Introducing The Platform for AI Success

The connective layer that makes enterprise AI work

After more than a decade helping enterprises achieve AI success, one pattern is clear: The organizations that succeed create strong connections between their teams, their AI systems, and the controls that make AI trustworthy at scale.

AI success requires three pillars to work together:

• People: Every builder type, from domain experts and analysts to engineers, contributes safely with tools for their skill set in a shared space.
• Orchestration: Machine learning models, LLMs, agents, business rules, and human judgment are coordinated into real operational workflows.
• Governance: Visibility, validation, and performance measurement are embedded from design through production.

The Platform for AI Success provides this layer — bridging people, orchestration, and governance in a single environment where AI operates as part of the business, not as isolated experiments.

Where the platform meets the problem

As AI spreads across the enterprise, three operational gaps are becoming impossible to ignore: Organizations can’t see or govern the growing number of AI agents, easily turn business ideas into working AI, or coordinate multiple intelligences required for complex enterprise decisions.

Closing the AI success gap means solving these challenges directly. The Platform for AI Success introduces these flagship capabilities designed to do exactly that.

Dataiku Agent Management

Agents are proliferating faster than organizations can track them. They’re being built in cloud platforms, enterprise applications, internal tools, and vendor ecosystems. In most companies, there’s no unified view of what agents exist, what decisions they’re making, or whether they’re delivering business value. This creates a new governance challenge.

Traditional monitoring focuses on technical health: uptime, latency, error rates. But an agent can be technically healthy while still failing the business by making incorrect decisions, violating policy, or producing inconsistent outcomes.

Dataiku Agent Management provides a control tower for enterprise AI agents — monitoring not just system health, but the quality, policy alignment, and business impact of agent decisions. It gives organizations a centralized view of agents across platforms including Dataiku, AWS Bedrock, Snowflake Cortex, Databricks, and Google. Business teams can see what agents exist, how they behave, and whether they’re delivering meaningful outcomes. As enterprises move from individual models to fleets of AI agents, this visibility becomes essential.

Apply for early access to Dataiku Agent Management: Express interest today.

Dataiku Cobuild

One of the biggest barriers to enterprise AI is translating business intent into working systems. Leaders know the decisions they want to improve, but turning those ideas into production AI typically requires weeks of coordination between business stakeholders, data scientists, and engineers, and something always gets lost in translation.

Dataiku Cobuild removes that friction entirely. Describe a business objective in natural language, and it generates a structured AI project inside Dataiku's visual environment, including pipelines, models, agents, and approval workflows. Unlike coding assistants that generate opaque scripts and ask you to trust them, Dataiku Cobuild produces auditable systems that teams can inspect, modify, and govern before a single line reaches production. Leverage the speed of the most advanced AI building tools, with the accountability that enterprise environments demand.

Dataiku Cobuild: Available June 2026 for all Designer-level users

Dataiku Reasoning Systems

The most important enterprise decisions rarely fit inside a single model or agent. Manufacturing disruptions, supply chain volatility, and financial risk management require multiple forms of intelligence working together: data pipelines, predictive models, business rules, domain expertise, and human judgment. Most AI deployments treat these components as separate tools.

Dataiku Reasoning Systems bring them together into coordinated decision systems designed around real business operations. Instead of deploying isolated AI components, organizations can orchestrate models, agents, rules, and human input into governed workflows aligned with explicit business objectives. These systems embed institutional reasoning directly into operational AI while maintaining transparency and oversight.

At launch, Dataiku Reasoning Systems focus on high-impact domains where decisions are both frequent and critical:

• Manufacturing Operations: Available now
• Supply Chain: Available in Q2 2026
• Financial Risk: Available in Q3 2026

The next phase of enterprise AI

Enterprise AI is no longer an experiment. It is an operational reality, spreading across every cloud, every team, and every critical business process whether organizations are ready for it or not. The question is no longer whether to invest in AI. It is whether the infrastructure exists to make that investment actually work.

The companies that succeed in the next phase of enterprise AI will not be the ones deploying the most models or agents. They will be the ones that can operate AI as a connected system across their organization.

They will know what AI systems exist, what decisions they’re making, and whether those decisions are delivering measurable business value. They will be able to translate business intent into working AI quickly, while maintaining visibility, governance, and control. And they will orchestrate models, agents, data, and human expertise into decision systems that improve how the business actually runs.

That shift — from deploying AI components to operating AI as a coordinated system — will define the next phase of enterprise AI.

The Platform for AI Success is designed to power that shift.