Capgemini: The Multi Year AI Advantage
Source: Capgemini Research Institute, The multi-year AI advantage: Building the enterprise of tomorrow, 2026
Key takeaways
AI is moving from experimentation towards a long term operating capability. More than half of organisations view AI as something that will reshape the enterprise over several years rather than a collection of short term pilots.
AI investment is becoming more selective, not necessarily smaller. Organisations are beginning to pause lower value projects and concentrate resources on areas where business impact can be demonstrated and scaled.
2026 is becoming an infrastructure and readiness year. Investment is moving towards data foundations, compute, governance, safety, risk management and workforce capability rather than model experimentation alone.
Enterprise AI value is expected to extend well beyond productivity. Operational efficiency remains important, but organisations also expect AI to contribute to revenue growth, market expansion, risk management, knowledge management and customer experience.
Generative AI is crossing from pilots into scaled use. Capgemini finds that 38% of organisations have already scaled Gen AI use cases, while 61% expect Gen AI to create a major impact during 2026.
Agentic AI is moving into enterprise experimentation and deployment. Globally, 36% of surveyed organisations are piloting or using agentic AI to some degree, with adoption varying materially by region.
Enterprise adoption is becoming a discipline in its own right. Scaling AI requires more than expanding pilots. Executive sponsorship, data foundations, governance, regulatory clarity, workforce skills and external partnerships all need to move together.
The operating model matters as much as the technology. Capgemini distinguishes between augmenting existing workflows with AI and rearchitecting workflows so that agentic systems perform more of the work under defined guardrails, oversight and auditability.
Human AI chemistry becomes a core organisational capability. 66% of organisations report measurable improvements in productivity and decision quality from human AI collaboration, but only 48% say roles and responsibilities between humans and AI are clearly defined.
Keeping humans in the loop is no longer enough. Employees increasingly need the authority and capability to supervise, challenge, direct and intervene in AI enabled workflows while remaining accountable for outcomes.
Human AI transformation cannot be solved through training alone. Roles, decision rights, workflows, accountability and management systems also need to change.
AI value increasingly depends on balancing two horizons. Organisations need to improve current operations while simultaneously building the capabilities and reusable assets required for future business reinvention.
Executive summary
Capgemini Research Institute’s The multi-year AI advantage argues that enterprise AI is moving beyond an experimental technology cycle and becoming a long term strategic capability. The core question for leadership is increasingly not whether AI will be used across the organisation, but how it will be embedded into operations, decision making, governance and work itself.
The report is based on a global survey of 1,505 senior executives from large organisations that have already deployed AI at limited or full scale. Capgemini therefore examines the next stage of the problem: moving beyond individual pilots towards repeatable enterprise adoption and sustained value.
Investment remains strong despite uncertainty around AI markets and valuations. Organisations expect AI budgets to increase and say that failing to scale as rapidly as competitors could create strategic disadvantage. At the same time, investment is becoming more disciplined. Lower value experiments are increasingly being paused while resources move towards proven use cases, data foundations, infrastructure, governance and workforce readiness.
AI is also spreading across the enterprise. Sales and marketing and IT operations are major investment areas, but adoption extends into customer service, supply chain, manufacturing, R&D and other business processes. Generative AI has progressed beyond the pilot phase in a significant share of organisations, while agentic AI, physical AI and edge AI are also moving through experimentation and deployment.
The report then turns to the harder problem of enterprise adoption. Capgemini argues that this is becoming a discipline of its own. AI can be inserted into existing workflows to augment work, or organisations can go further and rearchitect workflows around automation and agentic systems. The second path requires clearer decision rights, escalation mechanisms, accountability, governance and human oversight.
This leads into one of the report’s central ideas: human AI chemistry. As AI takes a greater role in execution and decision support, employees need more than basic AI literacy. They need to understand where AI can act independently, when human judgement is required, when outputs should be challenged and who ultimately owns the outcome.
Capgemini therefore argues that keeping humans simply “in the loop” is becoming insufficient. Organisations increasingly need people who can supervise, challenge and orchestrate intelligent systems. The shift is from task execution towards judgement, oversight and orchestration.
The report concludes with a model for the AI augmented enterprise built around four priorities: strengthen the technical and data foundations of AI, advance readiness through governance and strategic alignment, build human AI chemistry as an organisational capability, and balance near term transformation with longer term business reinvention.
Key stats and quotable claims
53% of organisations view AI as a long term capability with a five year or longer investment horizon.
More than half believe AI will have a long term transformational impact despite short term uncertainty.
Average AI spending is expected to reach approximately 5% of annual business budgets in 2026, up from around 3% in 2025.
68% are making tactical AI investments focused on returns over the next two to three years.
66% believe failing to scale AI as quickly as competitors could mean lost strategic opportunities or competitive advantage.
64% have started pausing lower value AI projects to redirect resources towards higher impact areas.
63% expect to rationalise or consolidate AI initiatives over the coming year.
If an AI winter occurs, 80% say they would shift attention towards foundational capabilities such as data and infrastructure.
72% would prioritise high impact, lower cost AI initiatives.
Organisations expect their fastest technical investment growth to be in data foundations and pipelines, at 72%.
On the organisational side, 73% expect rapid growth in investment in governance, safety and risk management.
69% expect rapid growth in workforce upskilling and reskilling.
85% rank operational efficiency and cost reduction among the leading areas of expected AI value.
84% identify revenue growth and market expansion.
81% identify risk management and compliance.
77% identify data intelligence and knowledge management.
71% identify customer experience and personalisation.
63% identify sales and marketing as a leading AI investment priority globally.
55% identify IT operations.
38% of organisations have already scaled Gen AI use cases.
61% expect Gen AI to create a major impact for their organisation during 2026.
Globally, 36% of organisations are piloting or using agentic AI in limited or more extensive forms.
67% identify stronger executive sponsorship and vision as an important accelerator of enterprise AI adoption.
59% identify access to external partnerships.
53% identify workforce upskilling and reskilling.
53% identify stronger governance frameworks.
53% identify regulatory clarity and ethical frameworks.
51% identify scalable data infrastructure and governance.
54% prioritise data sovereignty, including retaining control over sensitive or regulated data while using external AI systems.
66% say human AI collaboration has produced measurable improvements in productivity and decision quality.
59% say employees feel empowered to use AI in their day to day work.
Only 48% say roles and responsibilities between people and AI systems have been clearly defined.
Overall summary
The report starts from a relatively simple observation: AI investment is becoming harder to reverse. Even if enthusiasm around the technology cools, organisations increasingly see data, infrastructure, governance and AI capability as foundational investments rather than temporary experiments.
This does not mean organisations will continue funding every AI initiative. The opposite appears to be happening. AI portfolios are becoming more selective. Lower value projects are being paused, initiatives are being consolidated and resources are being directed towards domains where value can be demonstrated.
That shift changes the nature of the enterprise AI challenge. The early question was whether organisations could experiment with models and build pilots. The emerging question is whether those capabilities can be incorporated reliably into core workflows.
Capgemini describes enterprise AI adoption as a new discipline because scaling requires several capabilities to work together. Technology needs appropriate data and infrastructure. Governance needs to evolve with autonomy. Leaders need to decide where AI should augment existing work and where the work itself should be redesigned. Employees need new skills and clearer decision rights. Measures of success need to expand beyond simple productivity or pilot counts towards business outcomes.
The human side becomes particularly important as autonomy grows. Capgemini’s concept of human AI chemistry is not simply about whether employees are comfortable using AI tools. It describes the design of work in which humans and intelligent systems have clear roles, people know when to trust or challenge AI, and accountability remains visible.
That is why the report’s 48% figure is important. Two thirds of organisations report productivity and decision quality gains from human AI collaboration, but fewer than half say roles between humans and AI are clearly defined. Technology adoption is therefore moving faster than operating model clarity.
The longer term opportunity is what Capgemini calls the AI augmented enterprise. The technology becomes embedded into work rather than sitting beside it, but the organisation also becomes more deliberate about governance, decision authority, skills and accountability. The result is not simply more AI. It is a different operating model for combining human judgement with machine capability.
Deep dive
AI is becoming a long term strategic imperative
Leaders across industries increasingly view AI as a long-term strategic imperative rather than a discretionary or experimental technology. The survey shows that, despite short-term uncertainty, confidence remains high in AI’s ability to deliver transformative impact. AI has crossed a critical threshold: for CEOs and CXOs, the question is no longer whether to pursue AI, but how to embed it into the fabric of the enterprise.
As a result, organisations are accelerating investments to build AI as a long-term operating capability, with more than half of organisations committing to sustained, multi-year investment horizons. This approach reflects the belief that consistent, long-term investment in AI builds cumulative benefits such as deeper capabilities, stronger data foundations and accelerated innovation that late adopters will struggle to replicate.
Organisations are also balancing long-term vision with near-term priorities. Leaders are pursuing tactical initiatives that deliver quick returns while consolidating programmes around high-value areas. Even in the event of an AI winter, most expect continued investment in core capabilities and AI-driven transformation, with organisations shifting focus towards proven, scalable areas rather than experimental projects.
AI budgets are also set to rise. On average, organisations globally expect to allocate 5% of their annual business budget to AI initiatives in 2026, up from 3% in 2025. At the same time, leaders recognise the stakes: failing to scale AI as quickly as competitors could mean missed opportunities and lost strategic advantage.
Over the next 12 months, organisations aim to accelerate investments in infrastructure, data, governance and workforce upskilling, laying a stronger foundation for sustainable AI adoption and impact. AI is therefore emerging not only as a productivity tool, but also as a potential engine for enterprise growth.
Investment is becoming more selective
The persistence of AI investment does not mean every experiment will continue indefinitely. Capgemini finds that organisations increasingly want spending directed towards proven, scalable use cases rather than experimentation for its own sake.
Sixty four percent of organisations say they have already started pausing lower-value AI projects to redirect their efforts toward high-impact areas. Sixty three percent expect to rationalize or consolidate their AI initiatives over the next year to concentrate resources where value is demonstrably higher.
The emerging model is therefore one of portfolio discipline. Enterprises continue to invest, but increasingly differentiate between experimentation that develops capability and experimentation that has no credible route towards business value.
If optimism around AI falls, organisations still expect to protect the foundations. Eighty percent say they would shift focus towards foundational capabilities such as data and infrastructure, while 72% would prioritise high impact, lower cost AI initiatives. Sixty four percent would reassess the AI portfolio for cost effectiveness and strategic alignment, while 57% would preserve core talent and knowledge for future resurgence.
The implication is that AI infrastructure, skills and governance increasingly resemble durable organisational capabilities rather than discretionary technology spending.
2026 is becoming the year of infrastructure scale up
In 2026, organisations intend to allocate AI resources towards building robust data and technical capabilities alongside organisational and cultural readiness.
On the technical side, 72% expect the fastest investment growth in data foundations and pipelines, 64% in compute and infrastructure and 50% in model development or licensing. On the organisational side, 73% expect strong growth in governance, safety and risk management, 69% in workforce upskilling and reskilling and 56% in AI centres of excellence.
This combination matters because enterprise scale requires more than increasingly capable models. Data needs to be usable and governed. Computing infrastructure needs to support larger and more continuous workloads. Employees need capabilities that allow AI to be incorporated into work rather than treated as a separate technology.
Capgemini therefore positions technical infrastructure and organisational readiness as parallel investments. Scaling one without the other can create a mismatch between what the technology is capable of doing and what the organisation is capable of absorbing.
AI value is expected to extend beyond productivity
The report also challenges a narrow productivity framing.
Operational efficiency and cost reduction remain the most commonly identified source of expected value, selected by 85% of organisations. But 84% identify revenue growth and market expansion, 81% risk management and compliance, 77% data intelligence and knowledge management and 71% customer experience and personalisation.
AI is therefore expected to influence both sides of the enterprise value equation. It can reduce the cost of existing activity, but organisations also expect it to improve decisions, deepen customer relationships, accelerate growth and strengthen risk management.
Capgemini argues that the pace of investment has so far outstripped the speed at which organisations have deployed AI and extracted value from it. Business leaders increasingly understand that when experimentation fails to deliver expected outcomes, the issue may lie less in the technology itself than in the business approach, methodology and operating model surrounding it.
Full scale deployments will take time, and long term value will not lie in isolated AI use cases but in enterprise wide implementations. The emerging AI ecosystem therefore becomes more rooted in operational value and enterprise architecture, beginning with data foundations and infrastructure and increasingly focusing on human AI chemistry.
AI is now spreading across the organisation
Organisations globally are prioritising AI investments in functions where processes are well defined and outcomes can be measured.
Sales and marketing is currently the largest global investment hotspot at 63%, followed by IT operations at 55%. Across regions, the specific second priority varies, with supply chain featuring more prominently in markets such as the UK and APAC.
The logic is relatively straightforward. The fastest early wins tend to appear where processes have clear inputs, repeatable steps and measurable outcomes. Customer service, sales, IT operations, supply chain and manufacturing therefore become natural environments for scaling early AI deployments.
AI has progressed beyond experimentation to become a core enabler across organisations, cutting across horizontal functions, vertical domains and critical processes such as IT, KYC, underwriting and customer onboarding.
This breadth is important because enterprise transformation begins when AI stops being confined to individual teams and begins influencing how work moves across organisational boundaries.
Generative AI is crossing from pilots into scaled use
Generative AI has crossed the threshold from pilots to scaled use. Capgemini finds that 38% of organisations have already scaled their Gen AI use cases.
Three in five organisations expect Gen AI to create a major impact for their organisation in 2026. Capgemini therefore treats Gen AI less as an emerging experiment and increasingly as part of the mainstream enterprise technology environment.
But the report also cautions that generative systems introduce a different operating reality. Unlike deterministic software, these systems operate with greater uncertainty and emergence. Leaders therefore need to reset expectations around control, timelines and outcomes and create new ways of working around probabilistic technology.
Scaling Gen AI is not simply an infrastructure challenge. The organisation also needs mechanisms for managing resistance, evaluating outputs, establishing accountability and understanding when human intervention is required.
Agentic AI is gaining traction
Agentic AI systems span a spectrum, from human in the loop decision support to more advanced agents with limited, task specific autonomy operating under defined governance guardrails.
At a global level, 36% of organisations are piloting or using agentic AI in limited or more extensive ways. Adoption differs geographically, with China ahead of the US and Europe in the survey.
Capgemini’s broader argument is that the transformative potential of agentic systems comes from changing the relationship between the user and the technology. Rather than prescribing every individual step of a solution, employees increasingly articulate the objective while intelligent systems reason, plan and execute parts of the process.
This makes agent orchestration increasingly important. The organisation needs to design, govern and coordinate multiple AI agents as a coherent system and ensure that their actions remain aligned with business intent.
The challenge therefore moves beyond deploying individual agents. Organisations need to decide how agents interact with each other, where humans intervene, which decisions remain human owned and how exceptions are managed.
Organisations are moving beyond a simple build versus buy decision
Capgemini describes the emerging approach as building where AI differentiates, buying where it accelerates and orchestrating proprietary value over commodity models.
Off the shelf technology can accelerate deployment when capabilities are broadly standardised. Internal development becomes more relevant where use cases require unique organisational context, sensitive information or proprietary differentiation.
The resulting AI estate is therefore likely to be hybrid. Organisations will combine external models, internal applications, open source technology, proprietary data and enterprise specific orchestration.
This creates another governance challenge. Organisations need enough flexibility to adopt improving technology without losing control of sensitive information, costs, performance or accountability.
Enterprise wide AI adoption is becoming a new discipline
As organisations move from pilot stage to scaling, accelerating AI adoption demands a systematic approach and disciplined execution.
Organisations are realising that enterprise adoption is not just about expanding pilots; it is evolving into a discipline of its own. There is no universally defined approach yet, and enterprises are experimenting with different models.
Capgemini identifies six major accelerators. Stronger executive sponsorship and vision leads at 67%, followed by access to external partnerships at 59%. Upskilling and reskilling the workforce, stronger governance frameworks and regulatory clarity and ethical frameworks each stand at 53%, while scalable data infrastructure and governance stands at 51%.
The point is that enterprise adoption does not have one bottleneck. Leadership, technology, governance, skills and ecosystem capability need to advance together.
Augment or rearchitect
Capgemini distinguishes between two broad paths for enterprise AI initiatives.
The first is Augment: embed AI onto existing systems and workflows, such as copilots in CRM platforms or AI assisted quality assurance processes.
The second is Re-architect: redesign workflows to be automation first using agentic systems, with clear guardrails, oversight and auditability, such as AI driven case routing with human oversight and automated quality checks.
The distinction matters because adding AI to an existing workflow and redesigning the workflow around AI are fundamentally different transformation choices.
Scaling beyond pilots often requires more than adding tools. Organisations need fit for purpose governance, strong data foundations and operating models that define decision rights, escalation paths and accountability so AI can be embedded safely and sustainably.
The more autonomy AI receives, the more important those operating rules become.
AI transformation starts with leadership clarity
Capgemini argues that AI success is not defined by speed of adoption alone, but by clarity of intent.
Organisations that see AI as a multi-year source of advantage need to articulate why they are using it, what outcomes they seek and how humans will work alongside it. Sustainable value comes from investing in the right foundations and building human AI collaboration as a core capability, not from rushing deployment without strategic alignment.
The report’s survey reinforces the leadership role. Among organisations that view stronger sponsorship as an important adoption accelerator, 76% have created cross functional AI steering committees or leadership councils and 69% have established a clear enterprise wide AI vision endorsed by top leadership.
AI transformation is therefore increasingly a leadership and operating model agenda rather than purely a technology programme.
Governance, data and skills need to move together
The adoption playbook also includes much more operational work.
Among organisations pursuing scalable data infrastructure, 82% have improved data quality and reliability through automated validation, deduplication and cleansing pipelines, while 78% have established unified data catalogues and lineage tools.
Among those prioritising workforce upskilling, 82% have launched AI upskilling or reskilling programmes across technical, business and support functions, while 75% have created internal AI academies or structured learning paths.
Governance is developing alongside these capabilities. Among organisations prioritising stronger governance, 78% have clarified the human AI operating model for activities that fall within AI scope, while 76% have established or updated governance frameworks such as Responsible AI councils or ethics boards.
These figures show why enterprise adoption is becoming a discipline. The organisation needs a repeatable way to connect infrastructure, operating rules, workforce capability and leadership.
Data and AI sovereignty become strategic considerations
Data sovereignty is becoming a critical priority amid broader macroeconomic and geopolitical shifts.
Fifty four percent of organisations now prioritise data sovereignty, ensuring that sensitive or regulated data remains under their control even when used with external AI models or platforms.
AI sovereignty extends the question further. Organisations and governments increasingly want greater control over the models and infrastructure shaping critical decisions.
For enterprises, this can influence model selection, cloud architecture, data location, procurement and the balance between proprietary and external AI systems.
The technology architecture therefore increasingly needs to satisfy both speed and control.
AI performance is increasingly judged by enterprise outcomes
Organisations increasingly see AI as a catalyst for strategic transformation, moving beyond a narrow focus on short term financial gains.
Return on investment or business value realised from AI remains the leading performance measure, identified by 73% of organisations. But 68% also track the number of AI use cases deployed or scaled, another 68% track revenue growth attributable to AI, while organisations are also looking at market positioning and employee engagement.
The important shift is that leaders are no longer satisfied simply to count pilots.
AI initiatives are increasingly being held to the same standards as other major investments. The organisation wants evidence that AI affects revenue, operating performance, resilience, employee experience, risk or competitive position.
This represents an important maturity step. Experimentation becomes investment only when organisations can explain what value the technology is expected to create and how that value will be measured.
Human AI chemistry and trust
Human AI collaboration is already creating value
Capgemini defines human AI chemistry as designing AI systems where humans retain control over critical decisions, guide AI behaviour and intervene when needed, ensuring accountability as AI takes on greater scope.
Sixty six percent of organisations report that human AI collaboration has led to measurable improvements in productivity and decision quality. Fifty nine percent say employees feel empowered to use AI in their day to day work.
But only 48% say the organisation has clearly defined roles and responsibilities for humans and AI systems to work together effectively.
That gap is important. Human AI collaboration has already improved productivity and decision making, yet trust must be strengthened to unlock its full potential. Employees need clarity on how AI decisions are made, where accountability lies and how risks are managed. Without this, adoption can stall even when technology is ready.
From assistance towards collaboration
AI is shifting from a simple enabler to a strategic ally embedded in processes, driving efficiency and unlocking better results.
As AI takes over routine governance tasks, humans can increasingly focus on what Capgemini describes as meta governance: designing policies, reviewing exceptions and guiding AI. The role moves from doing every part of the work towards overseeing how the work is performed.
The underlying opportunity is significant, but the majority of organisations still lack fully formalised frameworks for this relationship. That creates a gap between the technological ability to automate work and the organisational ability to control and govern that automation.
Capgemini therefore treats human AI integration not as a soft adoption issue but as a component of enterprise architecture.
Human oversight remains central
The report repeatedly argues that greater AI capability does not eliminate the need for people.
Human judgement, accountability and decision ownership remain central. The question is how those responsibilities change when machines execute more of the underlying tasks.
Capgemini illustrates the shift through examples of AI supporting repetitive work while employees retain responsibility for complex judgement. In healthcare, the report highlights human in the loop clinical systems where clinicians retain control of decisions. In industrial settings, AI assists employees and bridges skill gaps rather than entirely replacing the worker.
The emerging model is therefore not simply people versus machines. It is the allocation of work between them.
The workforce challenge goes beyond AI literacy
Organisations must focus on reskilling and new capability building to enable human AI chemistry.
Capgemini finds that organisations are beginning to redefine traditional skill sets as AI becomes embedded into roles. Human AI collaboration, model evaluation and ethical decision making are among the capabilities receiving greater emphasis.
But this shift goes beyond traditional training.
Employees increasingly need to understand how to supervise AI systems, evaluate their output, intervene when required and take responsibility for decisions made through AI enabled workflows.
The new skills therefore include technical fluency, but also judgement, critical thinking, oversight and the ability to work with intelligent systems whose behaviour may be probabilistic rather than deterministic.
Reimagining the AI augmented enterprise
Enterprise scale changes the problem
As AI moves from experimentation to enterprise scale deployment, organisations face a new challenge: translating rapid advances in AI capabilities into sustained business impact.
This shift requires leaders to rethink how intelligence is embedded across the organisation, from core operations and decision making to governance, talent and technology foundations.
Success depends not only on deploying AI, but on building the capabilities, guardrails and ways of working that allow AI to scale responsibly and deliver value over time.
Capgemini organises this around four priorities:
Strengthen AI essentials to unlock enterprise wide intelligence.
Advance AI readiness through governance, alignment and strategic guardrails.
Build human AI chemistry as a core organisational capability.
Scale AI value by balancing Transform Now and Build Tomorrow.
1. Strengthen AI essentials
The first priority is to create a foundational engine for organisation wide AI access and value generation.
Capgemini recommends creating what it calls an Intelligence as a Service engine for the enterprise. Access to AI models and inference can be centralised so teams use AI consistently, while internal and external models can be connected through common interfaces to reduce fragmentation.
Data assets then need to become an intelligence fuel system. Data needs to support real time, multimodal and agent driven use cases, while organisations improve the efficiency of inference, caching and resource usage.
The model ecosystem also needs to balance sovereignty and speed. Organisations can combine internal, regional and external models to meet regulatory, cost and performance requirements while maintaining control over sensitive data.
Finally, infrastructure itself needs to scale for a world of continuous, real time and agent based AI workloads.
2. Advance AI readiness through governance, alignment and strategic guardrails
Capgemini argues that AI strategy should begin with CEO led priorities and enterprise goals.
Leaders need to clarify where AI should drive efficiency today and where it should enable longer term transformation. Leadership attention should be concentrated on use cases that matter most to the business rather than activity for its own sake.
Governance should then reflect the level of AI autonomy. Organisations need clear rules for when humans guide decisions and when AI can act independently. They must define who owns decisions, when humans step in and how exceptions are handled.
Digital sovereignty and resilience also need protection across hybrid technology environments. Organisations need control over data, models and decisions across different vendors and clouds, with security embedded directly into AI systems.
Finally, readiness needs to become institutionalised. Shared playbooks can standardise how teams experiment, deploy and manage AI over time, while guardrails establish expectations for reliability, transparency and risk.
Leadership, governance and operating models need to move in sync
Capgemini argues that readiness accelerates when leadership, governance and operating models move together.
One of the recurring failure modes is using AI simply as a patch for productivity while leaving the underlying organisation untouched. This can create incremental gains without materially changing how value is created.
Governance cannot be separated from this redesign. AI systems need to be catalogued, tracked, supervised and secured like other enterprise assets. As AI becomes more autonomous, governance needs to become more operational rather than remaining purely policy based.
At the same time, organisations need an operating model that can translate strategy into decisions about workflows, roles, autonomy and accountability.
3. Build human AI chemistry as a core organisational capability
Capgemini identifies three priorities for human AI chemistry.
The first is to redesign workflows around human AI teaming. Roles should be redefined so AI supports work while humans retain control over important decisions. AI can handle repetitive patterns while complex judgement is escalated to people.
The second is to develop hybrid skills that amplify human potential in AI driven environments. Employees need skills to work effectively with AI, including prompting and oversight. Teams also need to understand when to trust AI and when to intervene.
The third is to build behavioural trust through transparent and explainable AI. AI needs to behave predictably enough to reduce friction, while decisions need to be understandable and auditable.
The critical point is that trust is not created simply by telling employees to trust the technology. It emerges from reliable behaviour, understandable decisions, clear responsibility and the ability to intervene.
From human in the loop to human on the loop and human in the lead
Human AI chemistry is becoming one of the defining challenges in enterprise wide adoption. Keeping humans “in the loop” is no longer enough.
As AI becomes embedded across workflows and decisions, organisations must enable employees to supervise, challenge and direct AI systems, not just use them. This shift cannot be addressed through reskilling alone. Employees need the confidence and authority to intervene in AI driven processes and take accountability for outcomes.
Capgemini describes this as moving beyond human in the loop towards human on the loop and ultimately human in the lead.
The future therefore requires more than teaching employees how to interact with individual AI tools. People increasingly need to orchestrate fleets of agents, manage exceptions and own decisions across increasingly automated workflows.
The practical challenge is helping engineers, managers and other professionals transition away from hands on execution and direct oversight towards roles centred on judgement, supervision and orchestration.
Structured academy programmes and experiential learning can support that transition, but capability development also needs to include adaptability, critical thinking and design judgement.
AI value depends on redesigning roles and decision authority
Capgemini argues that AI delivers value when organisations redesign roles, skills and decision authority around people.
Every role will evolve to some degree. Some tasks will be removed, some automated, others augmented and new responsibilities created.
Organisations therefore need to think beyond whether a particular task can be automated. They need to decide what the remaining human role becomes, which decisions that person owns, how performance should be evaluated and what new capabilities are required.
This is particularly important as AI systems become embedded into high consequence workflows. Security, transparency and clear governance need to develop alongside workforce transformation.
Training people to write better prompts can help with immediate adoption, but it does not answer the larger operating model question of who owns the result when AI is part of the work.
4. Balance Transform Now and Build Tomorrow
The fourth priority is to scale AI value by balancing Transform Now and Build Tomorrow.
Transform Now means embedding AI into core workflows to improve productivity, decisions and customer experience. Organisations should focus on use cases that simplify operations and deliver clear, measurable results, then integrate successful pilots into core systems and processes.
Build Tomorrow means exploring new business models and operating approaches using more advanced AI capabilities. Experimentation can be separated from core operations where necessary to protect current performance, while reusable AI assets are developed to accelerate future innovation.
Capgemini argues that these should not be treated as competing agendas. Insights from live deployments can shape future innovation bets, while governance should enable speed today and safe scaling tomorrow.
The enterprise therefore needs two clocks running at once: one improving current operations and another creating optionality for future reinvention.
Strategy needs to remain flexible
The report closes the recommendation section with an important warning about the pace of technology.
For the first time in modern enterprise IT history, technology is moving faster than adoption can follow. Strategies can no longer be static; they must evolve continuously.
What organisations design today may need rewriting tomorrow as new models, infrastructure and operating paradigms emerge.
Flexibility is therefore not a temporary response to an immature market. It becomes a foundational capability for operating in an environment of continuing technological change.
Capgemini's Resonance AI Framework
Capgemini brings the report together through its Resonance AI Framework, which provides a sequential approach to AI driven transformation.
At the foundation sit AI essentials, covering scalable enterprise data foundations, advanced models and applications with built in AI capabilities.
The next layer is AI readiness, which introduces the enablers and guardrails required to secure, govern, customise and operationalise AI.
The third layer is human AI chemistry, where organisations design clear roles and interactions that allow humans and AI to collaborate reliably.
Once those layers are established, organisations can capture successive waves of AI value, ranging from operational efficiency and personalised experiences through to business reinvention and next frontier innovation.
The progression is therefore not simply from less AI to more AI.
It is from access, to adaptation, to adoption, supported by stronger foundations, governance and human AI collaboration.
Methodology
Capgemini Research Institute conducted the underlying survey in November 2025.
The research surveyed 1,505 executives working at organisations with more than US$1 billion in annual revenue across 15 industries in North America, Europe, APAC and Latin America. All of the organisations surveyed had already deployed AI at limited or full scale, and respondents were director level or above. Final-Web-Version-Research-Brie…
The sample spans general management and strategy, finance and risk, procurement and supply chain, technology and digital, operations, marketing, human resources, innovation, sales and sustainability.
The report therefore does not primarily measure whether organisations have begun experimenting with AI. It focuses on enterprises that have already entered the adoption journey and examines how they intend to scale it.
Conclusion
The central message of The multi-year AI advantage is that the next stage of enterprise AI will be determined less by access to individual models and more by the organisation built around them.
AI investment is continuing, but it is becoming more disciplined. Organisations are consolidating experiments, directing resources towards proven opportunities and building the infrastructure required for sustained use.
As adoption spreads, the technology question increasingly becomes an operating model question. Organisations need to determine where AI augments existing work and where workflows should be redesigned entirely. They need decision rights, escalation mechanisms, governance, data and infrastructure capable of supporting increasingly autonomous systems.
The workforce challenge changes at the same time. Employees need more than AI literacy. They need to supervise, challenge and orchestrate AI systems, exercise judgement when automation reaches its limits and remain accountable for outcomes.
That is why Capgemini places human AI chemistry at the centre of its framework.
Enterprise value depends not simply on whether humans and AI can technically interact, but whether the organisation has intentionally designed how they work together.
The report therefore describes a progression from isolated AI deployments towards the AI augmented enterprise: an organisation where intelligence is embedded across workflows, technology and data foundations are scalable, governance reflects autonomy, and people retain clear authority over consequential decisions.
The organisations that build that capability over several years may create an advantage that is increasingly difficult for later adopters to replicate.
Key takeaways
AI is no longer being treated primarily as an experimental technology. Organisations increasingly see it as a multi year capability that will become embedded across the enterprise, and investment is shifting towards durable foundations such as data, infrastructure, governance and workforce capability.
The next challenge is enterprise wide adoption. Scaling requires more than increasing the number of use cases. Leaders need to align technology, operating models, governance, skills, decision rights and accountability.
Generative AI is already moving into scaled use, while agentic AI is beginning to introduce greater autonomy into workflows. As autonomy rises, organisations need clearer rules for where AI acts, where people intervene and who ultimately owns decisions.
Human AI collaboration is already producing measurable improvements, but operating model maturity is lagging. Two thirds of organisations report gains in productivity and decision quality, while fewer than half have clearly defined the respective roles of humans and AI.
The workforce response therefore cannot stop at reskilling. Employees increasingly need the authority and capability to supervise, challenge and orchestrate AI enabled systems while retaining accountability for outcomes.
Capgemini’s resulting model is built around four priorities: strengthen AI essentials, build organisational readiness and guardrails, establish human AI chemistry and balance immediate transformation with longer term reinvention.
The multi year AI advantage is ultimately not created by deploying the most AI. It is created by building an enterprise capable of absorbing, governing and continuously reorganising around increasingly capable AI.
