Michigan has always been a place where new technologies collide with real-world industries. From assembly lines and advanced robotics to connected vehicles and digital health, our state’s economy is built on applied innovation. Artificial intelligence is the next wave in that tradition, but it arrives with a growing web of rules, enforcement expectations, and customer demands that every founder and operator needs to understand. This guide translates the fast-moving conversation about AI governance into practical language for Michigan entrepreneurs. It favors clarity over jargon, shows where the law stands today, and explains what smart teams are already doing to build AI products that survive the scrutiny of regulators, partners, and the market.
Michigan does not yet have a single, comprehensive AI statute that covers every system and use case. Instead, companies face a layered framework. Federal agencies set the tone with enforcement under existing laws and policy guidance, while states adopt targeted requirements and experiment with sector-specific rules. Customers and large enterprise partners add their own contractual controls. Standards bodies publish risk-management frameworks that, while voluntary, have quickly become the yardstick for diligence and procurement. Municipal governments, particularly in larger cities, debate AI in public safety and administrative operations, which influences local expectations about transparency and bias mitigation. The result is not a vacuum but a patchwork in which an entrepreneur’s obligations depend on the purpose of the model, the data it uses, the people it affects, and the claims the company makes about what the system can do.
The national baseline begins with a simple proposition: existing consumer-protection, civil-rights, and product-safety laws apply to AI. The Federal Trade Commission has said repeatedly that Section 5 of the FTC Act, which prohibits unfair or deceptive practices, covers misrepresentations about training data, capabilities, performance benchmarks, and risk controls. If a company markets an AI tool as accurate, unbiased, or “human equivalent,” the agency will expect evidence and will look skeptically at disclaimers that bury limitations in fine print. The Equal Employment Opportunity Commission has explained that the use of algorithms in hiring and promotion does not absolve employers of their duty to avoid discrimination. A screening tool that disproportionately disadvantages protected groups can still create liability even if the intention was neutral. The Department of Justice has made similar points in the context of disability rights, emphasizing the need for reasonable accommodation when automated systems interact with applicants or customers. These federal expectations coexist with the White House’s policy direction on safe, secure, and trustworthy AI, which has pushed agencies to tighten guidance, encourage transparency, and align procurement with recognized risk-management practices. For a Michigan startup, all of this means that the governing rule is still “don’t mislead, don’t discriminate, and don’t cut corners on safety,” even if your product uses a model instead of a traditional rules’ engine.
Standards have moved just as quickly. The National Institute of Standards and Technology released the AI Risk Management Framework to give organizations a common language for identifying and prioritizing risks, measuring whether controls are working, and documenting the lifecycle of an AI system from design to decommissioning. Many enterprise buyers now ask vendors to show how their development and deployment practices map to that framework, along with evidence of dataset provenance, evaluation methods, and incident response plans. Insurers and investors also use the framework to structure diligent conversations. While the document is voluntary, its influence is tangible: if your company can explain how it governs data quality, model robustness, security, privacy, and human oversight using the framework’s categories, you will be easier to onboard as a supplier and better positioned when a regulator asks how you manage risk.
The state-level landscape adds another layer. Across the country, legislatures have begun to regulate specific AI risks, including deepfakes in political ads, automated decision systems that affect employment or credit, and the use of biometric identifiers. Some states have enacted comprehensive obligations for “high-risk” AI systems, requiring risk assessments, notices to affected individuals, and mechanisms to contest adverse decisions. Even when those statutes do not apply directly to a Michigan business, they shape national expectations because large customers operate across state lines and adopt the strictest common denominator in their contracts. Entrepreneurs in Michigan will increasingly see requests from out-of-state partners to provide impact assessments, to maintain opt-out flows for certain features, and to retain logs that support auditability. When a deal review team in another state asks for this material, it is rarely optional; the most efficient response is to bake these practices into your product process rather than treat them as ad-hoc paperwork.
Closer to home, Michigan law already governs several pillars that intersect with AI. The state’s consumer-protection norms apply to marketing claims about AI capabilities and to the design of user experiences that could mislead people about whether they are interacting with a bot or a human. Michigan’s identity theft and breach-notification rules require reasonable security for personal information and impose duties when that information is compromised. If an AI system processes or stores personal data about customers or employees, those obligations are in play. Contract law remains the backbone of how businesses allocate risk, which means your data-processing agreements and vendor contracts should spell out how training data is sourced, whether models will be fine-tuned on customer content, what happens to outputs, and how each party will handle security incidents. In many respects, Michigan’s AI posture is pragmatic: the state relies on existing regimes for privacy, cybersecurity, and unfair practices while watching emerging national trends in algorithmic accountability.
Sector by sector, the touchpoints become concrete. In automotive, AI is embedded in advanced driver-assistance systems, battery management, predictive maintenance, and supply-chain optimization. Federal motor vehicle safety standards and guidance on automated driving systems set the baseline, but product-liability doctrine and deceptive-practices law loom large. If your marketing can be read to suggest “self-driving,” you should be ready to prove the system’s limitations were clear and that users received appropriate warnings about operational design domains. For suppliers, the key disciplines are change control, traceability, and post-market monitoring. Documenting datasets, training runs, and evaluation criteria creates an evidentiary record that matters when a customer or a plaintiff’s attorney asks how you decided the model was safe enough to ship. Michigan’s long history with automotive quality systems can be an advantage here: many teams already know how to live with gated releases, layered process audits, and corrective-action workflows. The trick is to apply those familiar patterns to data and models in addition to physical parts.
Healthcare presents a different regulatory texture. AI that supports diagnosis, triage, or personalized treatment may qualify as a medical device, triggering federal submissions and post-market surveillance obligations. Even when a model does not meet the definition of a regulated device, it still works with protected health information, which invokes privacy and security rules. A health-tech startup building in Michigan should expect its customers to demand a risk analysis that covers model performance across patient subpopulations, a plan for monitoring drift, and clear roles for clinician oversight. The ethical dimension is equally important. Patients and providers want to understand where the model fits in the care pathway, what sources of data were used to train it, and how the company responds when a model makes a wrong suggestion. Because trust is the currency of both medicine and startups, teams that invest in evaluation protocols and explainability without overstating what the model can explain find that sales cycles shorten and integration friction declines.
In employment, AI touches recruiting, screening, scheduling, and productivity monitoring. Employers in Michigan remain subject to federal anti-discrimination laws, and several states and cities elsewhere impose disclosure, audit, and notice obligations on automated hiring tools. Even if those extraterritorial rules do not legally bind a Michigan company, they create de facto standards for vendors who sell HR technology nationally. If you build or buy such tools, plan for bias testing that goes beyond a single snapshot, because fairness metrics can fluctuate with small changes in applicant pools. Preserve documentation of evaluation methods, including the choice of metrics, the design of hold-out sets, and the thresholds that trigger human review. Give applicants meaningful ways to request accommodations and to appeal decisions. And train managers to understand the system’s scope rather than treating model scores as ground truth. The quickest path to regulatory trouble is allowing an algorithm to become a black box that no one in the organization can explain.
Privacy and data governance sit at the core of AI risk. A model inherits the sins of its data, and those sins are often preventable with disciplined intake. Michigan companies should maintain an inventory of datasets that captures provenance, licensing, sensitivity, and retention limits. Public web data can be tempting because of its volume, but it is not always free to use and scraping terms matter. Customer data brings its own responsibilities: do your contracts permit training or fine-tuning on that content? Do customers expect you to segregate their data and purge it at offboarding? Do you provide controls that allow a customer to use your service without contributing to shared model improvements? Entrepreneurs who make strong, specific promises about data use backed by technical defaults like opt-out by default or single-tenant fine-tunes for sensitive verticals build credibility and avoid expensive renegotiations. In parallel, security controls need to evolve to meet AI-specific threats, including model inversion, poisoning, prompt injection, and abuse of data-exfiltration channels that ride through LLM connectors. The more your product exposes a general-purpose model to untrusted inputs, the more you should invest in sandboxing, content filters, and outbound guards that stop the model from revealing secrets even when a prompt tries to trick it.
Transparency is no longer a nice-to-have; it is becoming a de facto market requirement. Users want to know when they are interacting with an automated system, and in many contexts, they want to know why the system reached a particular output. Achieving meaningful transparency requires more than posting a policy page. Within the product, you should label AI features in plain language, describe the sources of data that informed an answer when feasible, and acknowledge uncertainty in ways that help the user decide what to do next. For high-stakes outputs those that affect access to services, employment, or health you should also provide routes to human review and channels to contest an outcome. The tone matters: transparency that reads like a legal disclaimer erodes trust, while transparency that reads like a partner explaining limitations builds it.
Contracting is where many AI governance questions are finally settled. The customer’s paper will ask whether you indemnify for third-party IP claims, whether you exclude training on their data, whether you commit to uptime and response times for model degradation incidents, and whether you maintain certain certifications or audits. A balanced approach is to offer a clear, narrow IP indemnity that covers allegations arising from your training data and model outputs, paired with warranties that you will not train on customer content unless they opt in. If your product depends on third-party models, say so plainly and pass through their restrictions. On the vendor side, impose similar obligations on your own suppliers and seek transparency into their evaluation methods. The goal is alignment across the chain so that your promises to customers match what your vendors promise to you.
Liability and insurance have followed the same arc. Traditional policies do not cleanly map to AI risks, leading some carriers to exclude algorithmic discrimination or IP claims tied to training data. Others are experimenting with endorsements that cover specific harms if the insured maintains defined controls. If you are negotiating coverage, come prepared with your governance artifacts: risk assessments mapped to a recognized framework, incident playbooks, red-team reports, and post-mortem templates. These materials demonstrate maturity and often improve pricing. Even if you are not seeking specialized coverage, the exercise of assembling them will strengthen sales and compliance.
For founders who want a practical playbook, the starting point is a simple one: treat AI like any other safety-critical or customer-impacting technology. Begin with a written statement of purpose for each system that captures intended use, out-of-scope use, and potential harms. Use that statement to drive a pre-deployment assessment that weighs benefits against risks and documents the controls you will rely on. Align product requirements with those controls so that mitigations are not theoretical. Build evaluation into your lifecycle, not as a one-time event but as a repeated process that tests the system on fresh data and adversarial prompts. Keep a changelog that links model versions to datasets and configuration. Publish a short, human-readable model card or feature note that sets expectations for users. Train your team to handle incidents, including security violations and harmful outputs, and rehearse those drills. When customers ask for your AI policy, give them something real: a brief, plain-English summary that connects your practices to the risks that matter in their industry.
The compliance culture you cultivate will shape the company’s trajectory in Michigan’s most AI-intensive sectors. In mobility, that culture looks like conservative claims and aggressive monitoring. In health, it looks like clinical oversight and data minimization. In manufacturing, it looks like sensor integrity, change management, and safeguards that prevent an optimization model from compromising worker safety. In financial services, it looks like audit trails, fair-lending testing, and channels for consumers to dispute decisions. Across all of these, it looks like leadership that understands when to slow down deployment because a risk is not yet under control. The companies that thrive are not the ones that never make mistakes, but the ones that discover issues early, fix them quickly, and document what they learned.
Michigan’s public sector, particularly in larger cities, is also shaping expectations through procurement and policy debates. When a city considers tools for fraud detection, permitting, or public safety, the conversation inevitably turns to bias, transparency, and due process. Vendors that provide clear documentation, give agencies control over thresholds and audit logs, and accept sensible public reporting find they are more competitive. Even if you sell only to private customers, those norms bleed into the broader market. They also foster a local ecosystem in which startups, universities, and industry groups collaborate on evaluation benchmarks that reflect Michigan’s demographics and economic realities.
Cross-border data movement is another operational reality for Michigan companies with customers or suppliers in Canada and the European Union. If your product handles personal information from those jurisdictions, you must account for international transfer mechanisms and the stricter privacy regimes they impose. Many teams solve this by deploying regional data stores, constraining training to locally sourced datasets, and limiting who can access logs and telemetry. These steps are not simply compliance checkboxes; they also reduce the blast radius of a breach and increase resilience against supply-chain incidents that originate overseas.
Funding and incentives will continue to matter as the state attracts AI investment. Public-private partnerships, research grants, and workforce programs increasingly include language about responsible AI. Applicants who can show concrete governance practices, diversity in data curation, and community engagement in product testing often earn an edge. Investors mirror this trend by asking more pointed diligence questions about model provenance and safety. The earlier a founder can answer those questions with confidence and with living documents instead of slideware the easier it becomes to raise and to recruit.
Looking forward, the direction of travel is clear even if every detail is not. Federal agencies will keep enforcing truth-in-advertising and nondiscrimination laws against AI claims. National standards will iterate, and enterprise buyers will fold those iterations into their procurement checklists. States will continue to legislate around high-risk use cases, producing a compliance floor that rises year by year. Michigan will adapt its own laws where necessary while relying on its strong consumer-protection and data-security backbone. None of this should deter Michigan entrepreneurs. It should encourage them to do what local builders have done for a century: engineer with discipline, communicate with candor, and treat safety as a competitive feature rather than a drag on speed.
If you are just starting out, begin with three internal commitments and let everything else flow from them. First, commit to knowing your data by cataloging sources, licenses, and sensitivities before you train. Second, commit to testing like your customers’ lawyers will, which means adversarial prompts, stress scenarios, and bias checks that reflect your actual users. Third, commit to telling the truth about what your system can and cannot do, and then design your user interface so that people can act on that truth. These habits are simple, but they compound. They lead to fewer surprises, faster sales, and products that withstand scrutiny when something inevitably goes wrong. In the Great Lakes State, where industries run on trust and precision, that is not just good compliance; it is good business.
Finally, remember that governance is not a one-time project. Models drift, features expand, and customers push products into new territories. The companies that endure treat AI governance as a living system with owners, metrics, and budgets. They review incidents without blame, fix root causes, and revise their controls. They engage peers, universities, and community groups to learn where their models fail and how to improve them. And they accept that regulation will evolve—not to end innovation, but to channel it toward outcomes that people can rely on. That is the Michigan way: build things that last and prove they are safe enough to carry people’s hopes as well as their data.
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Sources:
1- NIST AI Risk Management Framework, National Institute of Standards and Technology, Version 1.0 and subsequent updates. https://www.nist.gov/itl/ai-risk-management-framework
2- Executive Order 14110 on Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence, The White House, October 2023 and agency implementation guidance. https://www.federalregister.gov/documents/2023/11/01/2023-24283/safe-secure-and-trustworthy-development-and-use-of-artificial-intelligence
3- Federal Trade Commission business guidance on AI, including enforcement statements regarding unfair or deceptive practices in AI claims. https://www.ftc.gov/news-events/news/press-releases/2024/09/ftc-announces-crackdown-deceptive-ai-claims-schemes
4– EEOC technical assistance on the use of software, algorithms, and artificial intelligence in employment selection procedures. www.eeoc.gov/sites/default/files/2024-04/20240429_What%20is%20the%20EEOCs%20role%20in%20AI.pdf
5– Michigan Identity Theft Protection Act, MCL 445.61 et seq., and related state data-security and breach-notification requirements. www.legislature.mi.gov/documents/mcl/pdf/mcl-act-452-of-2004.pdf
This publication is for general informational purposes and does not constitute legal advice. Reading it does not create an attorney-client relationship. You should consult counsel for advice on your specific circumstances.