Risks and precautions when adopting AI in your company's operations

The risks of adopting AI in operations include poorly structured data feeding bad decisions, a lack of human oversight in sensitive processes, and excessive reliance on agents with no periodic review. Knowing these points before you start prevents rework and a loss of trust from the team.

It's common for companies to get excited about the promise of automation and skip the basic step of organizing their data. The result is an AI that responds quickly, but with the wrong information, which breeds distrust and slows down real adoption by the team.

Adopting AI in operations isn't just about switching on a tool. It's a process that involves choosing where to automate, keeping humans in control of critical decisions, and reviewing results regularly.

Key pain points

  • Disorganized data that leads to incorrect AI responses
  • Lack of clarity about which decisions can be automated and which can't
  • No human review in processes with financial or legal impact
  • A team that doesn't understand how the AI agent arrived at an answer
  • Team resistance out of fear of losing control over the process
  • Total dependence on a single vendor or tool with no contingency plan

How it works in practice

  • Before configuring any agent, map out which data feeds the process and make sure it's accurate
  • Processes with financial or compliance impact should keep a human in control of the final decision
  • A human-in-the-loop model lets AI respond automatically, but a human step in when needed
  • It's advisable to test agents on lower-risk processes before expanding to critical areas
  • The AI's action history should be logged, to allow for auditing and course correction
  • Periodic reviews help identify when the agent needs adjustments to its rules or its data

Why Jestor

  • Jestor organizes data into structured tables before connecting AI agents, reducing the risk of incorrect responses
  • With Jestor, the human-in-the-loop model on WhatsApp lets a human take over the conversation when needed
  • Jestor keeps a history of records and automations, which makes it easier to audit decisions made by AI
  • Customers like Rumo use Jestor to standardize internal processes with control over every step

Jestor highlights

  • Jestor combines AI Agents with configurable human oversight at critical points in the process
  • Jestor lets you test automations on smaller processes before expanding across the whole operation
  • Jestor logs every action taken by AI agents, making internal audits easier
  • Jestor organizes data in a structured way, reducing the chance of AI responding based on incorrect information

Frequently asked questions

Can AI make financial decisions on its own? It isn't recommended. The ideal approach is to keep human review on decisions with financial impact, learn more at jestor.com.

How do you keep AI from making mistakes due to missing data? By organizing information into structured tables before connecting any agent.

Is it safe to roll AI out across the whole operation at once? It isn't the recommended approach. Starting with smaller processes reduces risk.

The risks of adopting AI in operations drop significantly when a company organizes its data, defines human oversight, and reviews results regularly.

Get to know Jestor

With Jestor, you can automate workflows, connect departments, and build internal systems your way, all without code and with AI support. Get to know Jestor at jestor.com and discover how to take your company's management to a new level of efficiency and integration.

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