Industry executives and experts share their predictions for 2026. Read them in this 18th annual VMblog.com series exclusive.
By Maryna Bautina, Senior AI Consultant at SoftServe
For the past decade, AI has primarily existed almost entirely in digital environments – text, images, models, copilots, and chatbots. But 2026 will mark a transition point as AI increasingly interacts with, learns from, and adapts to the physical world.
As companies push for automation that doesn’t just behave, but adapts, two emerging fields are at the forefront of this shift: embodied AI and Bio-Hybrid AI. While one could definitely file these technologies under “stranger than fiction” as researching and testing these technologies advances rapidly, bringing them closer to maturity, and in some cases, sparking early enterprise interest.
“Embodied AI” Becomes the New Year’s Breakout Story
Up until now, AI and robots only worked in perfect, controlled, or virtual environments. But 2026 will be the first year these systems learn the way humans do: by trying, failing, and adjusting in real time. Embodied AI is more than just a technical step forward for automation – it becomes the foundation for enabling systems and robots to perceive, think, and make decisions based on their surroundings.
We’re already seeing this emerge in manufacturing, where early applications are providing immediate business value. Consider Volkswagen’s project to update the assembly process for roof liner stands. This process is notoriously complex, requiring significant investment, time, and resources to automate the assembly. Manually designing automation solutions can take years, given the vast scope for solutions and prolonged iteration cycles needed.
Using embodied AI, Volkswagen capitalized on its reinforcement learning capabilities on a digital twin. Simulating its production process allowed for its robotic programming to prepare for different setups, generate multiple strategies, specify material properties to align with production requirements, and implement adaptive processes utilizing vision and force sensors for real-time adjustments – all of which normally requires months of engineering work.
Once largely restricted to highly specialized applications, embodied AI will enter mainstream use in 2026, introducing a new era of flexibility, intelligence, and accessibility at all levels. Deployment will no longer be the finish line, with its growth fundamentally changing how businesses approach automation in the new year and demand ongoing customization of processes, benchmarking tools, and operational accountability from their partners.
AI Gets Physical…Literally
While we’re all accustomed to the hardware and software our computers currently use, bio-hybrid AI marks the first step toward intelligence we don’t engineer. Referred to as “wetware,” scientists are growing tiny brain-like cell clusters and connecting them to computers to test whether biological tissue can learn or process information. Early prototypes exist at Johns Hopkins and in EU-funded labs. It’s the earliest signal of a new class of compute: biological systems that learn the way life does, not the way software does.
While the work is still in its early stages, organoid-based systems show early promise for drug development for neurological conditions. Because drug development can take over a decade and cost billions due to late-stage trial failures, using AI to analyze responses from human-derived organoids could enable researchers to screen a wider variety of drugs in ways that mimic human biology, helping to prioritize candidates to test further for certain conditions or diseases. By 2026, we expect this approach to move beyond experimental research and become the first practical steps toward a form of bio-intelligence that we don’t fully engineer, as the AI learns from living human tissue to enhance the overall success rates of drug development, resulting in more customized, personal treatments and a reduction in costly late-stage failures.
The Next Evolution of Enterprise AI
With the shift from knowledge-based pilots to more action-based applications surging in the last year, agentic AI has proven its ability to execute – orchestrating tasks, integrating with enterprise systems, and driving processes forward with little to no human intervention. Physical AI is the next phase of this evolution, bridging the gap between the data-centric models and the requirements of real-world robotics and production environments. Taken together, embodied AI and bio-hybrid AI signal a larger shift toward systems that don’t just predict outcomes, but act on them.
For enterprises, 2026 will be the year their digital journeys become more adaptive, less reliant on manual coding, and dramatically faster to deploy – a shift that will reshape how CIOs and IT leaders evaluate their operations in 2026.
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ABOUT THE AUTHOR
Maryna Bautina is a Senior AI Consultant at SoftServe, a premier IT consulting and digital services provider. Bautina builds smart AI systems that actually get used – from generative AI tools to large-scale ML platforms. She’s led high-impact projects and spoken at major tech events. When she’s not tackling real-world challenges, she’s mentoring rising talent and exploring what’s next in tech.
