Deloitte: State of AI in the Enterprise - The untapped edge
Executive summary
This report argues that many organizations are still at the “untapped edge” of AI. Progress is real: workforce access is expanding, pilots are moving toward production, and leaders report rising productivity and confidence. But the report highlights persistent gaps between experimentation and enterprise transformation, between access and actual daily use, and between strategic readiness and operational readiness in talent and infrastructure.
It also emphasizes that the next phase of AI is broader than GenAI alone. The report spotlights agentic, physical, and sovereign AI as major forces reshaping how organizations operate, govern, and compete, and it frames success as a deliberate shift that redesigns core processes and operating models with AI while elevating human strengths such as judgment, creativity, empathy, and relationship-building.
Key stats and quotable claims
Workforce access to AI tools expanded by 50% in one year, from under 40% to under 60%.
25% of respondents said their organization has moved 40% or more of AI experiments into production to date; 54% expect to reach that level within 3 to 6 months.
34% of companies are starting to use AI to deeply transform their businesses; 30% are redesigning key processes; 37% are using AI at a surface level.
Nearly 3 in 4 (74%) companies plan to deploy agentic AI within two years; 1 in 5 (21%) report a mature model for governance of autonomous agents.
Physical AI adoption is 58% today and is projected to hit 80% within two years.
Overall summary
Deloitte frames January 2026 as an “activation” moment: many enterprises have moved past curiosity and pilot proliferation, but they have not yet converted broad AI availability into durable enterprise value at scale. The report’s central argument is that AI advantage is increasingly determined by activation mechanics (how AI shows up in daily work), scale mechanics (how pilots become production systems), and reinvention mechanics (how organizations redesign work, products, and operating models), not by access to AI tools alone.
1) Access is expanding faster than adoption
Workforce access to sanctioned AI tools expanded by 50% in one year (from under 40% to under 60%). But among workers with access, fewer than 60% use AI in their daily workflow, a pattern “largely unchanged from last year.” Deloitte treats this as the leading indicator of the “untapped edge”: organizations can report deployment progress while still leaving productivity and innovation value dormant because the day-to-day system (permissions, integration, reliability, training, incentives, manager expectations) doesn’t compel or enable habitual use.
2) Scaling stalls because pilots and production are fundamentally different
Only 25% say their organization has moved 40% or more of AI experiments into production to date, though 54% expect to reach that level within three to six months. Deloitte explains why so many organizations get stuck: pilots run “with a small team in a few months using cleansed data in an isolated environment,” while production requires “infrastructure investment, integration with existing systems, security reviews, compliance checks, monitoring systems, and ongoing maintenance.” Production also exposes edge cases, operational risk, and time dilation (use cases stretching far beyond initial estimates). The report’s message is that value capture depends on designing for deployment from day one, rather than treating scale as a later phase.
3) Most organizations are seeing productivity, but only a subset are rewriting the business
AI’s reported business impact is rising (with 25% reporting a transformative effect, up from 12% a year ago), and leaders expect a broadening set of benefits. But Deloitte emphasizes that the market is splitting by depth of transformation: 34% are starting to use AI to deeply transform products, processes, and business models; 30% are redesigning key processes around AI while keeping business models intact; and 37% are using AI at a surface level with little or no change to existing processes. Deloitte also explicitly flags that revenue outcomes lag efficiency outcomes—many “hope to achieve” revenue growth versus fewer achieving it today—implying that reinvention and new value creation require more than efficiency-focused deployments.
4) Automation expectations are rising faster than work redesign and talent system change
Deloitte notes that more than a third (36%) expect at least 10% of jobs to be fully automated within a year, and 82% expect that level of automation looking out three years. Yet 84% have not redesigned jobs around AI capabilities. The report argues AI “doesn’t just augment existing processes” but “often requires fundamentally rethinking operating models and how work gets done,” including new career pathways as entry-level roles are automated, and shifts in supervisory/managerial work toward orchestration of human–AI teams (with exploration of flatter structures).
5) The frontier is widening: agentic, sovereign, and physical AI expand the control surface
Deloitte frames GenAI as only one chapter; the next phase introduces new governance, geopolitical, and operational constraints.
Agentic AI: Agents can “set goals, reason through multi-step tasks, use tools and APIs, and coordinate work with people or other agents.” Nearly 3 in 4 (74%) plan to deploy agentic AI within two years, but only 1 in 5 (21%) report having a mature model for governance of autonomous agents. The core risk is scale outrunning guardrails.
Sovereign AI: Deloitte defines sovereign AI as designing, training, and deploying AI under local laws, on infrastructure the organization controls, using locally governed data. It reports that 83% view sovereign AI as at least moderately important to strategic planning (with 43% rating it very/extremely important), and that organizations increasingly factor country of origin into vendor decisions and build stacks with local vendors. The implication is that “where AI is built” becomes a strategic requirement, not a procurement footnote.
Physical AI: Deloitte defines physical AI as systems that perceive the real world, make decisions, and drive physical actions through machines/control systems. Adoption is already meaningful (58% report at least limited use) and is expected to grow quickly (to 80% within two years). Deloitte emphasizes that physical deployment changes the economics (total cost of ownership), regulatory burden, and safety/monitoring stakes versus software-only AI.
6) Preparedness is uneven: strategy is ahead of infrastructure and talent
Finally, Deloitte highlights a readiness imbalance: organizations feel more prepared in strategy and risk/governance than in technology infrastructure, data management, and especially talent, reflecting the persistent difficulty of modernizing systems and skills at the pace of innovation.
Taken together, the report argues that capturing AI’s “untapped edge” requires leaders to close the access-to-activation gap, engineer the pilot-to-production pathway, redesign work and career paths (not just train people), and build governance and infrastructure that can keep pace with agentic, sovereign, and physical AI—while shifting from incremental efficiency toward strategic reinvention.
Two additional threads sharpen this thesis.
7) Governance is not just risk control; it is the scaling mechanism
Deloitte is explicit that governance becomes the “mechanism that enables rapid, confident scaling,” not a back-office compliance layer. The report’s own risk ranking underscores why: companies cite data privacy and/or security, legal and regulatory compliance, governance capabilities and oversight, and model quality/explainability as top concerns. The implication for the operating model is that governance has to be designed as part of the production pathway (inventory, monitoring, audit trails, escalation, accountability), otherwise scale stalls or risk compounds.
8) Physical AI changes the economics (TCO) and the implementation clock
Where software deployments can be rolled out rapidly, Deloitte notes that physical deployment is slower because it carries higher costs and capital requirements, longer development cycles, stricter safety regulations, and the need for specialized hardware and maintenance. The report highlights that decision-makers should account for total cost of ownership (facility retrofits, sensors and robots, integration work, maintenance and spare parts, and downtime). This extends the overall summary’s “activation” point: organizations need different planning assumptions when AI is moving from knowledge work to operations.
9) The report’s implied “whole-product” requirement
Across these findings, Deloitte’s implied definition of success is not “AI usage” but an end-to-end system: access + activation + production-grade delivery + redesigned work + governance + infrastructure. The report’s six focus areas at the end (close the access/activation gap, redesign work around AI, build governance before scale, address sovereign AI requirements, build a living technology and data infrastructure, pursue strategic reinvention) can be read as a checklist for that whole product. Organizations that treat these as separate initiatives tend to stay at the edge; those that run them as one integrated operating model are positioned to turn early momentum into durable advantage.
Deep dive
Introduction
Throughout history, some of our greatest leaps forward have come when human ingenuity combines with transformative technology. Each era’s boldest breakthroughs—from harnessing steam to building the internet—began when people leveraged new tools to amplify their potential. Artificial intelligence (AI) is the latest chapter in this story. It has already transformed the way we work and create, yet we have barely scratched the surface of what’s possible when human expertise and AI capabilities unite.
Business leaders today face an unprecedented challenge: moving beyond pilots to truly integrating AI into the heart of their organizations. It requires a deliberate shift in which people set a vision and make responsible choices, and AI provides the insights, speed, and scale to deliver against that ambition. That means redesigning core processes and operating models with AI, ensuring that human strengths—such as judgment, creativity, empathy, and relationshipbuilding—are elevated, not automated.
On the one hand, we see clear acceleration from organizations: with wider workforce access to AI tools, early productivity gains, and growing confidence in AI’s potential. On the other hand, we see a gap between experimentation and true enterprise transformation. Many organizations are primarily using AI to drive efficiency, while a smaller group are pulling ahead by beginning to reimagine business models, offerings, roles, and ways of working.
Key trends are also reshaping the future of AI: Sovereign AI is redefining technological autonomy for nations and organizations alike, impacting trust and competitiveness; Agentic AI is enabling autonomous reasoning and action, raising new governance and control challenges; Physical AI is merging the digital and material worlds, making safety and human oversight critical.
Overview
Organizations today stand at the untapped edge of AI’s potential. Ongoing developments in agentic, physical, and sovereign AI present new challenges and opportunities. Momentum is building, yet the greatest gains still lie ahead as organizations translate early progress into scalable impact.
Surveyed companies have broadened worker access to AI by 50% in just one year—growing from fewer than 40% to around 60% of workers now equipped with sanctioned AI tools. While only 25% of respondents said their organization has moved 40% or more of their AI experiments into production to date, 54% expect to reach that level in the next three to six months, demonstrating the pathway to value is clear and achievable.
Today, 34% of companies are starting to use AI to deeply transform their businesses, 30% are redesigning key processes around AI and the remaining 37% are only using AI at a surface level with little or no change to underlying business processes.
About the Annual State of AI in the Enterprise Report
This annual report was fielded to 3,235 director-level to C-suite-level respondents across six industries and 24 countries between August and September 2025. Industries included: consumer; energy, resources and industrials; financial services; life sciences and health care; technology, media and telecom; and government and public services. The survey data was augmented by additional insights from 15 interviews with global C-suite executives and AI and data science leaders at large organizations across a range of industries. For details on methodology, please see page 40. This annual report is part of an ongoing series by the Deloitte AI Institute™ to help leaders in business, technology and the public sector track the rapid pace of AI change and adoption.
Key findings
AI is moving from the pilot and experimentation phase to enterprise scaling as worker access to AI expands
According to our latest survey, workforce access to AI has expanded by 50% in just one year—growing from under 40% to under 60% of workers with sanctioned access to AI tools, and 11% of leading companies currently provide workers with near-universal (more than 80%) access to sanctioned AI tools. However, among those workers with access, fewer than 60% use it in their daily workflow—a pattern that remains largely unchanged from last year. This suggests that while access is widening, enterprise AI remains underutilized, and its productivity and innovation potential are still largely untapped.
The scale acceleration is beginning
Moving from pilot to production is arguably the most important step in capturing AI value—yet this is where many companies stall.
Today, 25% of respondents said their organization has moved 40% or more of their AI experiments into production to-date; however, 54% expect to reach that level in the next three to six months, demonstrating the pathway to value is clear and achievable.
The proof-of-concept trap
Why do so many pilots fail to reach production?
The answer lies in a fundamental mismatch between pilot and production requirements.
A pilot typically can run with a small team in a few months using cleansed data in an isolated environment. However, production deployment typically requires infrastructure investment, integration with existing systems, security reviews, compliance checks, monitoring systems, and ongoing maintenance—each of which demand significantly more resources and coordination.
“If there is no coherent AI strategy in organizations, you are likely to see pilot fatigue.”
AI transformation reveals productivity for most, business reimagination for a few
AI’s real-world business impact is rising fast, with 25% of leaders now reporting that AI is having a transformative effect on their companies—more than double from 12% a year ago.
Organizations are redefining how they work, but not all are diving to the same depth
Among the surveyed companies, one-third (34%) are already starting to use AI to deeply transform their businesses—creating new products and services, reinventing core processes, or even fundamentally changing their business models. Another third (30%) are redesigning key processes around AI but keeping their business models intact. And the remaining third (37%) are using AI at a more surface level, with little or no change to existing processes.
Additional findings (agentic, physical, sovereign)
AI agents are scaling faster than the guardrails
Autonomous AI agents are racing into the enterprise, but oversight is lagging. Nearly 3 in 4 (74%) companies plan to deploy agentic AI within two years. Yet, 1 in 5 (21%) report having a mature model for governance of autonomous agents, raising the specter of unintended risks.
Physical AI is already embedded in operations—and its footprint is growing fast
Physical AI is rapidly becoming integral to operations worldwide, with 58% of companies already using it to some extent and adoption projected to hit 80% within two years.
With sovereign AI taking hold, where technology is built matters as much as what it can do
Sovereign AI is about more than technology ownership. It’s about strategic independence. More than 3 in 4 companies (77%) say the location of AI development is a key factor when choosing new technologies, signaling that geographic sovereignty is now as important as innovation.
AI fluency and work redesign
Companies are focused on building AI fluency instead of redesigning work around AI
Within a year, more than a third of surveyed companies (36%) expect at least 10% of their jobs to be fully automated. The majority of surveyed companies (82%) expect at least 10% of their jobs to be fully automated when looking out three years.
These changes require careful thinking about career pathways. Leaders in the qualitative interviews expressed concerns about potential disruption to professional development pipelines as a result of automation. Entry-level jobs involving data entry, reconciliation, and first-level customer support at their companies are being prioritized for automation, but these jobs are often the starting point for longer careers. Organizations will likely need to develop alternate pathways for professional advancement, ensuring that employees have expertise that includes foundational processes.
Most companies have yet to redesign jobs around AI
Despite high expectations for automation, 84% of companies have not redesigned jobs around AI capabilities.
AI doesn’t just augment existing processes. It often requires fundamentally rethinking operating models and how work gets done.
Entry-level and task-aligned roles could be most affected, as automation may replace common, time-consuming tasks. However, as front-line jobs become more automated, supervisor and managerial roles will likely shift toward orchestration of human-AI teams.
This is prompting many organizations to explore flatter structures: 53% have considered pod-based or non-hierarchical models since fewer roles require supervision of large teams; however, only 16% have moved to such models to a great or maximum extent.
Talent strategies are falling short
According to the leaders surveyed, insufficient worker skills are the biggest barrier to integrating AI into existing workflows. Yet, fewer than half of companies are making significant adjustments to their talent strategies, with most (53%) simply focusing on educating employees to raise AI fluency.
While most are focused on educating employees, far fewer are rearchitecting roles, workflows, and career paths.
Sovereign AI
With sovereign AI taking hold, where technology is built matters as much as what it can do
Sovereign AI is when a country—and the companies operating within it—design, train, and deploy AI under their own laws, on infrastructure they control, using locally governed data. The goal is to reduce dependence on foreign vendors for critical AI capabilities.
More than 8 in 10 companies (83%) view sovereign AI as at least moderately important to their strategic planning, and nearly half (43%) rate it as very important or extremely important.
More than 3 in 4 companies (77%) now factor an AI solution’s country of origin into their vendor selection decisions, and nearly 3 in 5 (58%) now build their AI stacks primarily with local vendors.
Ultimately, sovereign AI isn’t just about technology ownership. It’s about strategic independence.
Agentic AI
AI agents are scaling faster than the guardrails
After years of non-AI chatbots that answered basic questions, companies are now deploying sophisticated AI agents that can set goals, reason through multi-step tasks, use tools and application programming interfaces (APIs), and coordinate work with people or other agents.
Agentic AI will surge
Today, 23% of companies are using agentic AI at least moderately. However, within the next two years agentic AI is expected to become nearly ubiquitous, with nearly 3 in 4 companies (74%) using it at least moderately, 23% using it extensively, and 5% fully integrating it as a core component of their operations.
Physical AI
Physical AI is already embedded in operations—and its footprint is growing fast
Physical AI is the class of AI systems that perceive the real world, make decisions, and drive physical actions through machines or control systems. It sits at the intersection of AI and machine learning, sensors, controls, and robotics.
Physical AI integration is already expanding, with 58% of companies reporting at least limited use of physical AI, and among these, 18% are leveraging it to a moderate or greater extent.
However, the percentage of companies using physical AI in any capacity is expected to reach 80% within two years—with 15% using physical AI extensively and 3% fully integrating it as a core element of their operations.
Survey results indicate that organizations in Asia Pacific (AP) are leading in early implementation of physical AI. 71% of AP respondents report at least minimal use of physical AI, compared with 56% in both the Americas and EMEA.
A key factor in early adoption is environmental control. Physical AI use cases that take place in controlled domains such as factories and warehouses tend to progress much faster than use cases in open, real-world environments, where the challenges and risks are far more complex and unpredictable.
Controlled environments are leading the way
Physical AI applications span a wide range of industrial and commercial settings. For example, one company we interviewed is automating package sorting and routing while granting warehouse robots more autonomy to decide where and how to store items to maximize floorspace. Other common use cases include collaborative robots (cobots) on assembly lines, inspection drones with automated response capabilities, robotic picking arms, and autonomous forklifts.
Types of physical AI with the greatest expected impact
As physical AI gains broader adoption, certain types are expected to have a bigger long-term impact than others: intelligent security systems and/or smart monitoring (21%); collaborative robotics (20%); and digital twins (19%).
AI preparedness
Leaders feel more strategically ready for AI than operationally ready in infrastructure and talent.
Despite the rapid evolution of AI beyond Generative AI (GenAI) to agentic and physical AI, 42% of companies believe their strategy is highly prepared for AI adoption and 30% say the same about risk and governance, both increasing since last year’s report (+3 and +6 percentage points, respectively). These areas have likely advanced more quickly as they depend primarily on executive decision-making and policy development.
Meanwhile, perceptions of high preparedness have shifted down compared with last year for technical infrastructure (43%), data management (40%) and talent (20%), revealing the persistent challenge of modernizing systems and skills at the speed of innovation.
“Nearly 80–90% of new use cases are generative AI. So yes, companies prepared, but for a different future. GenAI needs a new set of capabilities.”
Tapping into AI’s full potential
The research is telling: AI’s transformational potential is real, but capturing it requires far more than just technology investments. Organizations should treat AI as foundational. The most successful won’t be those with the most AI projects or the biggest budgets, but those who build AI into the foundation of how they operate, compete, and grow.
Here are six key focus areas to help your business capture AI’s untapped edge:
Close the gap between access and activation
Most organizations have deployed AI tools, but far fewer have achieved meaningful usage. The gap between availability and adoption is now the primary barrier to value. Successful companies focus on activation, not just access.
High-performing implementations start with empowered employees who experiment, share early wins, and become internal champions. Top-down directives alone rarely drive meaningful change. Grassroots adoption supported by senior sponsorship creates momentum and helps ensure solutions align with real workflows.
Activation requires early attention to practical constraints: system integration, data permissions, and operational reliability. For organizations applying AI not only to digital processes but also to physical systems—such as robotics, IoT devices, or machinery—early planning for these operational realities is especially critical.
Organizations that design for deployment from the outset, rather than treating scale as an afterthought, see far higher adoption. Hands-on, role-specific training and visible executive advocacy materially shift employee behavior.
Leaders that treat pilots as stepping stones to production, not isolated experiments, are likely to achieve faster and more durable impact.
Unlock human advantage by redesigning work around AI
AI is reshaping work at every level. While most organizations currently focus on personal productivity, leaders are rebuilding processes, roles, and career paths around expanded AI capabilities.
The most successful organizations reimagine jobs to seamlessly combine human strengths and AI capabilities, ensuring both aspects are used to their fullest potential. New roles—AI operations managers, human-AI interaction specialists, quality stewards, and others—signal a deeper shift: AI is now a structural component of how work is organized.
Advanced organizations streamline workflows that AI can execute end-to-end, while humans focus on judgment, exception handling, and strategic oversight. The goal isn’t to replace humans or merely assist them, but to create complementary working relationships between humans and AI, in which the combined output exceeds what either could achieve alone.
Organizational structures are beginning to flatten as AI absorbs routine execution tasks. Some companies are merging technology and people-leadership functions to ensure that systems and workforce design evolve together. The pace varies by industry, but the direction is consistent: Roles, skills, and career paths should be rebuilt, not simply adjusted.
Organizations should take an AI-native approach and redesign work holistically rather than layering AI onto legacy processes.
Build governance before you scale and make it everyone’s role
Governance is no longer a compliance exercise; it’s the mechanism that enables rapid, confident scaling. Enterprises where senior leadership actively shapes AI governance achieve significantly greater business value than those delegating the work to technical teams alone.
True governance makes oversight everyone’s role, embedding it into performance rubrics so that as AI handles more tasks, humans take on active oversight. This shared responsibility empowers employees to help identify challenges and guide safe, trusted AI use.
Effective governance integrates with existing risk and oversight structures, not parallel “shadow” functions. It focuses on identifying high-risk applications, enforcing responsible design practices, and ensuring independent validation where appropriate.
Leading organizations proactively monitor evolving legal requirements and build systems that can demonstrate safety, fairness, and compliance.
Autonomous systems heighten needs for data and cybersecurity governance. Organizations need to define where humans should remain in control, how automated decisions and data use are audited, and which records of system behavior should be retained.
Cross-functional teams—technology, legal, compliance, and business—establish governance frameworks early so that scale does not outpace control.
At the same time, governance should be calibrated to balance risk management with innovation, ensuring that oversight enables experimentation rather than constraining it. The objective is not to add bureaucracy but to create clear, adaptive guardrails that allow responsible progress at speed.
Address sovereign AI requirements with focus and discipline
As national governments accelerate efforts to establish sovereign AI capabilities, enterprises will likely navigate increasingly complex expectations around data control, model transparency, compliance, and localization.
Sovereign AI is no longer limited to the public sector; it is reshaping requirements for every organization handling sensitive data or operating across jurisdictions.
At the same time, compute strategy becomes a core component, requiring careful evaluation of both data residency and processing locations (e.g., in cloud, on-prem, hybrid, or edge environments) to remain aligned with evolving regulations and performance needs.
Leading companies take a focused approach: assessing which data and workloads must remain within national or regional boundaries, determining where local model hosting is mandatory, and clarifying how transparency, auditability, and documentation standards differ across markets.
They establish clear policies for data residency, model retraining, and cross-border flows, supported by infrastructure capable of meeting multiple regulatory regimes simultaneously.
Enterprises that ignore sovereign AI constraints will face escalating operational disruption, higher compliance risk, and restricted access to key markets.
Those that proactively engage build strategic advantage: They can reduce regulatory uncertainty, enhance customer trust, and position themselves as preferred partners in industries where sovereignty concerns dominate.
Sovereign AI readiness is now a core element of enterprise resilience and global competitiveness, not a specialized compliance task.
Build a “living” technology and data infrastructure for tomorrow’s AI
Legacy data and infrastructure architectures cannot power real-time, autonomous AI. As AI capabilities extend beyond software into devices, machinery, and edge locations, organizations need to evaluate if their technology foundations are ready to support potential physical AI deployments.
Modernization should create a living AI backbone: an organization-wide, real-time system that adapts dynamically to business and regulatory change, elevating infrastructure from IT initiative to strategic capability.
Leaders are enabling modular, cloud-native platforms that securely connect, govern, and integrate all data types, fostering rapid experimentation and seamless scaling.
They break down silos with domain-owned data products and embed privacy, sovereignty, and security-by-design, while enforcing enterprise standards for quality, interoperability, and lineage.
This balanced approach delivers decentralized innovation supported by centralized control.
A unified, trusted data strategy is indispensable. Poor or fragmented data compounds risk and undermines every AI initiative.
Forward-thinking organizations converge operational, experiential, and external data flows and invest in evolving platforms that anticipate the needs of emerging AI.
Infrastructure determines enterprise velocity; those that modernize early will likely accelerate while others remain constrained.
Pursue strategic reinvention, not incremental efficiency
A widening performance divide separates companies treating AI as core to strategy from those viewing it as a cost-saving tool.
Leading organizations invest heavily in using AI to reshape operations and create new revenue streams, resisting the pressure to chase every trending technology in favor of initiatives that genuinely advance strategic goals and deliver real value.
These organizations pursue growth across multiple horizons: strengthening current operations, expanding into adjacent markets, and building entirely new businesses enabled by AI.
They rethink their organizations from the ground up and imagine how to build without legacy constraints, rather than digitizing old processes.
This extends to reimagining business models and adapting to emerging trends like sovereign AI.
This intentional reinvention is one of the strongest predictors of achieving outsized returns.
Autonomous AI systems are accelerating this shift. In knowledge-intensive industries, they can absorb substantial routine work, enabling people to focus on higher-order activities.
High performers are reorganizing around systems that perceive context, make decisions, and act independently, balancing bold transformation with operational continuity.
They move at a pace suited to their organization’s readiness, making thoughtful trade-offs and fostering informed decision-making grounded in evidence rather than hype.
The strategic opportunity is discovering new sources of value that competitors cannot easily replicate.
With developments in agentic, physical, and sovereign AI rapidly expanding the boundaries of what’s possible, companies today are at the edge of tapping into AI’s full potential.
Whether it’s figuring out how to capitalize on the latest cutting-edge innovations, making the leap from pilots to large-scale deployment, or using AI to create an enduring competitive advantage, enterprises around the world are on the edge of transforming themselves with AI.
The challenge now is activation: bridging the gap from tool access to meaningful adoption, moving beyond experimentation to operationalizing AI at scale, embedding AI into core business processes—transforming technology potential into enterprise value.
Key takeaways
Close the access to activation gap by designing for daily workflow use, not just tool availability.
Treat scaling as an operating model problem: move beyond pilots with infrastructure, integration, monitoring, and governance.
Redesign roles and processes so human strengths are elevated, not automated.
Build governance before deploying autonomous agents widely.
Plan for physical AI safety and oversight as AI moves into operations.
Incorporate sovereign AI constraints into technology strategy and vendor selection.
