Top 10 opportunities for technology companies in 2026

Achieving ROI through an accelerating investment cycle.

In brief

• Technology companies can accelerate growth in 2026 through M&A, joint ventures and interoperable AI products that incorporate physical AI to drive innovation.
• Embedding safe AI practices, adopting sovereignty-by-default principles and deploying technical specialists to scale adoption and strengthen governance.
• Transforming business models with outcome-based pricing and optimizing AI strategies across business functions to drive sustainable value creation.

The technology industry enters 2026 amid accelerating artificial intelligence (AI) adoption, geopolitical complexity and mounting pressure to deliver measurable outcomes. Tech companies are focused on proving the ROI of AI, rethinking business models and driving a mandate to execute at scale. Executives are no longer asking whether AI will transform their business — they are asking how to operationalize AI-native strategies with safe and reliable practices, and capture value with agentic-driven solutions. 

This urgency is supercharged by an operating environment that is increasingly nonlinear, accelerated, volatile and interconnected (NAVI). Competitive advantage will hinge on enterprises embracing superfluidity, where autonomous systems handle routine execution while human leaders focus on strategic direction. Technologies such as autonomous AI, Web3, and smart contracts can further eliminate friction and enable real-time, adaptive governance.

Against this backdrop, the following opportunities represent actions for technology leaders to drive growth, resilience, and trust in this rapidly shifting environment.

Opportunity 1

Scale faster with M&A and joint venture ecosystem plays in this hyper‑velocity AI moment

Velocity will be the defining factor of success in 2026. The lightning-fast pace of AI innovation makes it the top priority for how companies scale and capture advantage. Organizations that move fast – without sacrificing interoperability or governance — will be best positioned to seize winner-take-most scenarios.

In today’s data-driven economy, technology companies are forming structured alliances and outcome-focused partnerships to unlock new markets, scale distribution and codify data rights. The EY collaboration with NVIDIA to launch risk management solutions on the EY.ai agentic platform illustrates how alliances can combine domain insights with advanced AI infrastructure to accelerate innovation and deliver differentiated value. Such collaborations not only accelerate growth but also enable access to sectors that were previously restricted due to regulatory or financial barriers. A recent EY poll of tech CEOs found that 83% are prioritizing joint ventures and alliances in the coming months – up nearly 30% compared to January 2025 – reflecting the urgency to act quickly.¹

At the same time, the market environment is fueling targeted M&A activity — with large companies especially interested in startups with assets to enable advanced AI integration or unique data capabilities. Forward-thinking leaders will take an all-of-the-above approach, seeking out partnerships and acquisitions that can optimize portfolios and help capture rapidly emerging opportunities before they vanish.

Success in this landscape requires more than opportunistic deals. Organizations must prioritize interoperability and transparent outcome-sharing mechanisms from the outset. By embedding governance and aligning incentives across ecosystems and partnerships, with tech companies can build resilient systems that deliver differentiated value. This will enable the agility to adapt at speed amid evolving regulatory and technical demands.

Opportunity 2

Design for agentic interoperability and physical AI

AI embedded in products is no longer a differentiator. The next frontier is cross-platform and cross-cloud agentic interoperability, enabling products to operate seamlessly across ecosystems while unlocking new automation and orchestration layers. This shift positions interoperability as a core design principle as enterprises demand flexibility across multi-cloud environments and heterogeneous stacks.

With critical momentum, physical AI and robotics are emerging as essential frontiers for innovation, moving closer to edge environments and 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 will gain a competitive edge as the convergence of software intelligence and physical execution will become a defining differentiator.

Multi-cloud AI is breaking exclusivity models and creating demand for orchestration as well as GPU-capacity brokering. This environment favors companies that design for agentic interoperability across clouds and platforms. 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 functional leaders to operationalize safe, reliable AI — especially as deployments mature and scale

As observability improves, safe, reliable AI has become an operational imperative — extending beyond ethics and compliance to protecting both revenue and reputation. With AI permeating every function and scaling from early proofs of concept to enterprise-wide adoption, governance must be flexible and proactive. Organizations are moving toward distributed accountability by empowering functional leaders – those closest to workflows and risks — to define guardrails, set clear risk appetites and establish reliability practices into day-to-day operations. Without robust frameworks, companies risk fragmented practices resulting in cascading failures and decisions that derail business objectives. 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 ethics and responsible AI strategy. 

Embedding safe and reliable AI at scale means institutionalizing governance as part of product and operations lifecycles. Critically, data readiness emerged as the most underprepared dimension – requiring robust lineage, quality checks, and governance to mitigate systemic breakdowns as AI scales. Tools need to keep pace as leaders enforce standards, including areas such as data quality and lineage, policy as code, bias and drift monitoring, incident playbooks, and auditable rollbacks. Finding the right balance enables 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 commercial 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.

Business model innovation is accelerating. In fact, 89% of tech CEOs surveyed say they are exploring innovative pricing models such as outcome-based pricing.² But exploration alone won’t be enough. In 2026, leaders must move from pilots to meaningful deployment — tying pricing directly to delivered outcomes and measurable value. Enabled by AI’s ability to eliminate adjacent services and activities, outcome-based pricing is emerging as the preferred approach to address changing customer expectations and navigate macroeconomic pressures. These outcome-based models aim to create a “no regret” buying experience for customers.

Simultaneously, AI tools are being deployed across business functions — sales, service, support and financing — creating opportunities for integrated offerings and new value propositions. By embedding generative AI (GenAI) and agentic applications, companies automate and enhance these functions, enabling bundled solutions that combine products, services, and financing into frictionless experiences. This evolution is giving rise to Service as Software, where traditionally human-driven interactions are delivered through intelligent, automated platforms. Success will depend on designing for agent-driven commerce by, for example, exposing product and pricing through secure APIs and aligning commercial models with outcome-based metrics. Interoperability will be a critical success factor as companies work to deliver seamless, end-to-end workflows across platforms and clouds — introducing challenges that are as much commercial as they are technical.

Opportunity 5

Optimize for flexibility in model selection

The wealth of options between open and closed AI models is creating new strategic decisions for tech companies. As organizations weigh the trade-offs between transparency, customization and cost (open) versus performance, support, and integrated safety (closed), the ability to navigate this divide becomes a source of competitive advantage. The open model ecosystem is rapidly maturing, offering lower barriers to entry, faster iteration, and the potential for deep integration into proprietary workflows – often at a fraction of the cost. Closed models, meanwhile, continue to set benchmarks for raw capability and reliability, but may introduce higher costs, vendor lock-in, and less flexibility for localization or compliance.

This evolving landscape is not just a technical debate — it’s also a global business and policy issue. In regions where access to proprietary models or infrastructure is restricted, open approaches are enabling broader adoption and innovation. For enterprises, the opportunity lies in promoting a flexible strategy that balances price and performance, avoids single-vendor dependency and aligns with evolving regulatory and sovereignty requirements. Organizations that can orchestrate both open and closed models — optimizing for workload, geography and compliance — will be well positioned to capture value, manage risk and adapt as the AI stack continues to fragment.

Opportunity 6

Design sovereignty by default and run a borderless talent model

Regulatory fragmentation and geopolitical uncertainty are redefining operating models. Sovereign and local AI processing is becoming standard as governments tighten data residency and compliance mandates. 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 goes far beyond compliance. It spans where talent resides, where compute happens, and how foundational models reflect national values, morals and traditions. Countries and cultures are asserting control over infrastructure and shaping AI to align with local priorities.

For technology leaders, sovereignty is both a technical and organizational challenge. Architectures must embed jurisdictional controls from the outset, balancing performance with compliance across regions. These decisions influence cost, latency and scalability and they force modernization of talent strategies toward borderless pods and regional skill hubs. Yet confidence remains low. Only 47% of global tech executives believe their AI strategy effectively enables talent across the organization, underscoring how workforce models are lagging behind the pace of technological and geopolitical change.³ Visa restrictions and local mandates complicate mobility even as innovation demands global collaboration, creating dual pressures on infrastructure and talent agility.

Winning in this environment means institutionalizing sovereignty-by-default — embedding regional controls into workflows and infrastructure planning while adopting a borderless talent model that leverages distributed engineering pods and proactive risk management. Companies that integrate diverse regional perspectives and regulatory requirements into strategy will achieve compliance without sacrificing speed, enabling global scale in an increasingly fragmented landscape.

Opportunity 7

Deploy embedded technical specialists to navigate AI platform complexity 

As AI platforms and ecosystems become more complex and specialized, organizations face a critical question: Will the use of forward-deployed engineers (FDEs) or other embedded technical specialists become essential for success? The answer has broad implications. Embedding technical talent directly into business units or project teams can accelerate adoption, improve service delivery quality and help provide continuity as platforms evolve. However, it also raises new challenges around talent acquisition and retention, financial investment, scaling and the measurement of impact through clear KPIs. Currently, 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.⁴

Organizations must weigh the benefits of faster problem-solving and deeper integration against the costs and operational demands of maintaining large organizations of embedded expertise. The opportunity lies in structuring these roles to maximize value — helping to ensure technical specialists are connected to broader organizational learning, governance and outcome measurement.

Opportunity 8

Rethink your tax strategy for the digital infrastructure and AI era

The unprecedented scale and speed of investment in digital infrastructure — compute, data centers and the rapid development of proprietary AI models and tools — has elevated tax strategy to become a critical lever for technology companies. As AI leaders scale globally, ramping up operations and hiring talent across multiple jurisdictions, the complexity and stakes of tax planning have never been higher. The rationale is clear: Tax is no longer just a compliance function but also a strategic enabler that can unlock capital, accelerate deployment and protect margins in a rapidly changing environment.

For technology companies, tax must be considered proactively — not as an afterthought — when making decisions about where to build, how to structure IP ownership and how to allocate costs and profits across borders. The right tax approach can influence everything from data center origination and cloud expansion to the monetization of digital IP and the structuring of global AI teams.

Tax is evolving into a data product in its own right. Leading organizations 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 shift enables proactive management of incentives, credits and compliance obligations, transforming tax from a cost center 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 agility, compliance and a clear view of global risk and opportunity.

Opportunity 9

Institutionalize AI FinOps to make finance the ROI engine

Coding and customer care have already demonstrated AI’s ability to transform enterprises. Finance is next — and it must become the proving ground for ROI. This domain sits at the center of critical processes, control and risk, making it the ideal next wave for meaningful AI-driven outcomes.

Companies are directing significant investment into AI for finance, but the returns have yet to materialize. Today, 51% of global tech companies rank Finance among the top three budget priorities for AI — behind only IT/digital and core business functions — yet just 27% say finance is among the top three drivers of AI ROI.⁵ This gap highlights the challenge: Well-resourced pilots and exploratory use cases have not translated into enterprise-scale value. In 2026, that must change. Finance leaders need to move from experimentation to deployment — embedding AI into forecasting, accelerating close cycles, automating compliance and using predictive analytics to drive smarter decisions.

Institutionalizing AI FinOps means making AI core to financial operations — adding real-time visibility into cash flow, dynamic scenario modeling, automated compliance checks and intelligent resource allocation. Done right, finance evolves from a reporting function into a strategic engine that drives margin expansion, optimizes capital deployment and enables faster, smarter decisions across the enterprise.

Summary

2026 is about responsibly executing AI at speed and at scale. The opportunities outlined here reflect a shift from experimentation to operational maturity and resiliency. While challenges persist, ranging from high energy consumption to legal challenges, to continued AI hallucinations, the pace and size of opportunity continues to increase. Technology companies that embrace these AI-native strategies, enable transformative governance, and rethink commercial models will lead in this next era.