2026 Technology Predictions: The Top 9 Trends Shaping the Year Ahead

For many organizations, AI has remained safely in experimental mode until now. In 2026, that all changes. AI starts behaving like infrastructure: embedded in workflows, integrated into core systems, and expected to deliver predictable results. Agents won’t just answer questions, they’ll take actions across platforms. Cloud won’t just host workloads, it will increasingly optimize them. And security won’t live at the end of a project, it will be designed into every workflow AI touches.

This year, prepare for the gap to widen quickly: organizations with modern platforms, governed data, and measurable delivery will see faster ROI. Everyone else will be stuck running experiments they can’t operationalize.

Here are SPR’s top 9 technology predictions for the year ahead.

The decade of agentic AI begins

This past year was widely called the year of agentic AI. However, many organizations found that implementing agentic systems effectively within real enterprise environments is challenging. Agents require secure access to tools and data, clear guardrails, human oversight, and the integrations and operational discipline needed to make those controls effective, not just aspirational.

In 2026, the focus shifts from “Is agentic AI real?” to “How do we build it incrementally over time?” As Matt Mead, CTO at SPR says, “The more realistic view is that agentic AI unfolds over a decade, not a year.”

What this means for leaders: Invest in agentic AI as a multi-year capability and begin with narrowly scoped workflows that you can govern and measure.

Agentic AI moves from experiments to enterprise workflows

Siloed experimentation was the right move for many teams in 2025. This year, leading organizations will integrate pilots into production workflows: agents that can perform bounded tasks, coordinate across systems, and advance work while humans retain control of high-impact decisions.

The key differentiator won’t be the model alone. It will include orchestration, permissions, identity, auditability, and operational safety, enabling agents to operate in complex environments without unacceptable risk.

What this means for leaders: Move beyond pilots by investing in agent “plumbing” (tool access, approvals, logging, rollback) so experiments can evolve into reliable operations.

AI governance, security and ROI discipline become non-negotiable

As AI enters business-critical systems, governance shifts from a technical matter to a leadership concern. “That naturally elevates AI governance and oversight to executive and board-level conversations,” says Mead, “because accountability, risk, and trust directly influence revenue, compliance, and reputation.”

At the same time, spending is shifting from experimentation to results. “After several years of heavy AI investment, many CFOs are growing impatient,” Mead adds. In 2026, the initiatives that succeed will be those with clear success metrics, defined ownership, and governance that enables safe scale rather than slowing everything down.

And critically, governance is inseparable from security and compliance by design. “Zero-trust frameworks, automated compliance, and AI-driven threat detection will be standard with security woven into CI/CD pipelines and runtime environments,” notes Geremy Reiner, Cloud Specialty Director at SPR.

What this means for leaders: Define measurable outcomes and assign accountable owners upfront. Put governance (controls + auditability) in place before scaling AI into critical workflows.

Technology modernization becomes the prerequisite for AI at scale

AI adoption is exposing foundational gaps: fragile platforms, poor integration, and ungoverned or inaccessible data. In 2026, modernization speeds up because it becomes the key layer enabling true AI adoption, particularly across cloud infrastructure, data platforms, APIs, and compliance.

“Organizations that invest early in modernization will gain a competitive edge by enabling faster AI innovation, better customer experiences, and lower operational costs, while laggards risk being left behind in an increasingly AI-driven market,” says Melissa McElroy, VP of Solution Delivery at SPR.

What this means for leaders: Prioritize modernization efforts that enhance AI speed and safety, such as API enablement, data governance, platform resiliency, and security foundations.

Data and context become the advantage; agentic threats increase

By 2026, many organizations will have exhausted the “easy” AI wins. The next wave of impact depends on data quality and context: how knowledge is captured, governed, and made usable across systems and teams.

“Now, organizations have exhausted the ‘low hanging fruit’ and are now facing larger institutional hurdles to deploying truly impactful AI systems: their own fragmented and mismanaged data,” says Josephine Wood, Data & AI Specialty Director at SPR. At the same time, risk rises as attackers adopt AI-enabled automation too: “we’ve begun to see an uptick in adverse cybersecurity events leveraging AI agents,” Wood notes.

What this means for leaders: Treat trusted data pipelines and security controls as AI accelerators. Invest in governance, permissions, monitoring, and incident readiness as AI expands.

Cloud consolidation accelerates, while the cloud becomes self-optimizing

AI-driven infrastructure demand is reshaping cloud economics and capacity. In 2026, the market continues to consolidate, while specialized providers emerge to address performance and compute needs that traditional models can’t always meet. “The cloud market will see consolidation, with a growing number of ‘neocloud’ providers emerging,” says Reiner.

At the same time, cloud platforms are becoming more intelligent: “Cloud platforms will evolve into intelligent, self-optimizing systems capable of predictive scaling, automated governance, and real-time resource allocation,” Reiner adds, shifting cloud from a passive platform into an active partner for optimizing cost, security, and performance.

What this means for leaders: Prioritize resilience and optionality. Assess portability, capacity risk, and cost controls just as carefully as features when making cloud decisions.

Smaller software teams deliver more output, and requirements become the bottleneck

AI-assisted development continues to boost engineering throughput, from scaffolding and test generation to accelerating portions of implementation. But as delivery speeds up, the next constraint becomes obvious: unclear requirements and fuzzy definitions of success.

“Most of these tools are spec-driven, which means they rely heavily on clear, detailed requirements to work well,” says Mike Saccotelli, Senior Director, Solution Delivery. In 2026, organizations that combine AI-enabled engineering with strong discovery, well-defined acceptance criteria, and measurable outcomes will see increasing gains.

What this means for leaders: Strengthen requirements discipline (discovery, acceptance criteria, success metrics) so AI-driven delivery gains lead to the right outcomes.

Legacy systems finally get AI, without full rewrites

For many enterprises, “rip and replace” is unrealistic. In 2026, more organizations will add AI capabilities without complete rebuilds by layering modern services around stable systems, augmenting legacy platforms with automation, decision support, and smarter workflows.

This approach unlocks value while preserving operational reliability and establishes a pragmatic modernization path that can evolve over time.

What this means for leaders: Look for “wrap and evolve” opportunities that integrate AI-enabled services at the edges while safeguarding stable cores.

AI becomes invisible, shifting from feature to experience

The initial wave of AI adoption often appeared as add-on features: a chatbot here, an “assistant” there. In 2026, organizations will increasingly use AI to enhance workflows without making it the main focus, including streamlining steps, boosting decisions, and reducing manual work.

While those experiments were useful, many organizations learned that attaching AI to a product does not automatically create value,” says Mead. The winners will redesign experiences around outcomes (not novelty) so AI becomes a quiet, consistent force-multiplier across the business.

What this means for leaders: Prioritize AI initiatives that remove friction from real workflows and demonstrate impact in cycle time, quality, cost, or risk reduction.

Taken together, these trends point to a clear reality: 2026 is the year AI grows up. The organizations that succeed will go beyond pilots and novelty by investing in modernization, trusted data, resilient cloud strategies, and governance that makes AI safe to scale. This is done all while recognizing that security, compliance, and measurable outcomes are now part of the baseline, not optional add-ons.

The goal now isn’t to “do AI.” It’s to build enterprise capabilities that improve outcomes, reduce risk, and make teams more effective as AI becomes integrated across products and operations.