Link: https://blog.planview.com/mapping-the-future-of-ai-agents-in-the-enterprise/
From Planview Blog
AI in the enterprise isn’t a switch from assistant to autonomous; it’s a spectrum of capabilities. Understanding that spectrum is how organizations move from curiosity to confidence.
Planview Chief Data Scientist Dr Dich Sonnenblick and Planview CEO Razat Guarav take the time in this enlightening video to break down how AI Agents are transforming strategic portfolio management and enterprise planning.
The Rise of Reasoning Agents
For most enterprises, the first step into AI began with copilots that could answer questions or summarize complex reports. That was useful but limited. “We’ve all used a large language model to, say, summarize a document. Agents take the power of large language models one step further by giving them the ability to reason and then to act,” as Dr. Rich notes.
Reasoning changes everything. When an AI can act within defined boundaries, it stops being a passive assistant and starts becoming an active collaborator. A reasoning agent doesn’t wait for a prompt; it monitors work, detects variance, and proposes solutions on its own.
Why Background Agents Matter
Enterprise work rarely happens in tidy, scheduled bursts. Priorities shift, dependencies move, and risks emerge between meetings. Background agents fill that space. “Where we’re headed next is this autonomous future where these same agents are going to be working in the background, working 24-7 to ensure that the jobs to be done… are being done for you and the results are being brought to you so that you are still in the loop with any actions that need to occur,” Dr. Rich Sonnenblick notes in the video.
They run continuously, assessing schedules, costs, workloads, and delivery patterns. When an issue appears, they notify the right person instead of adding another alert to a dashboard no one checks. The agent becomes a tireless teammate, ensuring projects stay aligned even when humans aren’t watching.
Early Enterprise Use Cases: From OKR Insights to Copilots
“We have agents today that are generating insights about OKRs… project and portfolio health, and we have agents that are underpinning Copilot . Planview Copilot as a conversational support coach assistant… to answer questions about your current business context and to do it with respect to best practices.” Dr. Rich Sonnenblick.
These early task-based agents already show measurable impact.
- OKR agents surface where objectives and results are drifting out of sync.
- Portfolio-health agents detect variance across cost, scope, or resource allocation.
- Copilots deliver instant context, project summaries, stakeholder updates, best-practice guidance.
Each builds familiarity and trust, setting the stage for deeper autonomy later.
Watch Next: Combining the Power of VSM and OKRs to Optimize Flow and Accelerate Speed to Value
Human Oversight as a Constant
The promise of autonomy doesn’t replace human judgment; it augments it. Agents may perform the repetitive jobs-to-be-done, but people remain accountable for direction and decision. Or as Dr Rich puts it, “so that you are still in the loop with any actions that need to occur.”
This is what differentiates enterprise-grade AI from experimental automation. Every recommendation must be explainable, governed, and reversible. Trust isn’t gained by handing control to machines, it’s earned by keeping humans meaningfully involved in every loop.
Where Your Organization Likely Sits Today
Most enterprises today operate squarely in that “task-based” middle ground: copilots that assist, summarize, and inform. That’s a healthy stage of maturity. It’s where teams learn how to trust AI outputs, validate reasoning, and strengthen their data foundation.
From here, readiness depends on infrastructure, unified data, permission governance, and semantic consistency. Without those, autonomy is just an aspiration.
What the Next Leap Means for Enterprise Readiness
The next evolution combines reasoning with simulation, agents that not only respond to conditions, but model them. They’ll blend optimization algorithms, machine-learning forecasts, and scenario analysis to deliver outcomes already tailored to business constraints.
“Agents could be triggering in the background a mix of optimization, machine learning predictions, or simulation technologies in conjunction with LLMs to provide a response that is very thoughtful, intelligent, but also extremely optimized or smart.” Razat Guarav.
It’s the difference between getting a report and getting a plan.
For organizations building toward that future, the priority isn’t speed, it’s structure. Trustworthy agents need governed data, clear accountability, and collaboration between data scientists and business leaders. Those foundations ensure that as AI grows more autonomous, it remains aligned with human intent.
Closing Thought
Agentic AI isn’t about replacing human expertise. It’s about scaling it, embedding reasoning and action into the flow of work so decisions happen faster, risks surface sooner, and outcomes stay aligned.
The future of AI in the enterprise will belong to those who build that spectrum.
Watch Next: Planview Copilot Demo: Generative AI Assistant
