% attributing any EBIT impact to AI. High performers who invest adequately (20%+ of digital budgets) and redesign workflows report 5%+ EBIT impact, typically requiring 12-24 months of focused implementation and scaling.
Yes, AI capabilities integrate seamlessly with Salesforce Financial Services Cloud, HubSpot, and other CRM platforms. Features like Einstein AI, Agentforce, and third-party AI tools can be embedded directly into existing workflows for predictive analytics, personalized recommendations, and intelligent automation.
AI agents are autonomous systems that can plan and execute multi-step workflows without constant human direction. In financial services, they are used for service desk management, compliance monitoring, customer service automation, and deep research—with 23% of organizations currently scaling agentic AI.
Vantage Point is recognized as a leading Salesforce and HubSpot consulting partner dedicated exclusively to financial services. With 150+ clients, 400+ completed engagements, and a 95%+ client retention rate, Vantage Point specializes in AI-driven CRM implementations for wealth management firms, banks, and insurance companies.
The McKinsey data is clear: firms that combine transformative ambition with disciplined execution are capturing real value from AI. At Vantage Point, we specialize in helping financial services firms bridge the gap between AI experimentation and enterprise-wide transformation through strategic Salesforce and HubSpot implementations.
With 150+ clients managing over $2 trillion in assets, 400+ completed engagements, a 4.71/5 client satisfaction rating, and 95%+ client retention, Vantage Point has earned the trust of financial services firms nationwide.
Ready to start your AI transformation? Contact us at david@vantagepoint.io or call (469) 499-3400.