In the previous four blogs in this series, I explored how leading financial services firms are scaling AI for value, driving rapid change, using agentic architectures to reinvent work and businesses, and reshaping hybrid workforces. In this final blog, I step back to the bigger picture: how leadership, culture, and the operating model shift when AI is scaled across the enterprise. I also highlight several of the questions boards are now asking.

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AI and leadership

Leadership is a decisive factor in AI outcomes. Our research found that CEO-sponsored gen AI-enabled enterprise reinvention delivers 2.5 times more value than efforts initiated further down the organization. This signals the importance of AI to the strategy and future of the company.

The best CEOs we work with treat AI as part of the business strategy and the broader investment and transformation portfolio. They connect AI to purpose, vision, and performance. They also build a coherent narrative that helps the organization understand what is changing, why it matters, and how to engage. (My colleagues in our Storytellers team support many clients on exactly this.)

Roles and accountability are changing

Most CEOs appoint someone “on point” for AI, often the CIO or CTO. In some firms, the Chief Data Officer has been elevated, or a Chief AI Officer has been added to the C-suite or extended leadership team. These roles bring expertise and accountability, but AI cannot sit with one function. It is the responsibility of the entire C-suite. Each leader must own how AI is used in their business, how risks are managed, and how outcomes are delivered.

The board’s role is becoming more material

The CEO and key leaders need alignment and support from the board. Board relationships and tech experience vary significantly. Our research shows that tech expertise among board members has grown from 6% in 2015 to 16% today. About 20% of banks now have more than a third of board members with technology experience and are more likely to be successful. Yet 17% of bank boards still have no members with tech experience, which makes support and oversight far harder.

In my experience, board sessions are vital. They create space to align on strategy, investment, and risk appetite. They also often produce some of the strongest advocacy for a human-led approach.

 AI and leadership development

AI changes how leaders lead. It shifts the pace of decision-making, the nature of work, and the shape of collaboration. Leaders and leadership teams need new knowledge, skills, and behaviors. The Turing Institute’s AI Skills for Business Competency Framework acknowledges leaders as a key persona.

Fluency, not specialism

Most leaders do not need to become AI specialists. They do need business fluency: an understanding of capabilities and limits, the major AI types, and how AI fits into data, technology, and business architecture. That fluency helps leaders spot opportunities, define problems well, and sponsor initiatives intelligently. It also grows fastest when leaders use AI tools themselves.

Investing and scaling with a reinvention mindset

Leaders must improve how they evaluate and scale AI investments. That includes building a balanced portfolio, prioritizing resources, and funding the change beyond technology. Scaling AI requires investment in process, work design, skills, adoption and governance, not just models and infrastructure.

Risk, resilience and trust at scale

Leaders also need to deepen their risk capability. As discussed in blog 3, they must set risk appetite with the board, assign accountability for AI products and use responsible AI frameworks, tools, and specialist support. This expands leadership responsibilities across legal and regulatory compliance, risk ownership within the three lines of defense and control behaviors in areas such as anti-money laundering and fraud prevention.

FS leaders must also engage proactively with central banks, regulators, and policymakers. We will only build trust at scale through evidence and embedded practice, particularly in high-stakes decisions such as credit, claims and trading. At the same time, leaders must strengthen resilience and security in a world of AI-accelerated threats, embedding AI into cyber defense, operational risk and stress testing.

Change leadership becomes a core capability

This is fast-paced, evolving change across colleagues, customers, markets, and society. Leaders must sharpen their change leadership. They must be advocates who bring the vision to life, build trust and activate adoption. They must collaborate across business and technical teams and across the ecosystem. They must lead with authenticity and inclusion, make hard decisions, and address concerns directly.

AI is also changing constantly. Curiosity and continuous learning are now leadership essentials. Over the last two years I have supported leaders from board and C-suite to frontline managers and their appetite to learn continues to impress me. People arrive with different starting points, but the leaders who progress fastest ask questions, use the tools and translate awareness into practical application.

If you would like to explore how we support leader capability building and real-world AI application, please reach out.

Moving from initial leadership education to the application of AI

Many leadership teams have already had an AI “101” briefing, or a “201” business school visit. That creates baseline literacy, and it matters. We now also have a full AI Academy and TQ modules within our LearnVantage capability to support this ongoing learning.

For most organizations, the leadership challenge has now moved onto applying AI to real business opportunities and problems and leading the change at scale across functions and teams.

Preparing and aligning middle managers and team leaders

AI changes management work. Coordinating, supervising, and controlling work looks different when agentic architectures become part of day-to-day operations. AI becomes more than a tool.

AI is not human and not fully autonomous, but it can sense, generate, remember, plan, and act. In a regulated industry, the ability to oversee performance, conduct, and decision-making across both human workers and AI agents will become increasingly important. Managers may need to supervise orchestration agents within agentic workflows.

Spans of control may expand sharply as “teams” include both humans and agents, moving from 10 to 100. To manage this, leaders will need dashboards and AI-enabled management tools for governance and control. The goal is to spend less time on admin and more time on judgement.

This is why leadership learning must include managers and team leaders, not only executives. These leaders need the same space to build understanding, ask questions, and start using AI. They also carry the day-to-day responsibility of addressing concerns, supporting adoption, and resetting ways of working. Aligning leaders at all levels materially improves transformation outcomes (Accenture Transformation GPS, 2025).

Evolving the culture for an age of intelligence

AI does not change a bank or insurer’s purpose and values. It does change how we work, think, and decide. Digital shifted culture in financial services; AI will shift it further and faster.

The specific cultural themes vary by firm, but common areas include:

• • Psychological safety in teams, and trust in AI.
  • Curiosity and learning new ways of working.
  • Integrating responsible AI compliance with innovation and speed.
  • Radical transparency about where and how AI is used.
  • A reinvention mindset that challenges the status quo.
  • Breaking siloes and building collective intelligence.
  • Protecting human value and human relationships at work.
  • Ensuring agents uphold conduct and consumer standards (for example, Consumer Duty in the UK).
  • Strengthening resilience and addressing systemic AI risks.

What enables that culture change?

First, tone matters. Leaders set the climate for trust and psychological safety, including the safety to speak up when something is wrong. I have been encouraged by leaders who discuss risks openly and treat concerns as legitimate.

Second, teams need empowering opportunities to take the first steps. The organisation should set a coherent view of how work will be done and then give teams space to experiment, learn, and adopt AI well.

Third, leaders must challenge siloes and “the way we’ve always done it.” Financial services has often had a bias to the status quo. Reinvention requires deliberate disruption.

Finally, human behaviour tends to follow the path of least resistance. If we want new behaviours, we must change the operating mechanisms around them. Delivering with AI should be low friction.

Regulatory tone matters as well. A more innovation‑positive stance, such as the UK FCA’s direction, shapes firm policy, compliance expectations and audit priorities.

Over the last five years, we have lasting shift in the importance of human needs at work: wellbeing, purpose, growth, and inclusion. As we apply AI, we must strengthen these elements, not erode them. Financial services is at its best when it delivers meaningful work, strong relationships, trust and belonging.

Leading organizations reinforce these human elements as they scale AI. They use AI to personalize career development and learning pathways, helping employees grow through meaningful work aligned to strengths and goals. At Accenture, leaders ask a bold question: “Are our people better off for working here?” They measure whether employees are building marketable skills, maintaining wellbeing, and feeling purpose and belonging. According to our Talent Reinventors survey, when employees feel “better off,” they are 1.7X more adaptable to change, 1.9X more likely to trust the organization and 35% more likely to drive innovation.

Adapting the operating model

An operating model is the set of capabilities and how they work together to serve customers. As AI and agentic architectures change workflows at the core of banks and insurers, operating models must adapt. Our recent thought leadership focused on the operating model changes required for AI. This highlights the need for more adaptive structures and readiness for continual change (see blog 3).

This shift challenges traditional organisational boundaries. Value streams cut through siloes, and financial services is full of them. We also need to move beyond one-off use cases and AI as standalone models. Agentic AI is becoming a reusable capability integrated with people, process, data, and technology.

Many clients are now asking a bigger question: what does an “AI-first bank” or “AI-first insurer” look like? Several themes sit inside that question. Let’s explore a few of the big questions within this.

Changing customer experiences and value dynamics

Agentic AI can make banking and insurance more proactive, conversational, and relevant. It can turn one-off digital transactions into continuous interactions that remember preferences and needs. It can also create space for firms to be more human when it matters most: in moments of emotion (bereavement, fraud), relationship (private banking), and advice (financial planning).

In the near term, AI interaction may feel daunting for elderly and vulnerable customers. Choice and inclusive design will matter.

Customer choice will also expand. Some customers will use personal agents to direct deposits and payments across providers via open banking. Customer journeys may increasingly be brokered between bots as much as between humans. That will reshape distribution, pricing, and loyalty.

Firms will take different competitive positions. Some will try to own the agent-customer interaction. Others will compete on trust through proprietary, highly compliant agents. Others will build ecosystems and integrate widely.

These choices will reshape economics, work, and workforce across each institution.

Real world example — changing customer interaction with AI

At a large Asian bank, we redeveloped the mobile app using AI in the creative process (including synthetic customer testing) and embedded AI for personalization and automated digital content and messaging. Across millions of touchpoints, the gen AI redesign increased customer satisfaction, drove 50% higher usage, reduced marketing costs by 40%, increased digital sales by 65% and lifted cross-sell to 30%. These are not customer-owned agents yet, but it signals how quickly AI can improve experience and performance.

Growth models leveraging AI network effects

AI will gradually reshape competitive logic. As firms embed agentic architectures across the core value chain, they lay the foundations for AI-first growth models. Over time, these models may create AI network effects, where learning loops improve performance faster than traditional scale advantages.

Fraud and transaction monitoring illustrate the direction. As agents learn from patterns and from each other, they can identify new features and signals. Agentic architectures do not only automate. They can create learning systems that improve continuously and feed collective intelligence. We are early, but the operating model implications are significant.

Composable, resilient and sustainable operating models

Agentic AI points to three operating model requirements:

1. Composability: Financial services capabilities are deeply interconnected and often hard wired into legacy platforms. Composability means designing modular AI components and agents that are reusable across products and geographies. That enables faster reinvention and reduces technical debt.
2. Resilience: Firms must build human-and-agent operating teams that can absorb shocks, maintain integrity and recover quickly. In regulated sectors, continuity and trust are non-negotiable. This includes the ability to run essential services if an agentic layer fails.
3. Sustainability: As AI compute workloads grow, banks and insurers must manage energy, carbon, and water usage, model efficiency, and greener cloud architectures. These choices will influence net-zero goals and regulator, customer and investor sentiment. There is also opportunity to fund the sustainable energy and water solutions data centres require.

Alignment with wider operating model shifts

Agentic AI arrives while many banks and insurers are already simplifying governance, adopting product operating models for continual change and evolving global capability centers (GCCs) into intelligent operations.

Product owners will need to evolve AI capabilities continuously and bring AI skills into delivery squads alongside engineers, customers, proposition and change professionals. GCCs are increasingly combining scaled operations with deep technology, data and AI capability to deliver intelligent operations for enterprise reinvention. Organizations without established capability centers may instead choose to onshore or transform operations in place.

Real world example — AI inside the GCC

With a large complex-lines insurer, we deployed more than 100 RPA, AI, and now agentic AI solutions across value streams and processes within the GCC. This improved service, insight and speed, and delivered a 35% cost reduction. Critical factors included consolidated work visibility, a front‑to‑back ambition, strong talent and sustained investment in both core platforms and AI.

Data and AI within the operating model

Historically, many firms treated data as a technology problem, funded primarily for remediation and regulatory compliance. That view is reversing.

Banks and insurers are increasingly treating data as a strategic business asset. Curating it has value in its own right, and it is also essential for training AI accurately and scaling it responsibly. As a result, we see rising investment in data and AI capabilities and greater emphasis on business and customer domain knowledge for data professionals.

Real world example: NatWest and AWS

We announced a five-year collaboration with AWS and NatWest Group to accelerate modernization of digital, data, analytics, and AI capabilities for its 20 million customers. The collaboration includes investment in colleague skills and a data driven culture. Paul Thwaite, NatWest CEO, said: “Our industry — and the expectations of our customers — are changing rapidly and we are building our capabilities in order to understand and serve their needs better and faster than ever before. Equipped with high quality data, we can continue to quietly revolutionize how we serve our customers through the use of AI and other technologies in order to provide more personalized products and services as a trusted partner in the moments that matter most.”

In closing: A constrained or an unconstrained future

This is a leadership moment. C-suites and boards face a clear choice between a constrained and an unconstrained view of what AI can mean for their organisation.

The constrained view aims to “get 10% out.” It focuses on narrow task automation, removing lower-value work, and optimising the workforce. It may also default to “not yet” or “fast follower.” Given market pressure, uncertainty, and investment fatigue, this response is understandable. But it is ultimately limiting.

The unconstrained vision is different. It is not about incremental savings, but about unlocking 10x performance and growth. In this model, growth is no longer limited by how many people a bank can hire. One person can lead and orchestrate a team of AI coworkers to deliver exponentially greater impact.

This is a deliberate choice to reinvent the business by combining human and AI capability. It assumes continual change, aligned leadership and deep trust, with people leading the transformation. Success depends on empowering employees to reimagine workflows and co‑design intuitive human–AI interactions that elevate work rather than replace it. The central question shifts from “Where can AI cut costs?” to “Where can intelligence transform outcomes?”

The next generation of leadership will not be defined by how much control we retain, but by how responsibly we accelerate capability, both human and artificial. The banks and insurers that thrive will be led by people who combine purpose, curiosity and courage and who elevate intelligence for the benefit of customers, colleagues, shareholders and society.

 Key Takeaways

1. 1. Leadership: How are you helping leaders understand AI, apply it to business opportunities, role model its use, and lead their people through change?
   2. Culture: Culture shapes AI use, and AI use shapes culture. Responsible AI changes risk and control ownership. Are you actively considering AI’s cultural impact?
   3. Operating model: Is your operating model designed for continual change? How will you adapt the three lines of defense for AI risks? Do you treat data and AI as commodities, or as distinctive capabilities?
   4. Mindset: Agentic AI is a leadership choice. Does your leadership team have a reinvention mindset, or a constrained one?

Conclusion

Over the last five blogs we’ve covered a lot of ground:

1. 1. The competitive advantage of scaling AI: How AI and agentic architectures are reshaping financial services.
   2. Taking a human-centered approach to AI: How AI and agentic AI in particular help us reinvent work and reshape the workforce in financial services.
   3. Leading the change for value: Strategies for driving successful AI transformations, as true business change that is scaled, responsible, distributed, rapidly evolving and human-led.
   4. Workforce change and the role of HR: How agentic AI is reshaping the workforce and creating a critical role for the HR function.
   5. Leadership, culture and operating model change: How AI is starting to change how we lead, ways of working and become a fundamental capability across our industry.

AI continues to be one of the most disruptive and exciting changes within financial services and the wider world of work. There is a gap emerging between the leaders who have scaled AI for value and taken a human-led approach.

I hope the practical examples and my reflections have been helpful to you in bringing to life some of the approaches that can make the difference. I’d love to chat further with you about your opportunities and challenges — please reach out to me directly on LinkedIn and read our Banking Top Trends for 2026.

AI impact on workforce change and the role of HR in financial services

17 Mar 2026

In my previous blogs in this series, I’ve looked at how leaders are scaling AI, how we are reinventing work using agentic AI and how to lead this change. In this penultimate blog, I look at how agentic AI is reshaping the workforce and creating a critical role for the HR function. 

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How agentic AI is reshaping financial services work

Across banking and insurance, AI is automating routine tasks, augmenting complex work, but the real shift is how human judgement is redeployed to higher-value decisions. As one insurance client put it, the goal is to “take the robot out of the human”. Agentic AI does exactly that: semi-independent, specialist agents work alongside employees like capable coworkers.

The impact is neither uniform nor inevitable. AI does not affect every task in a role and outcomes are not predetermined. Leaders make deliberate choices about what should remain human work and what should shift to AI. This is less about redesigning roles and more about designing work for outcomes and intent, not job titles.

Sector-by-sector impact

• • Insurance: Underwriting, claims, and servicing see the deepest change. Complex cases are augmented, while high-volume work becomes increasingly automated. Human effort shifts toward judgement, decision-making and relationships with clients and brokers.
  • Banking: Relationship managers and specialist advisors — particularly in wealth, private, corporate, and institutional banking — benefit from deep augmentation. In retail banking, automation concentrates on transactions and back office work, accelerating the move to digital channels and elevating judgement and exception handling.
  • Investment banking and capital markets: AI supports sharper judgement across dealmaking, execution, and risk management. Routine processing, compliance checks, and documentation are automated, freeing bankers to focus on creativity, negotiation and trust building.
  • Asset and wealth management: Research, portfolio management and client services are transforming. AI improves market insight and risk management, while automation absorbs reporting and regulatory administration, allowing more time for meaningful client conversations.

Agentic AI will reshape how value is created across the financial services workforce — not just the workforce itself. The right strategy is therefore highly organization specific.

Considering individuals, teams and networks

Reinventing work means designing for individuals and for the teams and networks that create value.

Most knowledge work in financial services is team based. Yet few teams are adapting effectively. Our Talent Reinventors survey found that only 19% of employees say their team experiments with AI together, and just 17% feel psychologically safe to share new ideas.

Talent Reinventors address this gap by using AI-driven analytics to strengthen team dynamics. Their teams report lower stress, improved wellbeing and faster, higher quality decisions, driven by psychological safety, experimentation, and shared learning.

This matters in financial services, where multidisciplinary teams, cross firm networks, and entire ecosystems such as exchanges, financial centers and industry partnerships define how work gets done.

Research supports this shift. A recent study from Ethan Mollick and his colleagues shares that teams with AI outperform both individuals with AI and teams without it. AI helps teams bridge knowledge and language gaps, generate more diverse ideas, and foster positive emotions. Human creativity still drives novelty, but together, humans and AI outperform either alone.

Learning new skills and ways of working

As our work changes, skills must change with it. Our recent Learning Reinvented research shows that while 84% of executives expect AI agents to work alongside humans within three years, and 80% of workers see AI as an opportunity, only 26% report receiving training on how to collaborate with AI. Progress is real, but too slow.

Skills in decline

• • Manual data entry
  • Summarizing correspondence
  • Drafting routine reports and proposals
  • Basic calculations and modelling
  • Simple customer interactions
  • Basic compliance checks

Many of these trends pre‑date AI, but AI accelerates them.

Skills in demand

• • Building, integrating, testing, monitoring, and explaining AI
  • Working effectively with agents — from basic prompting to advanced collaboration
  • Deep human skills: empathy, communication, judgement, negotiation, leadership, and trust building

Even operational areas such as fraud, payments, claims, KYC and lending are shifting toward smaller, more expert teams focused on complex cases and situational judgement.

This is not a one-time transition. Continuous learning enables workforce mobility and long-term employability. For some, this means upskilling within a role. For others, it means reskilling into entirely new careers.

Leading banks and insurers are already moving toward skills-based workforce models, supported by workforce planning, learning platforms and internal talent marketplaces.

Building enterprise-wide skills

To help organizations respond, we launched LearnVantage, our flexible AI-enabled learning ecosystem for future skills. It includes our AI Academy for boards, executives and employees, ranging from educational sessions to deeper learning including nano-degrees and external certification with Stanford and others.

At S&P Global we trained all 40,000 employees, including 7,500 people leaders and 200 board members and senior leaders on GenAI. Participation reached 100%, MPS was very positive and there was a 4X increase in adoption of AI tools. S&P Global is a clear role model for using learning to drive enterprise-wide change.

I am particularly impressed by some of the national efforts in the Middle East around reskilling — this has included the Emirates Institute of Finance, cloud skills training in the UAE and KSA, and AI upskilling for the Commercial Bank of Dubai. In Japan, we upskilled 36,000 front office employees and 300 leaders at a large insurer, enabling them to work effectively with digital assistants and deliver more personalized customer outcomes.

From learning to co-learning

Co-learning is when people teach technology and simultaneously learn from it and is the focus of this research. Early examples include AI coaches and enterprise copilots. In an agentic architecture, these systems adapt to individuals, improve with feedback and support learning in the flow of work.

Consider a contact center. A human agent leads the call; an AI agent transcribes, suggests compliant responses and summarizes outcomes. When the human agent skips or rates suggestions, the AI learns. After the call, it provides reflective feedback. Capability improves for both human and machine.

This addresses a few challenges faced in FS: a) how to raise capability and performance of both human and agent in the real world and not just in testing; b) how to improve usability, adoption and usage of AI, as great AI products that are not used do not generate value; c) how to genuinely put learning into the flow of work where it can best support employee and customer outcomes and d) to release time for deeper learning. Organizations using this approach are developing skills four times faster and doubling confidence in collaborating with AI.

At a large European insurer, this model reduced employee handled calls by 5–10%, cut average handling time by 10%, and released around 20% capacity, while improving skill levels and time to competence.

Developing AI specialists

Organizations also need specialist talent to build, govern and explain AI. These roles increasingly combine technical depth with business domain expertise.

AI job postings have more than tripled since late 2023, intensifying competition. Banks and insurers must differentiate themselves. For one large bank, we repositioned its employer brand in India, enabling the rapid buildout of a 1,000 person data organization operating in a modern, global model.

Reskilling the specialist workforce

Through LearnVantage we are also addressing deeper specialist reskilling for data and AI professionals, particularly following our acquisition of Udacity, Ascendient and AIdemy.

Examples include:

• • At a tier one investment bank, we built a comprehensive learning curriculum and real world simulation to strengthen data management capability across 15,000 operations professionals.
  • At a large national bank, we launched a data immersion program for 5,000 leaders, followed by a full data education pathway for more than 23,000 professionals.
  • At a global tier one investment bank, we retrained an entire 2,500 person data workforce on data policy, standards, and management.
  • At another large national bank, we reskilled more than 1,300 data, cloud and AI engineers to advanced and expert levels, achieving a 98% increase in skills proficiency and measurable gains in delivery and business value.

These efforts go beyond foundational training, where many reskilling programs stop. We consistently build advanced and expert capability through structured digital pathways, real projects, mentoring, nano‑degrees, and collaborative learning.

AI and performance

As AI quality increases, there is a risk that workers form an over-reliance on AI and effectively ‘fall asleep at the wheel’. If we have a human in the lead, we want them to be effective and active in this role. In a study of recruiters, Dell’Aqua found recruiters who over-relied on AI made worse selection decisions and missed brilliant candidates. The increasing performance of the AI was substituting, rather than elevating their human performance.

We are creatures of habit and seek to minimize cognitive load (aka the ‘path of least resistance’). We need a reason to pay attention, check AI results well, maintaining a healthy skepticism and not over-trusting. We need to use AI outputs as a draft and combine them with our own human ingenuity. Being a good human in the lead requires cognitive load.

If we do not understand the fundamentals of the work or how a decision should be made, we cannot properly review AI outputs, spot errors, or apply judgement when an override is needed. As AI speeds up work and improves its results, there should be more time for good decision making. The risk is that we instead rush decisions and over trust the machine. We have found ways to counter this: targeted training, structured decision prompts, breaking complex judgements into logical steps, requiring AI explanations and citations, anomaly detection, and ethical monitoring of both AI and human performance.

Once AI is scaled in the workplace, we need to embed AI into workbenches and tools in a way that promotes good use. We need to train employees how to use AI well and on the human skills we want them to apply (e.g. judgement). We need to provide incentives and disincentives to promote the right behaviors and create healthy patterns of self-reflection (e.g. team retrospectives, individual reflection, peer review etc.).

Maximizing AI return on investment is not about maximizing AI performance in isolation but improving human and agent performance together. This means raising human skills as well. For instance, my personal experience with my creative teams is that AI in the hands of skilled workers actively augmenting and enriching their work gets more creative diversity and faster results, rather than blunting their craft.

Amplifying human intelligence

How AI is trained to interact with human workers really matters to learning as well. A recent study from Wharton deployed GPT-based tutors to a thousand students — access to this AI significantly improved student performance — but when the AI was removed performance dropped. The AI had become a crutch. Where a tutor-based AI had been deployed, this performance drop was largely mitigated. We need to design and train agents to support employee skills development, like a good teammate.

This is particularly important in entry level and early years roles. Base skills, situational judgement and foundational domain knowledge have traditionally been developed through on-the-job learning. As these entry level roles are reduced and we have less basic work to ‘learn the ropes’ on, we need to find different ways to help the next generation of talent learn.

I love my colleague Karalee Close’s view on this — we need to use this moment to amplify human intelligence, not just augment or artificially replicate it. One area of continued interest for me is collective intelligence and how AI can power up the development of shared knowledge, create active dialogue, can lower language barriers and help us connect and collaborate with colleagues and content around the world.

Managing the hybrid workforce

Across my previous blogs in this series, we’ve seen agentic AI working alongside humans. For many people (me included), AI is already a co‑worker. Fast forward three years and a bank or insurer may operate with a workforce of a million, human and agent combined.

How do we manage this new hybrid workforce? Some thoughts regarding the agents:

• • Many organizations and individuals are giving their AI and AI agents names.
  • We already talk about ‘onboarding’ a new agent or set of agents.
  • Agents are not paid, but their training, operation and governance carry real cost.
  • Some organizations already include agent capacity in workforce plans. For now, agents do not hold formal positions or lines of delegation in financial services, but that may change.
  • We are seeing early use of ‘agent descriptions,’ similar to job descriptions.
  • Accountability for agent performance needs to be clear. We are just starting to see management of the agentic workforce (e.g. ‘Agent Ops’) coming together with wider workforce management. For example, a large financial services firm has made its agents ‘digital employees’ with logins, email access, systems access and human managers.

We also need to adapt how we support colleagues:

• • Attracting and selecting more Al ready talent, especially at early career levels.
  • Supporting teams as new agents are introduced.
  • Using agents to enable co-learning and improve human performance.
  • Addressing performance disparities between colleagues with and without AI access.
  • Creating the right incentives for effective AI use.
  • Managing non‑use or misuse of AI.
  • Deliberately designing human connection into work, and monitoring isolation or lone worker risks.
  • Clarifying whether agents trained by an employee move with them when they change roles.
  • Using digital twins to support knowledge transfer when people leave or change roles.

This discussion about the hybrid workforce is just emerging and needs careful exploration. While AI has human-like capabilities and it can be comforting to give it names, we should not anthropomorphise AI and make it equal to human workers. We will need better language. We to value the unique intrinsic worth of humans and maintain a clear difference to agentic workers.

Voice and measurement matter

An effective AI workforce strategy must combine employee voice with measurement. Organizations that actively listen to employees and reflect that input in AI decisions see higher adoption, stronger trust, and more sustainable performance (SHRM, 2025).

Employee voice can take many forms: employee representation on AI ethics boards, consultation with unions and ERGs, participatory design, structured feedback during pilots, ongoing feedback in use, and strong channels for raising concerns and whistleblowing. Financial services organizations have made progress over the past decade in listening and responding to concerns; this now extends to concerns about agent accuracy and performance.

Measurement makes progress visible. Tracking AI adoption, employee sentiment, skills growth, and digital fluency help leaders and HR course correct and sustain momentum.

The role of HR in reinventing work

The Chief People Officer or CHRO has a key role as a change leader, helping the whole leadership team lead the people change well, navigating how to manage the new hybrid workforce and supporting their own HR function in embracing AI responsibly.

HR teams must support leader education, workforce wide reskilling, and the acquisition and development of specialist data and AI talent. Business partners also need deeper understanding of how work and skills are changing within their functions. This requires breaking down silos.

Nearly all Talent Reinventors (96%) align HR, IT, and business leaders around a single talent and technology strategy, compared with just 16% of other organizations. Similarly, 93% have redefined their talent strategy to support AI adoption, positioning HR as a copilot of change rather than a reactive function.

To deliver this shift, talent teams must be able to grow, redeploy, and reshape hybrid workforces quickly. Reward teams need new economic models that reflect a hybrid workforce. At the same time, AI creates significant opportunity within HR, improving outcomes, experience, and service effectiveness.

For HR professionals, this is an exciting moment. Their role has rarely been more critical.

Real world examples: AI in HR

Our Responsible AI Programme for HR, launched in 2016, was designed to accelerate, govern and monitor AI use across HR. We now apply AI across the full talent lifecycle — recruitment, onboarding, development, mobility, reward and colleague support.

The impact has been material: around 45% productivity gains within HR, a 30% improvement in proficiency for priority skills, a 40% increase in internal fill rates and a 35% reduction in time to fill. These outcomes empower colleagues. For example, we use AI to infer skills from work and learning experience, surface them with employees and help them identify future roles, career paths, and learning options.

Many financial services clients are also moving from reactive hiring to proactive talent strategies, using platforms such as Eightfold.ai and Beamery to support sourcing, role matching, pipeline building and internal mobility.

Yet our Talent Reinventors survey found only around 7% of organizations use AI to drive an “internal first” mobility strategy. Most still rely heavily on external hiring or isolated internal moves due to poor skills visibility. Talent Reinventors take a different path. They are 4.4X more likely to have an adaptable workforce and 7.2X more likely to fill roles internally.

A large U.S. bank provides a strong example. By adopting a skills driven model and embedding AI enabled skills visibility into workforce planning, leaders can anticipate gaps and redeploy talent quickly, building a more agile and resilient organization.

At another large banking client, we equipped line managers with AI tools to draft performance summaries, deliver better feedback and support compensation decisions, saving time, improving quality and creating space for stronger judgement.

Take away points

Some key points you may want to reflect on — let me know your thoughts and ideas:

1. 1. As work is reinvented, roles, teams, and ways of working must change too. Are you designing for today’s jobs or are you shaping roles and teams for the future?
   2. Continuous learning is essential. Human skills such as relationships, communication, judgement and creativity must be deliberately designed into new roles. Are you reskilling at scale?
   3. Managing a workforce of people supported by AI agents requires new approaches and elevates HR’s role. How are you thinking about the hybrid workforce? How ready is your HR team?

Conclusion

In this penultimate blog, we explored how financial services firms must reshape their workforce. Agentic AI requires us to think differently about humans and agents working together. Banks and insurers are likely to employ smaller but far more digitally and human skilled workforces, driving sustained demand for reskilling. HR must be central to this transition, helping leaders navigate change and manage the hybrid workforce.

In the final blog in this series, I will explore how leadership, culture and operating models must also evolve to sustain this transformation.