Prediction #5
Automation becomes the bridge between data and business

In 2026, the most successful data teams won’t just manage data; they’ll automate its connection to business outcomes.

Expect to see a shift in focus from raw data engineering to automated workflows that link datasets directly to key business assets like models, KPIs and decisions. This automation will enable real-time insight and continuous alignment between AI systems and strategic goals.

Automation will also close the gap between technical and business users, allowing everyone—from data scientists to business analysts—to collaborate within a governed framework.

Despite $30–40 billion in enterprise investment into GenAI…95% of organizations are getting zero return. Organizations stuck on the wrong side continue investing in static tools that can't adapt to their workflows, while those crossing the divide focus on learning-capable systems.

Source: MIT. mlq.ai/media/quarterly_decks/v0.1_State_of_AI_in_Business_2025_Report.pdf

Collibra POV:

Our platform creates active links between datasets, AI models and policies—so your data is mobilized. Automating visibility, control and tracing allows your teams to achieve faster, better AI use cases.

A variety of novel governance practices are beginning to materialize to address the extreme agility, change, and cross-functionality required for managing the acceptable use of data via AI. Such practices—new roles, processes, and controls— work to continuously connect data and its use, sense emergent governance needs and help the organization respond with key adjustments, and balance myriad perspectives that create tensions between control and innovation.

Dr. Barbara Wixom, Principal Research Scientist, MIT CISR

How automated is your data-to-business workflow today?

Fully automated — minimal manual intervention
Partially automated — key connections built
Mostly manual — data and business teams still siloed
Not sure — we’re still defining workflows