AI reshapes skills in tech, finance & healthcare by 2026

The rapid integration of artificial intelligence across major industries is set to reshape the skills required in technology, finance, and healthcare going into 2026, according to sector-specific predictions from senior leaders at OpenText. AI's growing involvement in core processes is expected to introduce new challenges around data quality, trust, and operational transparency, as it becomes more deeply embedded in organisational workflows.

Developer landscape

The shift in the developer role continues, with engineers increasingly acting as coordinators for AI agents rather than simply writing code. According to Tal Levi Joseph, Vice President of Product & Engineering, Application Development & Maintenance at OpenText:

"Engineers will act as orchestrators of virtual AI agent teams, defining each agent's role, rules, tools, and collaboration patterns next year. Their job will shift from direct execution to designing systems of intelligence that align with business outcomes. Success will depend on setting checkpoints and KPIs that guide autonomous workflows and adjusting them when they drift. They will also design new testing methods to validate non-deterministic, agentic systems."

This anticipated evolution comes as many organisations pursue GenAI-augmented workflows, but still report a gap in AI and machine learning expertise. The rise in AI-written code and the influence of autonomous agents on architectural decisions could make developer workflows more challenging in the short term.

"AI will co-write more code, shape architecture, and make development messier before quality and efficiency balance out. Early adopters will pay a temporary quality cost as they adapt to AI-driven workflows. Smart organisations will connect AI copilots to broader context and institutional knowledge, linking requirements, tests, and historical defects, so productivity gains translate into better delivery. Over time, self-improving and self-healing pipelines will turn productivity into lasting quality," said Levi Joseph.

Joseph also highlighted a significant change for DevSecOps. "DevSecOps will embed threat modeling and prediction across the entire lifecycle. Instead of reacting to vulnerabilities, teams will anticipate risks based on code changes, dependencies, and historical data. Compliance, security, and dependency intelligence will converge to give end-to-end visibility into relationships and potential exposures. This predictive approach will sustain development speed while strengthening trust and governance in an AI-driven SDLC."

Financial sector

Financial institutions are expected to see AI become an integral component of daily operations. Monica Hovsepian, Head of Financial Services Industry Strategy at OpenText, outlined how AI will move from automation to roles characterised by decision-making and risk prediction.

"In 2026, AI will become deeply embedded in core financial operations such as shifting from automated loan approvals to dynamic risk modeling, acting as a trusted teammate that predicts risk, automates compliance, and adapts to customer needs in real time. The real shift will occur once agentic AI systems can take initiative, act across platforms, and respond without waiting for human input. Their accuracy will depend entirely on clean, secure, and well-governed data. Institutions that lead with AI will treat data quality and oversight as non-negotiable, knowing that trust depends on it."

Hovsepian further indicated that payments, compliance, and fraud detection will become increasingly automated and dynamic, prompted by agentic AI acting across connected banking ecosystems. "Open banking, AI, and embedded finance are converging into a connected system where money moves securely and often without user input. Agentic AI will anticipate intent, verify identity, detect fraud, and authorise transactions in real time across platforms. Payments will become invisible as they route themselves through the safest, most efficient paths while remaining transparent and auditable. Financial trust will depend less on interfaces and more on clean, well-governed data that keeps intelligent systems aligned and secure," said Hovsepian.

Regulatory compliance will also be shaped by AI systems, which must process and act on shifting regulatory requirements in real time. "Compliance will shift from a static requirement to a dynamic, AI-powered discipline. As financial data moves across borders, institutions must manage conflicting regulations in real time. Agentic AI will track regulatory changes, map data flows, and apply controls automatically while providing clear, auditable explanations. Organisations that build transparency and ethically governed, explainable AI into their systems will move faster, scale smarter, and turn compliance into a source of trust and long-term advantage," said Hovsepian.

Healthcare transformation

AI adoption in healthcare is expected to move from theoretical benefit to tangible impact by 2026. Scott Lundstrom, Senior Healthcare Industry Strategist at OpenText, pointed to advancements in clinical-grade AI and their integration with electronic health records (EHRs).

"AI has long promised to transform healthcare, but most organizations have lacked the tools, governance, and secure data foundations to do it safely. That is beginning to change as clinical-grade AI becomes embedded in core systems like EHRs. By 2026, natural language processing and predictive modeling will support faster decision-making at the point of care, while ambient intelligence automates documentation and reduces administrative load. The shift to proactive, AI-enabled care will depend on building security, usability, and trust into every layer."

Lundstrom also addressed patients' changing expectations regarding data use and privacy. "After years of breaches and opaque practices, patients now expect transparency about how their data is used. In 2026, leading healthcare organisations will treat data visibility and consent as central to the care experience, showing who accesses their records and why. Privacy-preserving technologies like federated learning will let patients contribute to innovation without losing control. The systems that empower patients to co-own their healthcare data will set a new standard for trust and loyalty," said Lundstrom.