Autonomous workflows
Software is evolving fast, fast enough that many legacy systems are starting to feel like dead weight. We’re moving toward a new phase of enterprise automation where AI doesn’t just suggest actions or auto-complete emails. It runs entire workflows. From sales to back-end operations, AI is beginning to handle the entire process with near-zero human oversight. In 2026, that shift will become obvious. You’ll see fewer “nice-to-have” AI features and more end-to-end autonomous systems doing real work.
This isn’t about slapping on a chatbot or integrating a language model into your CRM. It’s about core re-architecture. AI-native startups are building differently. Their systems are structured from day one to automate and run at scale without relying on human-centric processes. The result: they’re moving faster, onboarding clients quicker, and offering leaner, more adaptive solutions that are hard for incumbents to match. If your current systems weren’t built with AI in their foundation, catching up will be expensive and slow. And yes, that’s a genuine business risk.
The market is already rewarding companies that don’t just use AI, they are AI. Their go-to-market time is compressed. Their margins are tighter. Their ability to adapt is baked into their infrastructure. Meanwhile, companies that treat AI as a bolt-on will be forced to rebuild entirely if they want to compete. Waiting won’t reduce that cost, it’ll only increase it.
According to Thomas Cuvelier, Partner at RTP Global, “2026 will be the year of autonomous workflows, where enterprises will shift from ‘AI features’ to AI workers handling entire processes.” That prediction aligns with exactly what we’re seeing play out: incumbents being pressured to overhaul legacy tech stacks while AI-native disruptors leap ahead with streamlined products that scale.
If you’re leading a company, take a hard look at your tech stack. Can it support autonomous processes? If not, game plan your transition now. Because when the shift hits full scale, you’ll want to be ahead, not reacting from behind.
Emerging AI capabilities
We’re stepping into the next phase of AI, where the tech becomes smarter, safer, and more collaborative. You’re not just looking at tools that run instructions. You’re looking at systems that learn from their own failures, store critical information securely, and coordinate across multiple tasks and teams in real time. For executives, this isn’t just technical advancement, it’s competitive leverage.
Startups are already moving fast here. They’re building self-correcting workflows, AIs that detect when something goes wrong and fix it themselves, without needing human prompts. That cuts out delays and builds resilience right into the operating system. Then there’s secure memory. This isn’t just about storing data, it’s about doing it responsibly. AI systems are now being designed to retain sensitive organizational knowledge while meeting high standards for data privacy, security, and compliance. If you’re in a regulated industry, finance, healthcare, logistics, this is critical. It’s the kind of capability that moves AI adoption from useful to essential.
Multi-agent collaboration is another major piece. Think multiple AI agents, each optimized for a specific function, working in sync with each other and alongside humans to handle bigger, layered problems. That coordination is what makes AI scalable across complex environments. It turns isolated use cases into systems-level performance.
These developments aren’t experimental. They’re coming to market now. Thomas Cuvelier from RTP Global pointed to these areas, self-correcting workflows, secure memory, multi-agent systems, as the big technical frontiers for 2026. He’s tracking early-stage companies commercializing this tech. They’re not selling ideas, they’re deploying products.
If you’re leading a business unit, these are the technologies you should be prioritizing. They’re not hype-driven. They’re infrastructure. And if these capabilities aren’t on your roadmap, you should ask why. Because the companies that adopt them early will operate faster, safer, and with more intelligence built into every layer of the business.
Redesigning workforce organization
AI isn’t just changing software. It’s changing how companies organize people, and that change is overdue. Traditional hierarchical structures don’t function well in fast-moving, AI-augmented environments. What’s working now is a shift toward teams built around capability rather than reporting lines. Skills come first, not static roles.
Orla Daly, Chief Information Officer at Skillsoft, made this clear: “Organisations that flex their workforce based on priorities, and build teams based on skills versus reporting lines, will be better equipped to meet the demands of a fast-changing, AI-enabled workplace.” That’s where the edge is, structure that adapts to what the company needs now, not what was defined five years ago.
One model that’s gaining traction is the hub-and-spoke approach. A central AI governance team defines data policies, risk standards, and frameworks. Business units act as spokes, quickly deploying AI use cases with local autonomy. That gives the organization precision and speed. At scale, this creates consistency in how AI is used while still empowering teams to iterate and solve actual problems. It also gives leaders clear accountability on both risk and execution.
But agility without oversight is chaos. Daly emphasized that strong guardrails are just as important as experimentation. Companies need the confidence to try new things, but they also need a clear process to test, validate, and scale what works. That kind of operational discipline is how you separate transient tools from transformational systems.
As AI tools get distributed across functions, from HR and finance to marketing and legal, you can’t afford disconnection. Siloed teams will miss out. Cross-functional alignment, driven by clearly defined capabilities and shared goals, will drive both learning speed and product velocity. And it reduces waste.
If you want your workforce to keep up with your AI strategy, don’t wait for HR to propose a model. Build team structures that reflect adaptability, clarity of purpose, and trust in individual ownership. The teams that operate like that are already outperforming, quicker decisions, tighter execution, lower friction.
Key highlights
- Autonomous workflows are the next competitive edge: AI is shifting from feature-based tools to fully autonomous systems that manage end-to-end processes with minimal human input. Leaders should prioritize re-architecting legacy systems now or risk falling behind AI-native competitors by 2026.
- Emerging AI capabilities are redefining enterprise infrastructure: Self-correcting workflows, secure memory, and multi-agent collaboration are becoming operational requirements, not just innovations. Executives should invest in startups or internal R&D advancing these areas to ensure readiness for high-scale, low-error environments.
- Organizational models must evolve to unlock AI’s full value: Traditional hierarchies are too rigid for AI-driven operations. Decision-makers should transition to skill-based, cross-functional team structures with centralized AI governance to accelerate agility, maintain compliance, and drive system-wide adoption.
