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AI Tools Implementation

Build AI into the team's work. Scenario, integration, pilot, training and quality control, so the pilot becomes everyday work.

Sound familiar?AI tools are used manually and in scattered ways, The scenario has no owner and no quality criterion, Pilots don't turn into everyday work

AI and Technology

How we work

1

Scenario and roles

Who does what, and where a person checks the result.

2

Integration and rules

Which systems the tool is built into and how errors are handled.

3

Pilot on a limited area

Recording every case where a person had to step in.

4

Training and quality control

Spot checks and a response plan when quality drops.

Sound familiar?

  • AI tools are used manually and in scattered ways.
  • The scenario has no owner and no quality criterion.
  • Pilots don't turn into everyday work.

What usually lies behind it

Implementing AI takes more than choosing a model. It also takes a process, data, an interface, controls and training. A pilot without a stop criterion named in advance almost always ends with "promising overall" and never goes into production.

What you get

  • AI built into a working process.

  • Named limits for automated decisions.

  • A total cost of ownership estimate, including control.

The cost of waiting

If nothing changes

  1. 01

    The pilot will stay an experiment.

  2. 02

    Customers or the manager will be the ones to find the errors.

  3. 03

    The team will lose trust in the tool.

When you don't need this service

  • The scenario hasn't been chosen or tested. AI consulting comes first.
  • The process the AI is being built into isn't documented.

How to tell it worked

Effect against the baseline

time, cost, number of errors.

Human interventions

their share and trend.

Usage

whether people work with the tool every day.

Questions

How long does a pilot take?

Four to six weeks. Longer means the task was chosen too large.

Who checks the result?

A named person in the process throughout the pilot, and after that by sampling.

What about confidential data?

Before the start, decide which data can be passed to the tool and where it's processed.

Related

Services: AI Consulting · AI in Workflows · Process Automation

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When the model stops working

From the inside, it's hard to tell a temporary downturn from an exhausted model, and a mistake either way is expensive. This is a set of signs that help tell them apart.

Read: When the model stops working

A working AI scenario always has an owner and a verifiable result.