Jan 21, 2026

12 Experts Predict the Future of AI in 2026

2026 marks a turn for enterprise AI. Bets placed over the last two years now show results. We drew on first-hand lessons, partner ecosystem insights, and industry patterns to uncover key trends. Experts from retail, healthcare, life sciences, data, cloud, and AI take a stand on the shifts that will separate leaders from laggards. They also share views on what businesses need to get right now to avoid being caught flat-footed.

1. Which AI approaches will flop in 2026?

Anna Malitska

Revenue Enablement, Lead

By 2026, the "AI for AI's sake" era will end. Technology will no longer sell on buzz alone, but on measurable business impact. The current gold rush — where companies slap "AI-powered" on every product — has produced solutions that are impressive in theory, but don't improve conversion rates, customer value, or operational efficiency.

Ashley Testen‑Sherman

VP Client Success, Retail & CPG

AI developed in silos — where models, data, and intelligence are disconnected from the broader ecosystem — will flop. This closed approach creates blind spots, limits context, and amplifies risk, bias, and inefficiency. To be effective, AI must work across various systems, partners, and perspectives.

Gartner predicts that by 2027, AI agents will augment 50% of business decisions, requiring cross-system integration. We're already seeing this trend. One toy manufacturer improved supply chain visibility, increasing service fill rates by 6%, and reducing stock size by 50%. A pharmaceutical company saw 18% faster order processing and stock allocation using an integration platform.

2. What should companies prioritize in 2026 to build foundations for resilient, multi‑agent and AI‑intensive systems?

Ruslan Kusov

Cloud CoE Director

Most AI projects start strong, but 90% stall in pilot purgatory. To make AI work in 2026, companies will need an AI-ready cloud platform — modernized for scalability, security, and cost savings. The good news is that modernization that once took 2-3 years now be done in 4-6 months with AI.

MIT (NANDA) highlights a “translation” gap: only 5% of AI tools reach production, with 95% showing no company-wide impact. A modern cloud foundation helps AI learn and adapt through persistent memory, integration, and governance. A trend we're seeing is that many companies, such as Hallo Healthcare need to modernize without operational risk or rebuilds.

3. Will governance (ecosystem and orchestration) beat owning the “best AI model in 2026?

Oleksii Reutov

AI & Data Science Delivery Excellence Lead Data Science Group

By 2026, success in enterprise AI won’t be about chasing the latest models — it will favor partners and architectures that offer long-term resilience through hybrid, multi-model, and adaptability-first strategies.”

Ashley Testen‑Sherman

VP Client Success, Retail & CPG

In 2026, you won’t win alone. Multi-partner ecosystems will help you move faster. Each partner brings something unique — technical depth, creative vision, operational wisdom, or customer insight — that no single organization holds alone.

Ecosystems proved to be a performance advantage for Avery Products. They used NVIDIA's AI Enterprise software on AWS cloud infrastructure to accelerate design creation.

4. What are “intelligence engineers," and why does it matter in 2026?

Iurii Milovanov

AVP, AI & Data Science

In 2026, if your day-to-day involves writing glue code, dashboards, or data transformation scripts, there’s a good chance an agent already do it faster. That won't mean you’re out of a job. Intelligence engineers will own the interface between human insight and agent output, guide the agents, validate results, and ensure everything integrates cleanly.

The job shift is already visible: World Economic Forum reports 86% of employers expect AI and information processing to transform their business by 2030, and AI and big data are the fastest-growing skills.

Maryna Bautina

Senior AI consultant

Agentic AI — those self-running digital agents that plan, decide, and act — will start sneaking into everyday work as a quiet co-worker. Expect them to coordinate schedules, write software, and oversee routine business operations. It already happened in small pilots in 2025. This year it will go mainstream.

Agentic AI will spread unevenly but fast: Gartner expects over 40% of agentic projects to be canceled by 2027, yet predicts agents will influence decisioning at scale — 50% of business decisions augmented by 2027.

5. What AI focus will take the spotlight in 2026?

Lyubomyr Demkiv

Director, Robotics & Advanced Automation

2026 will be the year smart robotics scales in industry. Fewer pilots. More production-ready deployments. We’ll see physical AI–powered robots handling variable tasks with minimal supervision: Autonomous mobile robots (AMRs) will reduce warehouse inefficiencies, while humanoid robots will fill labor gaps in industrial operations.

Maryna Bautina

Senior AI consultant

Embodied AI will be the big story in 2026. AI finally steps out of the screen and into the real world - robots, drones, and machines that don’t just follow scripts, but learn through movement and feedback. We’ll see them show up in all kinds of places - from warehouses and hospitals, and perhaps even in our homes.

Embodied AI is evolving rapidly: IFR says “Physical AI” allows robots to train virtually and operate through experience, aiming for a “ChatGPT moment” in robotics. For now, humanoid deployments focus on single-purpose tasks in sectors like automotive and warehousing.

6. How will companies address the shortage of high-quality, task-specific datasets for reliable AI and robotics by 2026?

Lyubomyr Demkiv

Director, Robotics & Advanced Automation

In 2026, a persistent bottleneck will remain: acquiring robust, high-diversity datasets for task-specific model development. We can't realistically meet the data requirements for reliable perception at scale using only real-world samples.

Data (not models) will remain the constraint: Gartner warns 60% of D&A leaders will suffer critical synthetic-data management failures by 2027, even as the industry pivots to synthetic data at scale.

Lutz Richter

Space Projects Consultant Robotics & Advanced Automation

In 2026, companies will significantly increase investment in data management and data maturity, driven by the same forces pushing conversations about futuristic solutions like space‑based data centers. While orbital compute remains a decade‑plus away, the underlying pressure is already here: AI’s energy and data demands are outpacing what today’s infrastructure supports.

7. How will sovereign AI reshape infrastructure in 2026

Zoriana Doshna

AVP of Technology CoE - Intelligent Enterprise and R&D

Organizations that master specialized language models in 2026 will gain adaptability and domain-specific autonomy, turning model specialization into a strategic advantage while ensuring sovereign AI through secure, compliant control of data and infrastructure.

8. How will health systems use AI to create a full patient view, while keeping data private and trusted?

Peter M. Burns

Director of Consulting and Domain Solutions, Healthcare

In 2026, AI will act as a unifying force, as it pulls and mines data from medical records, imaging systems, claims, pharmacy records, care files, wearables and even home-based sensors, to create a comprehensive view of each patient’s health.

Modern data platforms that handle vast amounts of data and support more users, like AWS HealthLake and Google Health Data Engine, supports this shift by providing real-time analytics, FHIR-compliant pipelines, and collaborative workspaces for care teams.

Steve LoSardo

Vice President, Life Science Solutions & Consulting

Federated learning and community-controlled data models will solve the privacy paradox. Instead of centralizing data, AI will learn across distributed sources without moving sensitive patient information, enabling compliance and trust. This approach will shift value capture from corporations to communities, creating new ethical and business models.

9. How can leaders prevent runaway AI costs while scaling agents in 2026?

Zoriana Doshna

AVP of Technology CoE - Intelligent Enterprise and R&D

As agentic AI ecosystems grow, platform-level monetization and usage monitoring become critical. Financial governance will be the guardrail that keeps autonomous AI innovation from becoming a runaway expense. Proactive management of compute, data, and agent activity helps balance performance with financial sustainability.

While multi-agent AI systems can complete tasks 34% faster, they also drive up costs. The shift from passive AI tools to active AI agents changes the basic economics of how businesses use AI.

10. How can organizations turn AI ambition into real business impact in 2026?

Volodymyr Semenyshyn

Chief Revenue Officer

In 2026, value will come from redesigned processes, trusted AI systems, and strong human–AI collaboration. The organizations that succeed will be those that scale AI responsibly, control costs, and build trust from the start - turning AI into a core business capability, not a side project.

2026 will be the year AI moves from hype to measurable impact. Success won’t come from isolated tools or buzzwords, but from trusted systems, strong partnerships, and a clear strategy for scaling AI responsibly. We’ve seen what works — and what doesn’t — when clients face uncertainty. The question isn’t whether AI will change your business, but how prepared you are to lead that change.

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