By now, everyone has seen the MIT figure: Despite $30-40 billion in enterprise investment into GenAI so far, “95% of organizations are seeing zero return.” Boards want to see results; CFOs are asking hard questions. But the promised productivity gains have remained largely theoretical so far. The reasons are twofold: the sequencing problem and the scaling problem.
Agentic automation represents one of the greatest levers for unlocking new enterprise value at scale. But today, the implementation of agentic AI forces IT leaders into an impossible choice between two flawed approaches: “off-the-rack” point solutions that require predicting which workflows matter most, or “DIY” custom frameworks that dump complex automation responsibilities onto already-stretched teams.
Both of these approaches have strengths on their own, but each faces the same two fundamental challenges:
- They force organizations to design solutions before understanding the actual problem
- Neither can solve for both sequencing and scaling at the same time
The sequencing problem
Traditional automation requires CIOs to be fortune tellers. Which processes deserve automation first? Where does friction and/or opportunity actually exist? What workflows genuinely drive inefficiency?
Most organizations have to essentially guess. They survey employees, map theoretical workflows, and build automations based on assumptions (or at best, self-reported interviews) about how people work.
The fundamental issue is sequencing: enterprises are designing against problems they can’t accurately observe using traditional methods. Design predates discovery when it should be the other way around.
DIY custom frameworks have the opposite problem: they’re imminently customizable, but at significant cost. Even if someone did accurately identify the best candidates for automation, solving it requires deep domain knowledge of agentic tools and capabilities, plus the know-how to assemble, deploy, and maintain custom workflows in perpetuity (all while those employees have their regular jobs to do).
This approach doesn’t scale across enterprises where every department and team has unique workflows, different tools, and varying levels of technical sophistication. Custom solutions become technical debt faster than they create value.
Off-the-rack solutions scale, but don’t customize. DIY frameworks are indeed custom, but they don’t scale.
A different approach: Intelligent automation based on observed behavior
Leading organizations are flipping the script. Instead of predicting workflows, they’re implementing a Behavioral Agent Automation Platform (BAAP). A BAAP not only observes how people actually work with GenAI, but it can then automatically surface and build automations based on proof, not prediction.
The shift is from hypothesis-driven to data-driven automation. This is why BAAPs represent the future of enterprise agentic AI — they solve the prediction > proof, sequencing, and scaling problems in unison.
The building blocks that make Behavioral Agent Automation possible:
- A secure, horizontal platform for accessing and querying GenAI
- Model-agnostic access and orchestration (so you’re not locked into one foundation model)
- Enterprise-wide data access that feeds into the platform (Slack, email, SharePoint, Drive, CRMs… wherever knowledge actually lives)
- Behavioral observability within the platform itself
- An insights engine capable of interpreting observability data, identifying friction, and surfacing automation candidates proactively
- Governance infrastructure with human-in-the-loop approvals
- Autonomous assembly and deployment of agentic workflows (following human approval)
- Real-time telemetry on what’s working, what’s unused, and where friction persists
- Continuous adaptation so the deployed automations improve over time, automatically
New evaluation criteria
For CIOs, the evaluation criteria are shifting. The question is no longer whether a platform can execute workflows… It’s whether a platform can discover the biggest inefficiencies and solve them automatically.
- Can the platform observe how work actually happens across your fragmented systems?
- Can it identify friction from behavioral signals rather than requiring you to guess?
- Can it assemble and deploy individualized automation at scale?
- Can it do that while also maintaining the governance and control your organization requires?
- And perhaps most importantly, does it reduce the burden on IT teams or simply create a new technical dependency?
Organizations seeing real ROI share a common insight: they stopped trying to predict the future of work and started observing the present reality.
This doesn’t mean abandoning strategic planning or IT governance. It means building automation strategy on evidence rather than assumptions. It means deploying tools that learn from behavior rather than requiring employees to become solution architects.
For details on how Behavioral Agent Automation Platforms are solving the $40 billion enterprise AI value question, you’ll find the deep dive here.
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Source: News