AI change management will become a priority for CEOs
Anahita Tafvizi, Chief Data and Analytics Officer at Snowflake states that in 2026, it will not be the models that are the challenging aspect of AI, but the people. As companies deploy AI tools across all functions, the challenge shifts from building technology to changing how people work with it. Organizations will therefore need structured AI change management functions to effectively address issues such as reskilling, trust, reward incentives, and organizational structure.
Employees need to learn when to trust AI or better leave it aside, and how to remain accountable for decisions made with the help of these tools. Companies that recognize AI as a system that fundamentally changes decision-making and responsibilities will be the most successful.
The new core players in enterprise AI: 'Forward Deployed Engineers'
According to Baris Gultekin, Vice President AI at Snowflake, it is no longer enough for AI platform providers to offer customers just an API and documentation, as companies struggle to keep up with AI innovations. By 2026, AI vendors with teams of Forward Deployed Engineers will secure the largest and most strategic accounts. These specialists translate customer needs, mitigate AI risks, and build directly applicable solutions. This integrated model gives companies a significant advantage over competitors and determines which AI platforms stand out. It not only gains market share but also helps end-users actually realize returns from their AI initiatives.
AI changes the hierarchy within engineering
Dwarak Rajagopal, Vice President of AI Engineering and Research at Snowflake sees the hierarchy within engineering shifting. The priorities of engineers at all levels are changing through AI. As AI Agents automate routine coding and simple tasks, junior engineers must operate at a higher level and focus on infrastructure and system design. In short, work that was previously the domain of seniors. Senior engineers are also evolving into technical strategists and mentors, taking on a more guiding role in overseeing complete systems and ensuring that AI agents and teams work seamlessly together. This shift fundamentally changes computer science education and job descriptions, enabling emerging professionals to engage in more strategic work. This will not only reshape careers but also the way the next generation creates and manages technology.
Spoken language as a new programming language
The ability of an AI to generate and execute code forms a crucial bridge between the statistical, non-deterministic world of LLMs and the deterministic, symbolic logic of computers. This enables a new way of programming, where you simply speak your own language. You no longer need to know a specific syntax like Go or Python; the key is to clearly explain what you want the AI agent to do, according to Vivek Raghunathan, Senior Vice President of Engineering at Snowflake. By 2026, the main challenge in product development will no longer be coding, but creatively shaping and defining products. This shift democratizes software development, with the number of people who can build applications themselves and deliver valuable work expected to increase tenfold.
From forgetful to reliable: AI finally gets a real memory.
Vivek Raghunathan also states that a major limitation of current AI Agents is that they forget almost everything after each interaction. That is set to change as memory becomes a core function for AI Agents. Instead of having to start over each time, they can remember and use relevant information from previous conversations and contexts, enabling personalized and continuous collaboration. This goes beyond just remembering facts. An AI tool can then understand user preferences, project history, and changing goals. As a result, AI transforms from a simple, task-oriented tool into a growing partner, making interactions increasingly efficient and meaningful through a greater focus on context.
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AI Agents will become your new colleagues, so ensure a human onboarding
In 2026, AI Agents will become an integral part of your workforce. Guiding and managing them feels more like coaching than programming. In fact, we will onboard AI Agents like new employees: We will give them access to contextual documents, let them observe our workflows, assign small tasks, and provide continuous feedback so they can learn and develop. This applies to every step an employee goes through within an organization, including performance evaluations and even "promotions," where AI Agents gain more autonomy and responsibility as they prove their reliability. There will even be AI Agents at senior levels who manage other AI Agents and evaluate their work. This creates a dynamic AI workforce that continually improves itself and keeps learning and growing alongside human colleagues, according to Baris Gultekin, Vice President AI, Snowflake
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The embrace of data democratization as growth potential
Martin Frederik, Country Manager for Snowflake in the Benelux sees that we will achieve greater productivity gains next year through broader access to high-quality and reliable data. Recent research in the Benelux shows that inefficient data strategies and poor data quality cost organizations enormous amounts of productivity each year. Therefore, a solid data foundation becomes essential: data must be accurate, secure, and easily accessible, so teams can focus on innovation and real business value. In the AI era, this is already within reach thanks to simple, connected, and reliable data platforms that bring together all structured and unstructured data and provide the right context for reliable AI. Those who democratize data next year will directly unlock the greatest growth potential for the coming years.
Read also: 2026 will be the year of AI strategy in companies
