RESEARCH REPORT
Work that matters
Creating lasting business value, building human capability and opportunity, and deepening an organization’s ability to learn and adapt.
Read the report
AUTHORS
Karalee Close
Senior Managing Director - Talent Reinvention, Global
Gaston Carrion
Managing Director - Talent Reinvention, HR Reinvention
Mamta Kapur
Senior Principal - Accenture Research, Talent Reinvention, Europe
Everyone can see that AI is changing work. What hasn’t been settled is what people and organizations do about it.
Suddenly, it’s not what AI can do but how it’s used to create lasting value. How is work being redesigned so people and AI do what each does best? How is newly created capacity being funneled back into the business? How are jobs and careers evolving, how are organizations building judgment and expertise? This is the work that matters moving forward—work that creates lasting business value, builds human capability and opportunity, while deepening an organization’s ability to learn and adapt.
This report explores five trends that define that future.
Five trends shaping the future of work
- 01. New economics As AI takes on routine tasks, organizations must rebalance work in a way that’s meaningful for people and allows freed-up capacity to be reinvested back into the business.
01. New economics
As AI takes on routine tasks, organizations must rebalance work in a way that’s meaningful for people and allows freed-up capacity to be reinvested back into the business.
- 02. Work intelligence
02. Work intelligence
- 03. Employee experience
03. Employee experience
- 04. Decision advantage
04. Decision advantage
- 05. Leadership wisdom
05. Leadership wisdom
AI changes what work costs. Leaders decide what it’s worth.
The economics of work are being rewritten. How organizations allocate work across people and AI, and reinvest the capacity released, will determine whether they create lasting value and drive growth. That means continuously rebalancing people and AI around the outcomes that matter most.
What’s changing
- Work allocation is now an economic imperative. AI introduces new costs that shift with every model, task and token, and most organizations are still developing the visibility they need to understand what it truly costs to deliver outcomes.
- Human-led work matters more than ever. As AI absorbs routine work, human judgment, creativity and expertise are becoming more valuable than ever. The organizations pulling ahead are the ones that build those capabilities while keeping work meaningful and sustainable.
- Reinvesting released capacity is a shared responsibility. It’s a collective decision for the C-suite. All parts of the organization need visibility into where capacity is being created, what it costs and where it can create the greatest value.
Only 35%
of organizations can precisely calculate the cost per business outcome for their highest-volume AI use case.
78%
projected rise in AI token consumption over the next 24 months.
60%
of executives plan to reinvest the capacity AI frees up toward higher-value work.
What to do
Start with outcomes, not with automation.
Treat released capacity as an enterprise asset.
Start small, then scale what works.
Rebalance people, AI and operating choices as priorities evolve.
Start by understanding the tasks, decisions and workflows that drive value.
The skills, expertise and career paths that organizations built their workforces around are changing. Organizations need to understand that change at task, decision and workflow level to shape their talent strategy around the future of work.
What’s changing
- Work is changing, and so are the skills needed. AI is driving significant changes to roles across all career levels, and the expertise gap between entry- and mid-career jobs is widening, making it harder to build the talent pipeline the business needs.
- Build the foundations of expertise. As AI absorbs the routine tasks that once built judgment and expertise, one in five managers are already concerned that junior employees are missing the hands-on experience they need to develop and advance.
- Work is becoming more cognitively intense. Working alongside AI is raising cognitive demands for most occupations, and that burden is falling unevenly across the workforce.
- Long-held assumptions about who does what are changing. Work that once built expertise at entry level is disappearing. AI is redrawing the boundaries of who does what and changing the assumptions that shaped career progression for decades.
62%
of leaders expect AI to transform their business model within three years.
59%
of changes to roles across all career levels are being driven by AI, making work intelligence a prerequisite for any talent strategy.
15%
of executives are redesigning their end-to-end processes with AI at the core.
What to do
Start with the work, not the org chart.
Build genuine work intelligence.
Protect the expertise pipeline.
Connect learning to real work.
Once an HR metric, employee experience is now a C-suite priority.
AI is changing how people experience work: their workloads, their relationships and their sense of purpose. How organizations respond to that shift is becoming one of the most consequential drivers of performance.
What’s changing
- People need to see a future for themselves. AI is reshaping roles and career paths. Organizations that help their employees understand where they fit, how they can contribute and what opportunities lie ahead create better conditions for stronger performance.
- Trust is built through experience. When people feel heard, are involved in shaping what comes next and know their leaders mean what they say, trust follows and performance improves.
- There is no single employee experience of AI. People experience AI differently depending on their role, workload and how much their day-to-day work is changing. One size does not fit all—understanding individual needs is key to sustainable outcomes.
- Learning and connection must be intentional. AI creates space for collaboration but can quietly erode the everyday interactions where people learn from colleagues and build relationships. Organizations must deliberately preserve human connection within the experience of work.
40%
of executives rank employee experience and trust among the shifts most likely to affect organizational performance in the next three years.
42%
of employees say clear guidance on how their role is evolving would be one of the best ways to help them adapt to change.
Only 20%
of employees strongly agree they are active co-creators in shaping how AI changes their work.
What to do
Start with what people want from work.
Make work, skills and experience visible.
Design for connection, purpose and trust.
Measure experience by its impact on value.
As AI becomes widely available, advantage shifts to operating models and decision-making.
Organizations that redesign their operating model to learn from each decision create an advantage no competitor can replicate.
What’s changing
- Faster doesn’t mean better. The competitive edge is moving from decision speed to decision quality, toward the choices that set direction, allocate resources more effectively and resolve trade-offs in ways that create lasting value.
- Every decision makes the next one better. The context, judgment and outcome behind every decision an organization makes is a learning asset that compounds over time, and unlike technology, it cannot be purchased or replicated by a competitor.
- Governance is a performance capability. When decision rights and boundaries are clear, people and AI can move faster with greater confidence, making governance not a constraint on performance but the condition that enables it.
- Human accountability is now even more important. As AI takes on more tasks, the work that remains demands more: greater judgment, deeper accountability and the wisdom to look beyond AI’s recommendations. Being in the loop is not enough.
52%
of employees expect change in AI-supported decision-making in the coming year, signaling that the shift in how decisions are made is already underway.
68%
of C-suite leaders say agentic AI has already exceeded expectations on decision speed.
Only 39%
report that human accountability is clearly established.
What to do
Organize decision-making around shared outcomes, not functions.
Map critical decisions before automating the work around them.
Build governance that creates confidence to act.
Close the learning loop.
All leaders have information. The best ones have judgment, confidence and the ability to inspire.
What sets leaders apart is not access to information but the judgment to act on it — combining context and experience, moving forward when the path is unclear and inspiring people to follow.
What’s changing
- The leadership playbook is being rewritten. Leaders traditionally stood out for their expertise and knowledge. As AI makes both widely accessible, leadership is shifting from what they know to how they act, decide and bring people with them.
- Wisdom in action is becoming the new leadership standard. Leadership is no longer just about making decisions and setting direction. It’s about modeling the choices and behaviors an organization needs, taking responsibility for what follows and inspiring people to commit to the work.
- Four attributes define leaders who thrive amid uncertainty. Curiosity, courage, connection and the ability to balance competing demands are the qualities that allow leaders to act with conviction, bring people along and build the trust that sustains performance through change.
C-suite postings requiring five or more leadership behaviors nearly doubled, yet emphasis on human connection, one of the most critical, is declining.
39%
of C-suite leaders report that human accountability is clearly established as agentic AI takes on more autonomous work.
Only 45%
of organizations have monitoring capabilities robust enough to question AI outputs, understand where it fails and decide when to trust or override it.
What to do
Build leadership wisdom, not just expertise.
Design consequential experiences to stretch each leader’s range.
Keep accountability and ethical judgment with humans.
Build wisdom as a collective capability.
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Salesforce
AI agents handled 2.6 million customer conversations, enabling Salesforce to reskill and redeploy hundreds of support engineers into fast-growing roles. In sales, lead-nurturing agents generated more than $100 million in pipeline.
Accenture’s Talent Navigator
Breaking roles into tasks shows leaders where work stays people-led and where it can shift to AI or automation. Accenture’s experience with clients points toward 20-40% of capacity potentially being freed up. The clearer view gives leaders a shared fact base to redesign jobs and structures.
UPS
HR turned legacy processes into an omnichannel experience for 500,000+ employees. Satisfaction with HR rose from 40% to 98%; driver onboarding feel from eight weeks to eleven days.
Mastercard
AI helps secure more than 159 billion transactions a year, sharpening every subsequent judgement. Detection of compromised cards doubled; identification of compromised merchants rose 300%.
A global bank
A new CEO wanted to build a new culture for their organization. The work started with the senior leadership team. Coaching, monthly forums and quarterly team sessions were the catalyst for a common language and an enterprise mindset.
Contributing Experts
Andy Young
Senior Managing Director - Talent Reinvention, Banking, Capital Markets & Insurance
Stephen Wroblewski
Managing Director - Talent Reinvention, Talent & Workforce, Global
Kent McMillan
Managing Director - Talent Reinvention, Enterprise Operating Model, Global
Susie Kelleher
Managing Director - Talent Reinvention, Leadership Acceleration, Global
