2026 Predictions from Mei Dent, TeamViewer’s Chief Product & Technology Officer

Data governance will determine which organizations successfully deploy AI agents at scale and which get left behind. The technology is ready. Companies are already deploying AI agents for straightforward tasks such as ordering IT equipment, managing system access, and resolving basic IT issues without human intervention. These aren't future possibilities; they are happening now.

However, broader autonomous agent adoption – where employees can express an intent across more complex workflows and the agent executes without needing step-by-step human oversight – is being held back. But not by technology itself; the real barriers are data governance, change management, and business process transformation. Companies must fundamentally rethink their workflows to fully leverage autonomous agents. The technology has already shown what's possible; now businesses have to evolve their processes to match that capability.

Organizations should shift their focus from technology readiness to governance frameworks and change management strategies. Establish clear data governance policies now, map out which business processes need redesign to accommodate autonomous agents, and invest in change management resources to help employees understand the shift to intent-based execution.

Through 2026, the narrative around "AI-proof careers" will be recognized as fundamentally flawed. We shouldn't be thinking about careers being replaced by AI – the future is human plus AI, not versus AI.

Rather than making specialists obsolete, AI democratizes specialized tools, expanding who can use them effectively. AI lowers the bar so that product managers, for example, can also use sophisticated design tools that were previously only for trained professionals. The "special skill" becomes accessible to more people, while the experts can focus on more complex, creative challenges.

We must stop asking which roles will survive AI and start asking how every role can be enhanced by AI. Organizations should focus on training employees to become AI orchestrators – teaching them not just to use AI tools, but to critically evaluate outputs, guide AI toward better results, and identify opportunities for AI augmentation. Business leaders must foster a culture that encourages experimenting with AI tools across all functions, not just technical roles. The goal isn't to protect jobs from AI, but to evolve every job to leverage AI as a force multiplier.

The AI energy crisis will force a fundamental reckoning across every industry – not just one sector or segment. AI training and consumption will require more data centers, and the locations and energy sources of those data centers will come under intense scrutiny. With approximately 60% of the energy consumed by data centers today coming from fossil fuels, companies will be forced to choose between AI capabilities and environmental commitments – a tension that will drive innovation at an unprecedented pace.

Additionally, as regions implement stricter data residency requirements and local carbon commitments, the question isn't whether innovation will happen, but how quickly necessity will drive breakthrough solutions.

Organizations deploying AI at scale should begin evaluating their energy strategies now, not waiting for solutions to materialize. They should partner with data center providers that invest in sustainable power sources and can demonstrate concrete plans to reduce fossil fuel dependence. On top of that, businesses should prepare for a landscape in which AI capabilities vary by region, driven by local energy constraints and environmental regulations – data sovereignty and carbon commitments will increasingly shape which AI features can be offered where.

In 2026, we’ll see the emergence of AI systems capable of adapting to emotional sentiment, cultural context, and the subtle dynamics that make human interaction effective. Imagine a customer support agent that adapts to the culture, the emotional sentiment, and communication preferences of a customer or employee. Or an AI system that can gauge employee engagement by understanding patterns – recognizing when someone is disengaged by how quickly they move through tasks. There are softer things we can pay attention to around happiness and engagement that machines can process much faster and much more consistently than human workers.

Organizations should begin building AI systems with emotional intelligence and human values in mind, rather than treating these as afterthoughts to technical capabilities. Start by identifying use cases where emotional adaptation would create meaningful value – customer service, employee support, training programs, for example. Businesses should test AI systems not just for accuracy, but for their ability to recognize and respond appropriately to emotional context. And importantly, approach this with optimism rather than fear: emotionally intelligent AI isn't a threat to human connection, but a tool that can help us better understand what makes us tick and scale empathetic interactions.