Public debate about artificial intelligence often centers on how smart these systems might become. But for businesses and households, the more important question is practical: can AI systems be trusted to run parts of daily operations? Adoption follows reliability, not intelligence.
The real wedge: Removing coordination friction
Most delays inside organizations are not caused by complex decisions. They come from coordination work: gathering information, checking conditions, updating systems and routing tasks.
Typical cases include deal approvals bouncing between sales, finance and legal, revenue teams syncing contract changes across billing and CRM tools, compliance reviews split between documents and private chats and support teams stitching together logs and dashboards.
These tasks depend on consistency and timing rather than advanced reasoning. This makes them ideal entry points for agents that remove friction through reliable execution.
Modern models already exceed human performance in many analytical tasks. Yet few companies allow AI systems to act independently. The barrier is trust, not capability. Businesses fear silent failure; while a human mistake is visible, an automated mistake can cascade before anyone notices. To build confidence, organizations need clarity on what the agent checked, why it acted, when it escalated and how each step can be audited later.
Intelligence alone does not provide this. Agents require the same trust structures used in aviation, banking, and industrial automation. Here’s how that structure looks, so that agents go from "capable" to "dependable":
- Reliability metrics: accuracy, timing, escalation rates
- Audit trails: full visibility into every action
- Bounded autonomy: clear limits on what the system may execute
- Deterministic fallbacks: predictable behavior when data is missing
- Safe refusal modes: the ability to stop when uncertain
Everyday examples: How real trust looks
The requirements become clearer when imagining daily life with high autonomy. A doctor robot in the emergency room must show what symptoms it captured, what guidelines it followed, when it escalated to a human and how every step is recorded for review. Safety depends on transparency, not perfect intelligence. Automated triage at crowded clinics must apply rules consistently, document reasoning, provide override paths and surface safety metrics.
People trust systems when limits, guardrails and actions are visible. The same principles apply in the workplace.
Take contract amendments: the agent retrieves the contract, checks rules, verifies dates, flags issues, records each step and escalates when information is missing. Another example is customer incident triage: the agent gathers logs, compares them with known problems, applies the playbook and provides engineering with full context instead of disconnected screenshots. In these cases, intelligence supports execution and reliability earns adoption.
There are also points where autonomy must halt and some areas remain human-owned: negotiations, actions with legal exposure, irreversible changes, and domains with ambiguous or contradictory policies. The boundary should be operational risk and not model intelligence.
The directive for builders
Once an agent reliably runs a workflow, it becomes the system that sees real context: exceptions, overrides, conflicting inputs. Most organizations do not capture this information today. This visibility allows agents to expand their role safely over time. Trust grows from the bottom up and agents will earn responsibility by executing real work consistently.
The next competitive edge in AI will not come from the strongest model. It will come from the agent that organizations and households trust to operate workflows end-to-end. Builders should focus on clear operational boundaries, transparent audit logs, predictable escalation rules, strong reliability metrics, and refusal mechanics that prevent silent failure.
The shift in AI will not be driven by IQ. It will be driven by trust.
- Itamar Mula is principal at Hetz Venture.