In an era defined by resilience and continuous adaptation, where enterprise readiness is critical to delivering digital transformation outcomes, talent and execution capacity have become strategic assets.
This article explores one of the three overarching trends shared in our June 2026 press release, C-Suite AI adoption is rising, yet ambition is outpacing enterprise readiness. Based on conversations with more than 1,800 business and technology leaders worldwide, the insights reveal that while AI adoption continues to accelerate, many organizations are struggling to build the talent, delivery capacity and organizational alignment required to turn transformation strategies into measurable outcomes.
Understanding where execution capacity gaps exist can help leaders prioritize the talent, partnerships and operating model changes needed to accelerate modernization, scale AI and maximize AI value.
In our 2026 Voice of Our Clients research, executives cite that cost pressure remains the number one constraint to achieving business priorities. At the same time, 45% report that legacy systems pose a significant challenge to their data and AI strategies. Talent shortages continue to limit progress, with 69% reporting medium-to-high difficulty recruiting IT talent and 52% saying those shortages are materially impacting their programs.
The challenge is increasingly clear: While a majority of executives cite having an AI and digital strategy, many lack the talent and capacity management to execute those strategies.
Talent and capacity management become strategic assets
The challenge is no longer deciding what to change. Mature organizations understand the importance of modern data foundations, operating model evolution and workforce development. The challenge is accessing highly sought-after talent and building sufficient capacity to execute these priorities simultaneously and at scale.
As AI becomes embedded in core business and operational processes, it transcends being a technology initiative and becomes a catalyst for enterprise-wide transformation.
This shift matters because AI is compressing transformation timelines. Organizations no longer have years to modernize platforms, redesign processes and build new capabilities before realizing value. Competitive advantage increasingly depends on how quickly organizations can convert strategy into execution. As a result, talent and execution capacity have become strategic imperatives rather than operational concerns.
The organizations making the greatest progress are not necessarily those investing the most in technology. They are the ones creating the capacity to continuously modernize, innovate and adapt. They understand that scaling AI requires business and technology alignment, effective change management and access to the right skills and expertise.
Capacity management in the AI era is not solely a talent challenge
Execution capacity is also an alignment challenge. Many organizations continue to struggle with business and IT alignment to translate strategic priorities into coordinated execution, with only 48% citing deep alignment. Interviewed executives cite change management as essential to accelerating their AI and digital strategies, recognizing that transformation succeeds when people, processes and technology evolve together.
Organizations are also responding by expanding their ability to execute through multiple approaches. They are using automation to reduce operational burden, investing in talent development and AI skills, accessing specialized expertise through global delivery models, and increasingly leveraging managed services and strategic partnerships to supplement internal AI capabilities.
To achieve this, high-performing organizations are shifting their view of partnerships. They seek partners who can help them integrate intelligent automation directly into workflows to free up internal resources, leverage specialized business process services to handle non-core operations, and benefit from scalable managed IT services to keep core tech stacks agile and responsive.
A different operating model for AI-enabled transformation
Historically, organizations viewed external partners primarily as sources of efficiency and scale, often relying on them to maintain existing operations, support legacy environments or lower costs. Today, the dynamic is fundamentally different. Organizations are looking for partners that can accelerate modernization, shifting the focus from "keeping the lights on" to reengineering technology foundations and driving enterprise-wide transformation.
The objective is no longer to supplement internal resources. It is to create the capacity to modernize faster, scale innovation more effectively and realize greater value from AI investment.
When structured effectively, these models can help organizations overcome talent shortages, modernize legacy environments, strengthen resilience and accelerate AI adoption. Just as importantly , they provide access to specialized capabilities that many organizations cannot develop or sustain on their own.
The most successful organizations are therefore moving beyond transactional supplier relationships toward strategic partnerships built around shared outcomes and long-term value creation.
The result is a different operating model for transformation.
In high-performing organizations, AI is not treated as a standalone initiative. It is supported by modern platforms, trusted data, aligned leadership teams, scalable delivery models and a culture capable of absorbing change. Business and technology leaders are aligned around common objectives. Internal teams are empowered by automation and augmented by external expertise. Change management is embedded throughout transformation efforts, and governance enables both innovation and control.
In this environment, AI becomes more than a technology investment. It becomes a catalyst for productivity, agility and growth.
As the 2026 Voice of Our Clients insights demonstrate, resilience, AI readiness and execution capacity are increasingly interconnected. Organizations that successfully scale AI will be those that treat talent and execution capacity as a strategic asset and actively invest in expanding it.
You can read more in the second of this three-part series: While AI ambition is accelerating, enterprise readiness is struggling to keep pace.
Five questions to help strengthen execution capacity
For CXOs, strengthening execution capacity requires a deliberate approach to governing transformation partnerships whether through strategic alliances, Global Capability Centers (GCCs), or managed IT services. The objective is not simply to access additional resources, but to establish an agile, long-term foundation for enterprise-wide AI expansion.
While each partnership model requires its own approach, managed IT services often play a foundational role in providing execution capacity at scale.
To ensure these arrangements deliver true strategic value, five questions can guide the evaluation and governance of any managed IT services model:
- Is the partnership aligned to business outcomes, not just service levels?
Establish shared accountability for modernization, productivity, resilience and value realization—not operational performance metrics.- Does the model accelerate modernization while reducing technical debt?
Prioritize partners that can strengthen data and technology foundations while preparing the organization for future AI adoption and growth.- Does the partnership provide scalable access to critical skills and innovation?
Look for access to specialized talent, emerging capabilities and innovation ecosystems that can help close persistent skills gaps.- Is governance designed for transparency, adaptability and trust?
Ensure clear decision rights, outcome-based governance and visibility into performance, risk and business value.- Does the model strengthen organizational capability over time?
The strongest partnerships embed change management, workforce enablement and knowledge transfer, helping organizations build long-term capability rather than dependency.
The next phase of transformation will not be defined by which organizations adopt AI first. It will be defined by which organizations build the capacity to execute at scale.
Those that strengthen governance, partnerships and delivery models will be best positioned to scale AI responsibly, accelerate modernization and move from AI to ROI.
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