Dimitar Dimitrov is the founder and Managing Partner at Accedia, a leading European AI & Custom Software Development Company.
If companies focused on AI experimentation in 2025, in 2026, they’ll seek out real impact. Regulatory pressure, talent shifts and rising expectations for responsible AI demand higher reliability and security. This environment puts CTOs at the center of AI performance.
As the founder and managing partner at Accedia, I’ve led countless AI initiatives and software projects. Based on my experience in the industry, I’m sharing eight technology predictions for 2026 and what CTOs should do about them.
1. Developer Hiring Cycles Will Slow Dramatically
In 2026, hiring skilled engineers will take longer. Many firms will try to pair AI tools with a smaller group of experienced developers and hire fewer juniors. That shrinks the available talent pool right when architecture-minded, AI-literate engineers are in the highest demand. Meanwhile, HR is drowning in AI-generated résumés, which slows screening instead of speeding it up.
Assume your time-to-hire will stretch—and plan capacity accordingly. Invest in upskilling internal developers rather than relying only on the market. Where gaps threaten your roadmap, turn to AI development companies. For instance, one manufacturing company struggling to fill a critical AI development role in time to meet a GenAI order-intake platform deadline partnered with my company. By embedding hybrid teams of solution architects and AI-augmented engineers, they successfully launched within a quarter and met their timeline.
2. CTOs Will Be Tasked With Fixing Failed AI
A wave of business-initiated AI pilots and agent projects will underperform on accuracy and adoption due to weak data, unclear ownership and technical oversight. When that happens, CEOs will turn to CTOs to fix it. In many organizations, technology leaders already own AI strategy; more will be asked to lead on the business side as well.
Treat underperforming AI projects like any other critical system stabilization. Centralize governance, starting with an inventory of all AI use cases. Clean up the data and knowledge bases feeding those systems. Improve user experience, so people want to use them. Add quality thresholds and monitor all AI outputs.
3. AI-Native Development Platforms Will Reshape Engineering Teams
AI-native development platforms and coding copilots mean smaller teams can deliver what used to require much larger squads. But speed without safety is dangerous. If security, compliance and code quality aren’t built into the tool chain, you’ll gain velocity and lose reliability.
Treat AI as a force multiplier with guardrails. Standardize a set of AI tools and embed checks into your CI/CD: license scanning, vulnerability testing and automated code review for AI-generated changes. Culturally, position AI as a junior developer: helpful, fast and absolutely in need of review.
4. Domain-Specific AI Models Will Overtake General Models
General-purpose models are great for demos, but they’re not always suitable for regulated or high-precision work. In areas like finance, logistics and manufacturing, domain-specific models (or general models grounded in domain data) can deliver better accuracy, explainability and compliance.
Identify one to two high-value workflows where generic AI stumbles: pricing, claims triage, clinical intake, support, etc. Pilot a domain-specific model and measure performance against your current baseline. Build internal “context engineering” capabilities: people responsible for curating the right data, documents and rules, so models stay grounded and trustworthy.
5. Companies Will Shift To Small, Composable AI Agents
Big, do-everything agents sound impressive but are hard to control, debug and govern. The winning pattern in 2026 will be small, composable agents, with a narrow job, clear interface and strict permissions.
Resist the urge to build a single mega-agent. Instead, start with simple, well-bounded agents: ones that triage tickets, draft responses or check compliance. Give each defined input, output and escalation paths—and monitor them centrally. Treat them like specialized microservices with logs, metrics and owners.
6. Agent Orchestration Will Unlock The Next Wave Of AI Value
The autonomous agent market is expected to reach $8.5 billion by 2026 and $35 billion by 2030. The real upside won’t come from a single agent, but from orchestrating many agents that collaborate safely across workflows, turning isolated bots into an intelligent automation fabric. Еstimates suggest that with effective orchestration and risk management, this market could grow further 15% to 30%, reaching $45 billion.
Start building the foundations for orchestration: a simple agent registry—a common way for agents to exchange messages—and a basic “control room” view of what agents are doing. Pilot end-to-end flow using multiple small agents, track latency, cost and quality to see where orchestration brings leverage and complexity.
7. Twenty-Five Percent Of AI Budgets Will Slip Into 2027 Without Clear ROI
Boards are no longer impressed by AI pilots; they want proof. Only 15% of organizations saw any EBITDA lift from AI in the past year, and fewer than one-third can link AI to real P&L impact. The gap between AI vendor promises and actual value will prompt CFOs to pause or defer a significant portion of planned spending.
Re-baseline your AI portfolio against business outcomes. For every initiative, answer the following: What metric are we moving? By how much? Be ready to downsize projects that can’t demonstrate impact, and double down on those that can. Negotiate harder with vendors and focus on use cases where value is measurable.
8. Quantum Threat Readiness Will Become A Priority
Quantum computing won’t be mainstream in 2026, but its security impact will. As governments and large players advance quantum-accelerated computing, today’s widely used encryption schemes move closer to a future break point. Data with a long shelf life (financial, legal, IP, etc.) faces a harvest now, decrypt later risk.
Treat quantum readiness like a long-term security program. Start with a crypto inventory: Where and how do you use encryption today? Pilot hybrid, post-quantum-ready approaches in non-critical systems. Revisit key management and rotate or upgrade once standards settle.
Conclusion
For CTOs, these technology predictions for 2026 aren’t a call to chase every new tool, but to recognize where the ground is shifting and act with intent. Focus on proving that AI and automation drive real business outcomes and invest in readiness, from skills and orchestration to quantum-safe security.
Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
