How tech companies can build advantage in a shifting AI environment

Technology leaders can shift from AI pilots to value by rethinking models, scaling execution, and capturing return on investment with agentic AI.

In brief

• Technology companies can accelerate growth in 2026 through M&A, partnerships and designing for agentic interoperability across clouds and platforms.
• The adoption of distributed accountability models can play a key role in enabling enterprise-wide AI adoption.
• Linking pricing to outcomes while embracing a flexible approach to model adoption can address customer expectations and accelerate global growth.

As AI adoption accelerates and pressure to deliver measurable outcomes grows, technology companies are focusing on getting real returns from their AI investments by rethinking business models and scaling execution. Technology leaders are no longer asking if AI will transform their business, they are focusing on how to operationalise AI strategies safely and reliably and on capturing value with agentic AI solutions.

In an operating environment that is increasingly nonlinear, accelerated, volatile and interconnected (NAVI), competitive advantage will hinge on organisations embracing maximum flexibility, where autonomous systems handle routine execution while human leaders focus on strategic direction.

Against this backdrop, we have identified 10 opportunities for technology leaders to drive growth, resilience, and trust.

Opportunity 1: Scale faster through M&A and partnerships

A recent EY poll of tech CEOs found that 83% are prioritising joint ventures and alliances in the coming months, up nearly 30% compared to January 2025. Technology companies are forming structured alliances and partnerships to unlock new markets, scale distribution and secure data rights. The EY collaboration with NVIDIA¹ to launch risk management solutions on the EY.ai agentic platform illustrates how alliances can combine domain expertise with advanced AI infrastructure to accelerate innovation and deliver value. Such collaborations not only accelerate growth but also enable access to sectors that were previously restricted due to regulatory or financial barriers.

The market environment is fuelling targeted M&A activity, with large enterprises increasingly interested in start‑ups that possess distinctive data capabilities or offer products and services enabling advanced AI integration. Recent moves by major European industrial players, such as the acquisition of a leading UK‑based applied‑AI specialist by a global consulting giant, and multi‑billion‑dollar purchases of simulation and data‑driven software providers by a prominent continental engineering group, illustrate how aggressively established corporations are expanding their AI footprints.

This pattern reflects a broader continental trend: while much of the venture capital investment continues to flow toward US‑based AI start‑ups, European incumbents are accelerating strategic deals to secure critical capabilities, strengthen digital portfolios, and reduce reliance on external ecosystems. Forward‑thinking leaders will increasingly pursue such partnerships and acquisitions to optimise their portfolios and capture rapidly emerging opportunities before they vanish.

Opportunity 2: Design for agentic interoperability and physical AI

The next frontier for AI innovation is cross-platform and cross-cloud agentic interoperability, enabling products to operate seamlessly across ecosystems while unlocking new automation and orchestration opportunities. Interoperability will become a core design principle as enterprises demand flexibility across multi-cloud environments and heterogeneous IT stacks.

Robotics and other physical AI devices and systems are emerging as key areas for innovation, creating opportunities for differentiated offerings. Autonomous systems and industrial automation when combined with interoperable agent frameworks will be able to span multiple clouds and commercial platforms.

Companies that invest in these capabilities and design for agentic interoperability across clouds and platforms will gain a competitive edge as the convergence of software intelligence and physical execution will become a defining differentiator. The creation of dedicated product groups focused on both advanced AI and physical AI can help to ensure that innovation spans digital and physical domains, enabling faster ideation and execution.

Opportunity 3: Empower function leaders to operationalise safe, reliable AI

Safe, reliable AI has become an operational imperative extending beyond ethics and compliance to the protection of revenue and reputation. With AI permeating every function and moving towards enterprise-wide adoption, governance must be flexible and proactive. This requires the adoption of a distributed accountability model where function leaders are empowered to define guardrails, set clear risk appetites and establish reliability practices for day-to-day operations. Time is of the essence as a recent EY survey indicated less than a third of tech executives have high levels of confidence regarding their responsible AI strategies.

Embedding safe and reliable AI at scale also means institutionalising governance as part of product and operations lifecycles. This will enable rapid experimentation without sacrificing resilience, reliability or trust. Companies that succeed will mitigate regulatory and reputational risk and prevent operational failures that compromise growth and customer outcomes.

Opportunity 4: Rethink pricing strategy for the agentic era

AI-native companies are redefining how software is priced, packaged and purchased. The rise of agentic-mediated buying is transforming customer engagement, as traditional subscription and consumption models give way to secure APIs, instant trials and outcome-based pricing. Customers are starting to expect frictionless experiences and transparent value, not just access or usage.

Outcome-based pricing is emerging as the preferred approach to address changing customer expectations and navigate macroeconomic pressures. The most recent EY – Oxford Economics Global Technology Industry AI Survey found that 89% of tech CEOs are exploring innovative pricing models, including outcome-based pricing. But exploration alone won’t be enough. In 2026, organisations must move to meaningful deployment by tying pricing directly to delivered outcomes and measurable value.

In this shift, those technology companies that possess well‑structured data and can clearly demonstrate outcomes will hold a distinct competitive advantage as they are uniquely positioned to prove value transparently and consistently.

Opportunity 5: Optimise for flexibility in model selection

Organisations are faced with new strategic decisions as they weigh up the trade-offs between the transparency, customisation and lower costs offered by open AI models versus the performance, support, and integrated safety promised by closed models. Making the right choice can become a source of competitive advantage.

The open model ecosystem offers lower barriers to entry, faster iteration, and the potential for deep integration into proprietary workflows, often at a fraction of the cost of the closed alternative. Closed models, meanwhile, continue to set benchmarks for capability and reliability, but may come with higher costs, vendor lock-in, and less flexibility for localisation or compliance in different geographies and jurisdictions.

This is not just a technical debate; it’s also a global business and policy issue. Nor is it a simple either-or choice. In jurisdictions where access to proprietary models or infrastructure is restricted, open models enable broader adoption and innovation. The commercial opportunity lies in adopting a flexible strategy that balances price and performance, avoids single-vendor dependency and aligns with the regulatory requirements of different markets. Organisations that can adopt both open and closed models and deploy them as compliance and other requirements dictate will be well positioned to capture value, manage risk and adapt to changing conditions.

Opportunity 6: Design sovereignty by default and run a borderless talent model

Sovereign and local AI processing is becoming standard as governments around the world tighten data residency and compliance mandates. Countries are asserting control over infrastructure and shaping AI to align with local priorities. While regulations such as the European Union’s Digital Markets Act (DMA), Digital Services Act (DSA) and AI Act are impacting companies’ plans, sovereignty now stretches far beyond compliance. It spans where employees live, where compute happens, and how foundational models reflect national values, morals and traditions.

For technology leaders, sovereignty presents both a technical and organisational challenge. Architectures must embed local jurisdictional controls from the outset. This affects cost and scalability.

It will also force the reimagination of talent strategies. Visa restrictions and other local issues complicate mobility at the same time as innovation demands ever greater global collaboration.

Success in this environment means institutionalising sovereignty-by-default — embedding regional controls into workflows and infrastructure planning while adopting a borderless talent model that leverages distributed engineering pods and regional skills hubs to avoid local restrictions. Companies that integrate diverse regional perspectives and regulatory requirements into their strategy will achieve compliance without sacrificing speed, enabling global scale in an increasingly fragmented landscape.

Opportunity 7: Embed technical expertise to address platform complexity

As AI platforms and ecosystems become more complex, embedding technical talent directly into business units or project teams can accelerate adoption and improve service delivery quality as platforms evolve. Such talent is in short supply, however. According to the EY – Oxford Economics Global Technology Industry AI Survey, 27% of tech executives say a lack of AI skills is the primary barrier to greater implementation across the company, more than any other technical or operational challenge.

Organisations not only overcome the talent challenge but must also weigh the benefits of faster problem-solving and deeper integration against the costs and operational demands of maintaining large cohorts of embedded expertise. The opportunity lies in structuring these embedded technical specialist roles to maximise value helping to ensure they are connected to broader organisational outcomes.

Opportunity 8: Rethink tax strategy for the AI era

As technology companies scale globally, establishing new operations and hiring talent across multiple jurisdictions, the complexity and importance of tax planning have never been higher. As Ireland’s success in attracting global technology leaders demonstrates, tax is no longer just a compliance function but also a strategic enabler that can unlock capital and protect margins in a rapidly changing environment.

Tax must be considered proactively when making decisions about where to locate new operations, how to structure IP ownership and how to allocate costs and profits across borders. Given the nexus rules, the tax strategy will be heavily influenced by the talent strategy. Key decision makers and R&D functions need to be in the IP location at least as the hub matures. The right tax approach can influence everything from data centre location and cloud expansion to the monetisation of digital IP and the structuring of global AI teams.

Leading organisations are embedding tax analytics into their core data platforms, using real-time insights to inform business decisions, manage risk and enhance transparency with stakeholders and regulators. This enables proactive management of incentives, credits and compliance obligations, transforming tax from a cost centre into a source of value and resilience. The opportunity lies in building tax strategy into the foundation of digital transformation, so as the business grows, it does so with a clear view of global risk and opportunity.

Opportunity 9: Make finance the ROI engine for AI

The critical role played by the finance function makes it the ideal proving ground for return on investment on AI spend. Companies are directing significant investment into AI for finance, but the returns have yet to materialise. That must change. Finance leaders need to embed AI into forecasting, use it to automate compliance and employ predictive analytics to drive smarter decisions.

This means making AI core to financial operations, using it for real-time visibility of cash flows, dynamic scenario modelling, automated compliance checks and intelligent resource allocation. Done right, this can see finance evolving from a reporting function into a strategic engine that drives margin growth, optimises capital deployment and enables faster, smarter decisions across the entire enterprise.

Opportunity 10: Redefine enterprise security for AI, identity and nation-state threats

AI is now both a force multiplier for cyber attackers and a critical defensive tool for organisations. AI-enabled bad actors are accelerating the tempo, scale and sophistication of attacks. At the same time, increased regulatory scrutiny and rising customer trust demands are driving new board-level imperatives. Companies are responding accordingly and our recent global survey found that tech executives expect to more than double their cybersecurity spend over the next two years.

Technology companies must move to more proactive, AI-powered cyber defence and identity assurance. AI systems must be secured against prompt injection, data poisoning, and other threats. As AI becomes the backbone of critical infrastructure, the cost of failure is potentially catastrophic and includes operational outages, regulatory penalties and reputational damage cascading across markets and sectors.

The opportunity is to adopt integrated security platforms that unify endpoint, cloud, identity and data protection leveraging AI for both defence and governance. In a world where AI is weaponised by adversaries, cybersecurity and identity are no longer IT functions. They are strategic enablers of growth, trust, and market access.