IA 2026: agents, graphers and the rise of the Decision Engineer

Every summer, the AI Hype Cycle reignites the debate. By 2026, you should look at the Pieces that are laying the groundwork of how companies will decide. It's not another list: it's a architectural plan to go from “nice reports” to decisions connected with the result.

These are the five technologies that, together, make up the puzzle at the start of the curve.

Decision Intelligence: From “What Happened” to “What Do I Do Now”

For years we have invested in BI to describe the past. The next jump is operationalize the decision: systems that Recommend the best action according to objectives, restrictions and risk.

• What it provides: prioritization of initiatives, dynamic resource allocation, adaptive pricing, risk detection and mitigation.
• How it is implemented: optimization models + causality + simulation, connected to the operational layers (CRM, ERP, logistics).
• Key metric: uplift on business KPIs (revenue, margin, churn), not just model accuracy.

Agentic Analytics: from consulting data to talking with them

Analytics ceases to be a destination and becomes interactive and proactive. Los analytical agents they understand intent, they consult multiple sources, Explain results and They push insights when they detect relevant changes.

• Typical cases: actionable alerts, “what-if” analysis, plan generation, personalized executive summaries.
• Why it matters: reduces decision-making time and Democratize access to analysis without creating metric chaos.

AI Agents for Decision Intelligence: decision that learns in a loop

Beyond insight, these agents execute tasks, Evaluate results and Adjust decision policies on an ongoing basis.

• Base architecture: agent orchestrator + tools (APIs, knowledge bases) + security and governance policies.
• Business effect: automation of cognitive processes (approvals, reconciliation, procurement), with Feedback loop to get better every week.
• Critical Requirement: traceability and controls (human-in-the-loop) to align autonomy with risk.

Decision Engineer: the new key role

He is not a classic data scientist or an analyst. It's who Design the reasoning system: defines objectives, restrictions, signs, sources of truth and How do you close the loop between action and result.

• Responsibilities: decision modeling, business metric design, selection of techniques (optimization, causality, RL), governance and ethics.
• Skillset: product + analytics + engineering + business.
• Why now: Companies need intelligence architects that align AI with strategy and compliance.

Graph Data Science: The Foundation That Gives Context

The real company are relationships: customers with products, suppliers with risks, teams with projects. Los Graphs they capture that network and allow Reason about the context, something that tables can't do.

• Advantages: community detection, optimal routes, scoring by centrality, enrichment of RAG with Knowledge Graphs.
• Practical use: fraud, supply chain, B2B sales, B2B marketing, cybersecurity.
• Bonus: combined with AI agents, graphs act as structured memory for consistent decisions over time.

Take the next step: train at Mbit School

Put these ideas into practice with applied, business-oriented training. Choose your route: Master in Artificial Intelligence, Master in Data Governance or Master in Connected Industry and Artificial Intelligence. Request Information Today and accelerate your career in business intelligence architecture.