AI-native cloud providers redefine the cloud landscape
AI is forcing a hard reset on how we think about the cloud. The traditional model, where hyperscale providers dominate with generic services, is starting to show its limits. By 2026, AI-native “neocloud” providers are expected to generate $20 billion in revenue. These aren’t just startups trying to compete on cost. They’re building cloud platforms specifically designed for generative AI, signal processing, and intelligent decision-making.
What’s happening here isn’t a side shift. It’s core architecture being rewritten. Neoclouds are gaining momentum fast because they deliver tighter control over AI workloads, faster model tuning, and secure handling of proprietary data, something enterprises are starting to demand more aggressively. The hyperscalers, still dominant today, aren’t standing still. They’re responding with their own AI capabilities, agentic systems that can manage complex requests with minimal oversight.
But there’s a balancing act. The switch to AI-native infrastructure puts pressure on legacy cloud hardware. Expect some stress on data centers, Forrester is forecasting at least two major multiday outages in 2026 because of this. These aren’t just technical hiccups. They’re warning signs for anyone running mission-critical systems on aging cloud infrastructure. Enterprises that haven’t yet modernized will be playing catch-up.
Forrester expects at least 15% of enterprises to pursue private AI, AI built and hosted with internal control, not public-cloud dependence. That’s not paranoia. It’s smart data governance and positioning. Decision-makers should read that carefully: privacy, sovereignty, and infrastructure independence are joining performance and cost as board-level cloud priorities.
Lee Sustar, Principal Analyst at Forrester, summed this up clearly: “The rise of the AI-native cloud is driving massive business investments and remaking enterprise IT.” It’s a shift in spending, architecture, and strategy all at once. Smart companies won’t wait until 2026 to course-correct.
Enterprise software evolves with digital workers and increased automation
Most enterprise software still assumes humans are doing the thinking. That’s about to change. In 2026, enterprise apps will start managing more tasks independently, without the need for human initiation or review. Digital workers, essentially autonomous AI agents embedded within the software, will handle workflows, policy checks, and exception management directly inside platforms like ERP and HR systems.
This isn’t sci-fi. It’s happening incrementally. Forrester projects that more than 20% of enterprise application workflows will be automated with AI by 2026. At the same time, the top five human capital management (HCM) platforms will include features that allow digital oversight of humans. Think attendance, performance tracking, scheduling, all managed or assisted by AI agents instead of human supervisors. Half of ERP vendors will also offer autonomous governance modules, creating software that self-regulates processes using defined goals and ethical guardrails.
The focus here isn’t to replace people; it’s to free them from the endless administration that slows businesses down. This new layer of digital workforce integration allows leaner operating models and tighter information flows. But C-suite leaders need to think bigger than deployment. This is about restructuring how the business operates, because when your software can think, act, and verify independently, your team needs to work differently too.
Linda Ivy-Rosser, Vice President and Research Director at Forrester, captured the mindset shift well: “Enterprise apps will move beyond enabling employees with digital tools, to fully embracing and accommodating a digital workforce.” That’s not a UI upgrade. It’s a rewrite of workforce operations. And leaders who adapt early are going to set the performance baseline everyone else is chasing.
AI dominates software development and reshapes the SDLC
AI is about to completely take over how enterprise software gets built. Forrester forecasts that in 2026, software development will be the number one use case for AI across the enterprise. That’s not because AI helps write better code, it’s because it drives speed, context, and scale across the entire software development lifecycle (SDLC).
This starts with what Forrester calls “vibe coding.” Developers will use natural language and contextual cues to guide code production, think describing intent and letting the AI generate full modules. But it doesn’t stop there. By end of 2026, this technique will transition into “vibe engineering”—where the same principles are applied across testing, deployment, documentation, and debugging. AI won’t be an assistant anymore. It will be integrated into each phase of product building, delivering end-to-end automation with just-in-time optimization.
That brings both efficiency and complexity. The demand for AI-based development is creating a tight labor market. Forrester predicts that it will take twice as long to fill developer roles as it does today. The skillset is moving fast. Developers need to understand how to coach AI models, evaluate AI-generated code, and manage hybrid workflows where people and intelligent systems collaborate.
Diego Lo Giudice, VP and Principal Analyst at Forrester, said it best: “We will see the rise of engineering governance and the deeper integration of AI across the entire software development lifecycle, not just coding.” This isn’t about replacing engineers. It’s about augmenting every part of the development environment with layered intelligence that reacts, learns, and scales, faster than conventional processes ever could.
For C-suite leaders, this is the time to invest in AI-native development platforms, retraining programs, and product teams designed around continuous learning. Companies that get this right won’t just build faster, they’ll build software that adapts in real time, creating real business advantages.
IT infrastructure faces disruption amid accelerated AI demands
As enterprise AI matures, IT infrastructure is going to get tested, hard. It’s already happening. More enterprises are running AI workloads that push computing, storage, and network performance to new thresholds. Forrester sees a structural shift: organisations are moving 50% of server loads back on-premises, investing in tailored infrastructure to support AI scalability, data control, and latency-sensitive applications.
This reflects a broader concern where CIOs and infrastructure leaders are being asked to deliver both resilience and precision at scale. Public cloud still has a role, but it’s no longer the default for high-performance AI. That’s why “private AI factories”—dedicated environments for internal AI development, are set to reach 20% adoption among enterprises by 2026, according to Forrester.
Even with stronger local hardware, risks persist. AI systems come with inherently unpredictable behaviors when deployed in production. Forrester is forecasting that at least one major IT outage in 2026 will be autonomously prevented by an agentic AI system, an AI that detects anomalies in real time and takes corrective action without human input. That’s the sort of autonomy that infrastructure needs to handle growing volumes of distributed decision-making.
Security is also shifting. AI workloads require tighter integration between network and security tools, which is why Forrester expects half of current security tool vendors to move into the secure LAN space. That’s a clear indication of where market demand is heading, towards integrated, AI-aware environments that are secure by design, not bolt-ons.
Michele Pelino, VP and Principal Analyst at Forrester, pointed out: “In 2026, we will see significant disruption driven by accelerated appetite for all things AI.” Leaders need to be ahead of that curve. Infrastructure investments, data architecture, and vendor strategies must now be filtered through one clear lens: how do they accelerate the business’s ability to train and operate AI without failure, friction, or fatigue among engineers? If it can’t scale AI, it’s outdated.
Automation and robotics embrace AI-driven orchestration
By 2026, automation and robotics will shift from narrowly defined tasks to more adaptive and AI-directed execution models. Forrester sees this market converging: robotic process automation (RPA), integration platforms (iPaaS), and business process management (BPM) are merging into unified systems powered by AI. These platforms can orchestrate decisions and actions across business units, not just repeat script-based instructions.
Strategic use of robotics is expected to drive 20% of new enterprise use cases by 2026. That includes embedded robotics in warehouses, AI-led fulfillment systems, and context-aware automation inside ERP or CRM platforms. But uptake of full agentic functionality, where AI systems initiate and adapt operations independently, will remain cautious. Fewer than 15% of enterprises will activate these capabilities. It reflects a clear mindset: corporations want transformation, but not at the cost of reliability or control.
Many enterprises are also facing failed or stalled AI projects inside automation efforts. That’s where process intelligence comes in, tools that observe automation engines and intervene when workflows break down. Forrester expects these tools to salvage about 30% of failed AI deployments by identifying bottlenecks, correcting misalignments in logic, and optimizing performance. They operate almost like internal feedback systems that strengthen the resilience of AI over time.
The direction here is clear: enterprises are shifting from deterministic automation to adaptive models. Leslie Joseph, Principal Analyst at Forrester, explains this market shift well: “The enterprise automation space is moving toward adaptive, AI-driven workflows, as the focus shifts from deterministic to cognitive autonomy.” That means platforms must learn and adjust, while still operating within practical governance frameworks.
C-suite leaders should view this as an opportunity to push their operating models forward. The risk in waiting isn’t about losing to a competitor right away, it’s about falling into tech debt that takes years to unwind. Adaptive automation will become the baseline, and leaders who embrace structured experimentation with tight controls will gain execution speed without trading off auditability or consistency.
Key takeaways for leaders
- AI-native clouds are reshaping cloud investment: Enterprises should begin shifting select workloads to AI-native cloud providers to stay competitive, as Forrester projects $20B in revenue for these providers by 2026 and growing demand for private AI to safeguard strategic data.
- Digital workers will redefine enterprise software functionality: Leaders should evaluate enterprise platforms for AI integration potential, as over 20% of workflows will be automated and top-tier HCM and ERP platforms will embed autonomous governance and digital employee management features by 2026.
- AI will lead software development innovation: Companies must invest in AI-enhanced development environments and retraining programs now, as AI becomes the top enterprise use case and hiring timelines for developers double due to skill shifts toward supporting “vibe engineering” and AI-assisted SDLCs.
- Infrastructure and security models must adapt to AI demands: CIOs should prioritize hybrid architectures and AI-resilient infrastructure, with on-premise servers expected to hit 50% market share and AI agents expected to autonomously prevent critical disruptions by 2026.
- Automation will shift toward AI-led orchestration: Executives should adopt a cautious but proactive approach to agentic automation, ensuring governance is in place as AI drives smarter workflows and rescues 30% of failing AI projects through process intelligence tools.
