Why tech leaders are doubling down on AI and resilience

Technology services spending is a core and non-discretionary IT budget component

Technology services have moved from being optional to essential. The latest Bain Tech Services Decision Makers Survey (January 2026, n=280) shows that overall IT budgets will stay flat year over year, but investments in tech services will keep growing. Companies now treat these services as foundational to operations, not as temporary solutions. The shift signals that technology has become part of the core infrastructure driving growth, efficiency, and resilience.

This trend is consistent across most industries, with only a few, consumer packaged goods, B2B manufacturing, and property and casualty insurance, tracking slower growth. What’s changing isn’t just spending volume but mindset. Tech services are no longer viewed as side projects. They’re integrated into long-term strategies that define how organizations build value and maintain competitiveness.

For executives, the message is clear: technology partnerships need to offer long-term scalability and dependable delivery. Business leaders should focus on aligning with partners capable of scaling fast, innovating efficiently, and maintaining high reliability. Those partnerships are becoming as critical as any investment in infrastructure, as companies depend on them to sustain performance through uncertainty.

Executives should view this as a strategic rebalancing of budgets, away from isolated projects and toward continuous digital capability. This will be the deciding factor in how efficiently organizations adapt to rapid technological change.

AI and machine learning are integral to enterprise transformation and are no longer viewed as experimental

AI is no longer a test run within organizations; it’s now the backbone of enterprise transformation. Bain’s 2026 survey shows that 75% of executives expect at least 5–10% of their technology budgets to go toward AI and machine learning, with some industries, like retail, institutional banking, and oil and gas, planning to invest more than 20%. These numbers underline a decisive shift: leaders are no longer asking whether AI is relevant. They’re asking how fast it can deliver results.

Across industries, AI and machine learning are being built directly into operations, product design, and customer interaction. What was once considered discretionary innovation is now a core performance engine. CIOs and CEOs see AI as an essential part of scaling productivity and improving decision-making.

This transformation requires more than money. It demands rethinking infrastructure, governance, and workforce skills. Executives must treat AI integration as a long-term business redesign process. That means building internal capabilities around data, automation, and predictive analytics, while ensuring cross-departmental alignment so that transformation doesn’t stall at the operational level.

For leaders, AI’s acceleration offers new leverage for driving productivity and profitability, but only if investment is matched with clear goals, strong governance, and cultural readiness. The companies that treat AI as central to their future will define the competitive standards for the decade ahead.

Companies expect significant productivity gains and cost efficiencies from AI investments

Executives are no longer content with testing AI’s potential, they expect clear, measurable results. The Bain Tech Services Decision Makers Survey (January 2026, n=280) shows a mindset shift from exploration to execution. Businesses want AI to deliver improved developer productivity, faster testing cycles, and automated customer support. These aren’t future ambitions; they’re near-term expectations driving how technology budgets are structured.

Clients are pushing for double-digit productivity gains. This demand is reshaping the relationship between enterprises and tech service providers. Traditional full-time equivalent (FTE)-based billing models are losing ground to outcome-based pricing, where value is tied directly to efficiency and business results. Providers that can’t deliver faster timelines or cost benefits will face pressure to adapt.

For decision-makers, this shift means redefining how success is measured. Productivity goals linked to AI adoption must be specific, shorter delivery times, higher automation rates, or better output per developer. With that clarity, AI becomes a lever for financial performance, not just a technical enhancement.

AI also creates new margin opportunities for tech services firms. By automating delivery and reusing digital assets, they can expand capacity without scaling labor costs at the same rate. Over time, this raises both customer value and profit potential. Business leaders should see this as an ecosystem change: AI is strengthening both sides of the service equation, improving delivery and opening pathways for growth.

AI and cybersecurity are prioritized as critical, integrated elements of strategic technology initiatives

Across industries, priorities are converging around two central capabilities, artificial intelligence and cybersecurity. According to Bain’s 2026 findings, these have become the top focus areas in enterprise technology agendas. Companies no longer treat them as separate initiatives; they are now tightly connected parts of a single modernization effort.

AI serves as more than an automation tool, it now sits across enterprise functions, linking data modernization, process transformation, and security management. At the same time, every AI-led initiative brings new security challenges, making cybersecurity an equal priority. Modern programs are being built from the ground up with AI readiness and data protection at their core.

This shift away from incremental upgrades toward integrated transformation demands that technology service providers present cohesive solutions. Fragmented capabilities no longer meet expectations. Clients want unified delivery across cloud, data, AI, and security, with clear accountability and measurable business outcomes.

For executives, the message is direct: AI and cybersecurity strategies cannot be handled in isolation. They must operate as part of a synchronized, company-wide framework that strengthens trust, scalability, and performance. Firms that approach transformation in this way will lead. Those that don’t will find it difficult to compete in a landscape where resilience and intelligence define long-term success.

A shortage of advanced digital skills reinforces companies’ reliance on tech services firms

The growing demand for digital transformation is outpacing the availability of talent. The Bain Tech Services Decision Makers Survey (January 2026, n=280) reveals that executives across industries identify cybersecurity, AI and ML engineering, and data science as the hardest skills to source. Additional gaps exist in areas like application programming interface (API) design, full-stack cloud-native development, and cloud modernization. This shortage is no longer a temporary challenge, it is a structural constraint affecting how organizations execute strategy.

As companies move faster toward AI-driven operations, the supply of qualified talent cannot keep up. This shortfall increases reliance on technology services firms that can fill immediate capability gaps and deliver at scale. For executives, this dependency goes beyond simple outsourcing, it is a partnership model where external expertise accelerates market entry, improves performance consistency, and supports continuous modernization without waiting for internal hiring cycles.

To sustain transformation, C-suite leaders must align talent acquisition and partner strategy. Internal capability building remains critical, but it should run in parallel with partnerships that bring specialized technical depth. The most effective tech service firms combine technical excellence with strategic advisory skills, helping clients not only implement solutions but also build a roadmap for developing their own long-term expertise.

Executives should treat talent strategy as a core business priority. The competitive edge now depends on combining in-house talent with trusted external capabilities. The organizations that manage this balance effectively will move faster, operate more efficiently, and adapt more easily to disruptive technologies reshaping every industry.

Key highlights

• Tech services are now essential investments: Technology services have become a fixed part of IT budgets, driving resilience and operational efficiency. Leaders should treat these as long-term strategic assets rather than discretionary costs.
• AI and machine learning are core transformation drivers: AI has moved from experimentation to execution, with most companies allocating 5–20% of their tech budgets to it. Executives should embed AI into enterprise operations to strengthen productivity and competitiveness.
• Measurable outcomes define AI success: Businesses expect tangible results such as faster delivery, lower costs, and higher developer productivity. Leaders should adopt outcome-based partnerships and pricing models to link performance directly to business value.
• AI and cybersecurity must evolve together: Enterprise priorities now center on integrated transformation that unites AI, cybersecurity, cloud, and data modernization. Decision-makers should align these capabilities under one roadmap to maximize efficiency and resilience.
• Talent gaps are fueling reliance on tech services firms: Shortages in AI, cybersecurity, and data science are driving companies to depend on external partners. Executives should balance internal capability building with strategic partnerships to ensure sustained innovation and operational agility.