By Contributor,John Winsor

Copyright forbes

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When business leaders ask me where AI is taking work, I remind them it’s not a choice between control and chaos. It’s about learning to orchestrate both. Two operating models are emerging that appear to stand in tension but actually depend on each other. Agentic models are autonomous software workers that compress coordination costs and execute with speed inside the enterprise. Open models are boundary-spanning systems that draw on ingenuity, capacity, and knowledge from beyond the firm. The instinct is to choose one. The reality is that the future belongs to companies that embrace both, running autonomy at the core and openness at the edge.

Consider agentic models. An insurance executive told me their adjusters were spending thirty minutes on every claim, most of it clerical. The company deployed an AI agent to process intake data, classify claims, and flag anomalies for human review. Processing time dropped from thirty minutes to three. Accuracy improved. Employee stress declined. This is the power of an agentic model, small autonomous AI workers embedded in workflows under clear guardrails. They are not just tools but teammates, powerful and tireless, though still dependent on human guidance.

Agentic models succeed when their scope is clear, when observability makes every action traceable, and when governance ensures permissions and kill switches. Leaders need to measure outcomes that matter, such as error detection and escalation rates, not just throughput. The payoff is faster cycles and internal agility. The risk is agent sprawl, dozens of unsupervised systems making decisions without accountability. That is not a productivity strategy. It is unmanaged risk.

Open models expand possibilities outside the firm. They orchestrate external contributors, from freelancers to partners to communities and customers. Cybersecurity is a vivid example. No company can employ enough experts to anticipate every vulnerability. That is why the most resilient firms run bug bounty programs, inviting thousands of contributors worldwide to hunt for flaws. Rewards go only to validated results, and resilience improves by tapping talent the firm could never afford to hire.

Open models work when interfaces are clear, when incentives are aligned, when licensing and privacy frameworks are in place, and when communities are well managed. They unlock capacity and diversity of ideas that cannot be replicated internally. When done poorly they become open theater, flashy initiatives with no connection to business value.

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The interplay between the two models can be seen through a simple two-by-two grid that considers scope and sourcing. A closed point solution might be a pilot agent reviewing contracts. A closed system might be an autonomous compliance function. An open point solution might be a crowdsourced R&D sprint. An open system might be a shared data commons across partners. The strongest organizations move across the grid, blending internal autonomy with external abundance.

The mistake leaders make is treating this as a binary choice. Some double down on agentic pilots and neglect the ecosystem. Others launch open challenges with no internal systems to absorb the results. Both approaches disappoint. The value comes from integration. A global pharmaceutical company built an agentic system to scan millions of medical publications for drug interactions. Instead of keeping the results in-house, they opened them to external researchers. Agents handled scale, experts provided interpretation, and R&D timelines were cut nearly in half.

Building a hybrid model requires discipline. Leaders should map the work, identifying which steps benefit from speed and which require diversity. They should design interfaces so agents and external contributors can plug into the same system. Governance must be clear, with accountability for agent decisions and external contributions. And they should close the loop, ensuring that external inputs flow into internal systems and agent outputs fuel challenges worth opening up.

Governance is the connective tissue between these models. Agentic systems require traceability and clear kill-switch protocols. Open systems require clarity around intellectual property and licensing. Both must align with evolving ethical standards and regulations. Autonomy and openness sound like opposites, yet both depend on trust frameworks to scale.

For leaders wondering how to begin without creating chaos, a staged approach works best. In the first two weeks, select a high-volume workflow and define scope and guardrails. Over the next month, launch an agentic pilot with observability built in. In weeks seven through ten, open part of that workflow to a bounded external challenge. By the end of the quarter, integrate contributions, measure results, and decide whether to scale, kill, or iterate. This avoids isolated pilots and ensures agents and external contributors are designed to complement each other from the start.

The greatest risk today is not failure but success on the wrong terms, scaling internal autonomy without external ingenuity or running external programs without the systems to absorb them. Both leave value on the table. The organizations that win in 2025 will not ask whether to choose agentic or open. They will answer both. Autonomy at the core and abundance at the edge. That is the operating system of the future, and the only question is whether you are ready to run it.

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