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RIGHT FIT ADVISORS / MICHIGAN

Generative AI assistants
in Michigan.

A project assistant can help locate decisions and document versions across an approved project collection. For teams in Michigan handling engineering changes across a supplier network, use a configuration change, supplier acknowledgement and production dependency to make the starting point concrete. RFA scopes generative AI assistants in Michigan around the task, review requirements and people responsible for the result.

Discuss your Michigan generative ai assistants needs ↗

YOUR OBJECTIVE COMES FIRST

Project document navigation
Give people an assistant whose work they can inspect.

Michigan’s economic development corporation lists mobility, advanced manufacturing, engineering, life sciences, technology and professional services among its core industries. A useful scoping example is engineering changes across a supplier network. Follow a configuration change, supplier acknowledgement and production dependency through the work with engineering, supplier quality and program management. A change accepted by one supplier may leave another working from an older specification. How is acknowledgement tracked across every affected supplier?

Michigan Economic Development Corporation: industry context ↗
01

Choose a narrow job for the assistant

Define the user, the task and the first useful output. A research draft, a support response and a summary of an account require different sources and checks. Describe what the assistant must decline, defer or hand to a person instead of promising a general answer to every question.

02

Keep source access meaningful

Identify document owners, current versions and existing permissions. For retrieval-based tools, apply access controls before content enters the answer-generation step. Decide how superseded documents are removed or marked, and how reviewers can inspect the relevant source passage. Working example: A decision brief with document and revision references.

03

Evaluate answers beyond their wording

Test representative questions against expert-reviewed evidence. Examine factual support, missing context, citation accuracy and the handling of questions the sources cannot answer. An answer can sound convincing while omitting the condition that makes it useful or safe. For engineering changes across a supplier network, agree how engineering, supplier quality and program management contribute where those functions are involved.

04

Decide which actions need approval

Reading an approved record is different from modifying it or sending a message. Scope tool access and confirmation requirements by action. Preserve a clear draft state and activity record so users understand what the assistant prepared and what has actually changed. Review check: Tests locate the applicable version or flag uncertainty.

THE RIGHT FIT APPROACH

From your objective
to a scoped engagement.

01

Collect questions and approved examples

Review the task with the people doing it. Gather permitted source material, a representative question set and examples of acceptable and unacceptable outputs. Bring a decision brief with document and revision references. Test the scope against this constraint: A later revision can change the meaning of an earlier instruction.

02

Build a reviewable assistant workflow

Connect the selected sources and supported tools. Test permissions, missing evidence, citations and action boundaries alongside the ordinary user experience.

03

Release with evaluation and feedback

Give users correction guidance and an accountable owner for source updates. Recheck representative questions when prompts, models, tools or document collections change.

PLANNING THE ENGAGEMENT

Questions about support in Michigan.

What if project documents disagree?

Present the conflict and route it to the designated owner rather than choose silently.

Does an assistant need to train a custom model?

Not necessarily. An assessment may identify an existing model with retrieval and supported tools as a better fit. Decide based on the task, evidence, permissions and maintenance needs rather than treating model training as the default.

How do we prevent made-up answers?

No design should promise the complete absence of errors. Narrow the task, use relevant sources, test unsupported questions and provide review and escalation. Evaluate whether answers are supported, not just whether they include a citation.

Can the assistant update our CRM or send email?

These are distinct permissions and implementation decisions. Define the exact fields or messages involved, approval requirements and activity logging. Start with reviewable drafts when the user needs to confirm an external action.

LET’S FIND YOUR RIGHT FIT

Discuss your Michigan generative ai assistants needs.

Share the work ahead, your location and preferred timing. We will discuss fit, availability and the next step together.

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