AI
Blog
Browse 24 articles on AI products, agents and business implementation. We cover task definition, answer verification and working with data. Educational examples are identified separately from client case studies.
Updated
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Teaching a Retail Worker to Audit Sales Data With AI
A hands-on exercise: a sales spreadsheet with seeded errors, an AI report, and a peer review of the instructions.
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Teaching Marketing to Check AI: A Six-Review Exercise
How to train a team to cross-check AI output against real facts, and how to catch invented quotes, promises, and numbers.
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How to Train a Bank Team on AI and Actually Verify the Skill
How to pick a training program for a bank unit and check employee skill without connecting AI to customer accounts.
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Iva: a personal assistant with memory, running on your server
What Iva actually gives a business owner and what to prepare before connecting real work data. Based on code v0.4.10.
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How to Roll Out AI at Work Without Resistance or Meetings: Five Moves
How to bring AI into your company when colleagues push back: five moves where everyone wins immediately and each next step costs less.
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Cumulative Flow Diagram: Why Your Team Keeps Missing Deadlines
Why teams fall behind even at full capacity - and how to see where work stalls using a cumulative flow diagram.
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How AI Can Find the Real Reasons Customers Are Calling
How to use AI to sort calls and messages by three questions, find root causes, and know what to fix in your processes.
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A Daily Briefing Instead of Micromanagement: How Leaders Stay Informed Without Checking on Everyone
How a manager can see what the team is doing every morning without rounds or manual checks: a briefing template, sources and access rules.
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Where Leads Disappear Between the Form and Payment - and How to Measure It
How to find which step loses leads or payments: a loss-rate formula and a ten-point checklist.
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How to Calculate What a Task Is Worth Before Work Starts
Simple arithmetic that shows the maximum possible return on any feature, campaign, or project - before the team spends weeks on it.
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An AI Agent in a Shared Chat: What to Set Up Before You Launch
What to configure in participation rules, data access, and task management before an AI agent joins your team chat.
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How to Brief an AI Agent on a Work Project
How to define the output, source data, constraints and checkpoints so an AI agent completes a multi-step task without losing control.
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How an AI agent differs from a chatbot - and when a bot is enough
When rules and integrations are enough, and when you need a system with tools, an action log, and a human handoff point.
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Verifiable AI Answers From Company Documents
How to build document answers with verifiable sources, access controls and a clear response when the required information is missing.
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AI Parses a Complex Excel File and Builds a Dashboard
How to brief an AI agent on a multi-sheet Excel workbook, then check formulas, source figures and the finished dashboard.
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How to Run Your Team's First AI Training Session on Real Work Tasks
A step-by-step breakdown of a first AI session for teams: real tasks, result verification, and reusable templates you keep after the session.
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How to Turn a Repeatable Task into an AI Agent Skill
How to document a repeatable process, package it as an AI agent skill and test the instruction with a colleague.
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What Clients Expect from AI Vendors and How to Check Their Work
YC and Sequoia describe a new company type: clients buy a finished result, not a tool or hourly vendor work.
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AI Implementation: How to Choose a Process and Test the First Version
From reviewing a process and its data to the first version, integration and team training. An example sequence and mistakes to check.
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How to Organize AI Work in Your Company and Check the Results
A shared environment for AI agents: data sources, access rights, memory and answer checks. We explain eight parts of a company AI harness.
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FDE (Forward Deployed Engineers) turn business pain into product. Where the model breaks
A breakdown of Forward Deployed Engineering - when an embedded engineer builds a real product, and when it's just costly outsourcing. Majento's view.
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Six Steps to AI Transformation: How Companies of Any Size Can Rebuild Work Around Agents
The Majento framework: six concrete steps to transform how your company works with AI agents, from a startup to an enterprise.
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Access Rights in the Corporate Brain: Why a File's ACL No Longer Protects What AI Already Knows
The unit of access control has shifted from object to claim. A breakdown for teams rolling out enterprise AI search.
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88% of Companies Use AI. What Sets High Performers Apart
AI use and business impact are different measures. McKinsey findings and practical steps for assessing tasks, training teams and deploying agents.
Choose a starting point
The articles below cover different parts of the same route: preparing a team, defining a task, checking source material and building a working system. Start with the problem you can describe and verify today.
| Your task | Start here |
|---|---|
| Prepare the first practical team session | First AI team workshop |
| Describe an agent's inputs, permissions and review steps | AI-agent task brief |
| Check answers against company documents | Verifiable answers from company documents |
| Review a workbook before building a report | AI and Excel |
Connect reading to a project
Keep one workflow, a responsible owner and representative examples in view. Record what the current process produces and how an employee checks it. A useful instruction may be enough; a recurring task with available data can become a separate implementation project.
See team training, the training example on fictional data and AI implementation for the next step. The company page separates Majento's services, own products and team background.
Primary references
For the risk and review questions discussed in these guides, read the NIST AI Risk Management Framework and OWASP guidance for AI application security. For platform-specific interfaces, use the official Telegram bot documentation and Mini App documentation.