Artificial intelligence has moved from the margins of innovation into the middle of day-to-day business. Michigan companies are already using machine learning to score leads, flag fraud, route trucks, predict maintenance needs, summarize customer service tickets, and generate marketing copy. The tools feel like magic because, at their best, they are fast, scalable, and accurate. But the legal obligations and risks do not disappear just because an algorithm sits between your staff and the decision. They evolve. For Michigan business owners, the immediate challenge is less about keeping up with every new model and more about building a governance foundation that can adapt as technology and rules change. What follows is a practical, Michigan-grounded orientation to the AI legal landscape what applies already, what is emerging, and what you can do now to stay compliant while still moving quickly.

Please note this blog post should be used for learning and illustrative purposes. It is not a substitute for consultation with an attorney with expertise in this area. If you have questions about a specific legal issue, we always recommend that you consult an attorney to discuss the particulars of your case.


There is no single, comprehensive “AI law” in Michigan or at the federal level that neatly answers every question. Instead, the obligations that matter to a Michigan business arise from a patchwork of existing laws, agency guidance, industry standards, and contracts. That patchwork can feel frustrating until you realize that most of what regulators expect from AI systems is simply a translation of familiar duties into a new context: do not deceive customers, do not discriminate in employment or lending, safeguard personal information, notify people when a breach occurs, and keep adequate records to show that your process is reasonable. Michigan companies have lived under those expectations for years. The difference with AI is that you need to demonstrate how models are trained, how they are validated, how their outputs are used, and how humans intervene when the system is wrong. Thinking in those terms aligns day-to-day engineering

Michigan does not currently have a comprehensive consumer privacy statute on par with the newest state privacy laws, but several existing Michigan laws already interact with AI deployments. The Michigan Identity Theft Protection Act imposes data breach notification and security obligations that matter any time your models ingest or generate information tied to identifiable people. If your systems collect Social Security numbers for identity verification, the Michigan Social Security Number Privacy Act restricts how those numbers can be displayed, shared, and stored. The Michigan Consumer Protection Act, though carved out in places for regulated businesses, still prohibits deceptive or unfair practices, which maps directly onto AI-enabled marketing claims, personalized offers, and automated customer support. Taken together, these statutes do not mention algorithms by name, but they set guardrails around the personal data that fuels AI and the marketing that promotes it. If your model uses or reveals personal information, assume Michigan’s general security and consumer protection duties apply to the pipeline, not just the database.

Even if your business serves only Michigan customers, federal regulators remain the most important sources of AI expectations. The Federal Trade Commission enforces bans on unfair and deceptive acts, which reach false claims about what a model can do, black box scoring that harms consumers, or data practices that exceed what people reasonably expect. The Equal Employment Opportunity Commission has clarified that automated screening tools do not insulate companies from liability if those tools disproportionately screen out protected groups or if they are not validated like other selection procedures. The Consumer Financial Protection Bureau has warned lenders that AI-driven credit decisions must still yield specific, accurate reasons for adverse actions and must avoid discrimination under the Equal Credit Opportunity Act. If your product operates in health or finance, HIPAA and the Gramm-Leach-Bliley Act ride along, constraining what data you feed into training, fine-tuning, and inference. Those federal obligations are not new, but they become newly urgent once decisions are scaled up by an algorithm that can make a wrong move at industrial speed.

In the absence of a single statute, the federal government has supplied a blueprint for what responsible AI looks like in practice. The White House Executive Order on Safe, Secure, and Trustworthy Artificial Intelligence sets out high-level expectations around safety testing, transparency, and the management of sensitive capabilities. The National Institute of Standards and Technology’s AI Risk Management Framework turns those aspirations into a practical, voluntary standard, complete with lifecycle controls for mapping risks, measuring system behavior, and managing residual issues. While neither the Executive Order nor the NIST framework is a Michigan law, both shape what regulators and courts will view as “reasonable” governance. For a Michigan company trying to operationalize AI, adopting NIST’s vocabulary and structure can help translate legal risk into concrete engineering and process requirements.
Michigan employers have embraced AI to sift résumés, analyze video interviews, and score applicants for “fit.” The legal burden here is straightforward even if technology is not: automated decision-making does not excuse discrimination. Under federal law enforced by the EEOC, applicants must be evaluated in a way that avoids disparate impact unless a practice is job-related and consistent with business necessity. Michigan’s own civil rights protections  which track and, in some respects, exceed federal prohibitions pull in the same direction. If you use a vendor tool to screen candidates, the “set it and forget it” posture is dangerous. You need to understand what data the tool uses, how it was validated, and how your own hiring flows might amplify bias. You should be prepared to conduct periodic adverse impact analyses, keep records of your validation work, and provide accommodations when automated interviews disadvantage candidates with disabilities. The key is not to outlaw automation but to insist on the same rigor you would bring to any selection device that materially affects who gets hired, promoted, or paid.

Michigan’s consumer protection principles make a simple demand of companies that deploy AI in marketing: say what you do, do what you say, and be able to prove it. If you advertise an AI-enhanced feature, those statements become promises that the FTC and the Michigan Attorney General can hold you to. Overclaiming accuracy, omitting limitations, or implying an endorsement by “AI” that does not exist are classic examples of deceptive practices. The safest approach is to build your marketing content on top of a documented capability statement drafted in plain language for non-technical readers. That statement should be honest about training data, the nature of the task, typical failure modes, and the human oversight that surrounds the feature. Your customer-facing copy can still be crisp and persuasive, but it should never outrun what your internal documentation can support.

The most difficult legal questions arise before the model ever makes a prediction. If you are collecting data to train or fine-tune a model, you need to map the lawful basis for that collection and the compatibility of the new use. Contracts, privacy notices, and consent records matter because a training use is not always inside the scope of the permissions you already have. If the original purpose was to provide a service to a user, repurposing that data to improve a general model may require a clear notice and a route to opt out. Even in the absence of a single Michigan privacy statute, the combination of federal unfairness standards and Michigan’s expectations around honest data practices push you toward transparency. The more sensitive the category health, finance, precise location, children’s data the stronger the case for explicit permission and technical separation between the production system and the training pipeline.

Most Michigan businesses will not build large models from scratch; they will procure models as a service, bundle third-party APIs, or run open-source models on their own infrastructure. That makes vendor management central to AI compliance. Your contracts should spell out what data flows where, whether prompts and outputs are retained for training, who owns fine-tuned weights, and what happens if a model produces a harmful or infringing output. You will want representations around training data provenance, rights to use, and the absence of code or data that violates privacy or intellectual property. Indemnities should be calibrated to the risks you actually face, with specific attention to copyright claims over generated outputs and privacy claims over training corpora. Beyond paper, insist on audit-friendly logs, a change-management process for material model updates, and a right to information about significant performance regressions. The contract is not a substitute for governance, but it is the best leverage you have over an AI supply chain you do not fully control.

AI changes how intellectual property is created, but it does not erase the underlying rules. In the United States, copyright protects human authorship, which means purely machine-generated material is not registrable on its own. That does not make the content free to use without consequence; a business can still own the economic value of outputs through contract, trade secret, and compilation rights, but copyright turns on the human contribution. When your staff uses generative models to produce marketing assets, documentation, or code, you should develop a policy that describes the required level of human creative control and the disclosures you will make in any copyright registration. At the same time, the unresolved question of training data continues to produce litigation and policy debates. Until the dust settles, the safest operational stance is to prefer vendors who are transparent about their training sources, who offer opt-out controls, who provide indemnities that match your risk profile, and who give you technical tools to filter or trace outputs to reduce the chance of reproducing protected content.

Security obligations are stable even when the technology is not. If your AI system touches personal information about Michigan residents, the expectation that you implement reasonable security safeguards applies to the model and the surrounding infrastructure. Threats are not purely external. Prompt injection, data poisoning, and model extraction attacks are now part of the everyday threat model. In practice, that means you should treat prompts and system instructions as untrusted inputs, enforce strong content filters at input and output, and strictly segregate secrets and personal data from any context that the model can see. On the reliability side, remember that an AI failure rarely looks like a server crash; it looks like confident nonsense, subtle bias, or a one-off hallucination that makes it into production because the test set was too easy. You can reduce that risk by adopting automatic evaluation harnesses with real-world test cases, maintaining model cards that catalog known behaviors, and using canary deployments that limit the blast radius of a bad update. When something does go wrong, a Michigan breach notification analysis should be part of your incident response routine, because a model that reveals private information can trigger the same duties as a lost laptop.

You can strike a balance between useful disclosure and operational secrecy by following a “no surprises” rule. Customers should not be blindsided to learn that a chatbot, not a human, handled their complaint, or that pricing or credit terms were shaped by an algorithm. Employees should not discover after the fact that their keystrokes or voice are being analyzed by a model to measure productivity. Clear, layered notices short at the point of interaction with a link to deeper information in your policy offer predictability without overwhelming people. In settings that affect legal rights or access to services, such as lending decisions, tenant screening, or hiring, go further: provide adverse action reasons that are specific enough to be meaningful and an appeal route that reaches a human with actual authority to fix mistakes. These are not just best practices; they are anchors in existing federal and state expectations around fairness and transparency.

A hallmark of defensible AI governance is that you can show your work. Keep a contemporaneous record of what model you used, what version, how you configured it, what data flowed through it, how it was evaluated, and what your monitoring surfaced over time. Store validation plans and results in a way that allows you to retrieve them quickly when an investigation or lawsuit appears. If the system materially affects customers or employees, preserve documentation of bias testing, human-in-the-loop controls, and any exceptions granted by leadership. This kind of recordkeeping feels burdensome at first, but it pays dividends when you need to explain a decision to a regulator, persuade a judge that your process was reasonable, or simply roll back to a known-good configuration after an update misbehaves.

Even a local manufacturer or services firm can trip into international compliance if data or models move across borders. European customers, for example, trigger the General Data Protection Regulation, which imposes strict requirements on automated decision-making and cross-border data transfers. The newly adopted EU AI Act layers on top of GDPR with risk-tier obligations that will likely influence global vendor contracts and product design even for companies without a direct EU presence. If you sell into Canada or the United Kingdom, analogous privacy regimes will apply. The practical takeaway is simple: if you run global workloads or serve international customers, build your governance program to the strictest regime you face, then cascade that discipline down. That approach minimizes the risk that you need to maintain divergent engineering practices for different markets and future-proofs you as other jurisdictions emulate the early movers.

Turning principles into a sustainable practice starts with inventory. List every place AI touches your business, including pilots and shadow IT. For each use case, capture who owns it, what model is in play, what data it sees, what decision it influences, and what regulatory regimes it implicates. Pair that map with a lightweight intake process for new projects so your inventory stays current. With visibility in hand, create an approval pathway proportionate to risk. Low-risk uses, like internal text summarization with scrubbed data, might require only a basic review. Higher-risk cases anything customer-facing, employment-related, or touching sensitive data should go through a structured assessment modeled on NIST’s AI risk framework. That assessment asks what could go wrong, how likely it is, what harm would look like, and what controls can reduce the risk. If you adopt this rhythm, you will find that your legal, security, and engineering teams start to speak a common language about risk and tradeoffs.

An AI policy should be specific enough to guide behavior but short enough to read. Describe approved tools, banned practices, and the red-flag scenarios that trigger a formal review. Set rules for handling confidential information, customer data, and secrets in prompts. Clarify the level of human oversight required before outputs go out the door. Explain what employees should do when a model behaves badly and where to report incidents. Pair the policy with focused training that uses your own workflows as examples, not generic hypotheticals, and refresh it regularly as your toolset changes. Most policy failures stem from mismatch between paper and reality; keep the two in sync and employees will actually use the system rather than route around it.

Audits and investigations are easier when you anticipate them. Decide now how you will demonstrate compliance to a skeptical outsider. For external audits, that may mean gathering SOC 2 reports from vendors, retaining independent validation of high-risk models, and centralizing your policy, training, and assessment records. For government investigations, prepare a playbook that identifies who will handle inquiries, what your privilege boundaries are, and how you will compile documentation without freezing development. Litigation poses a different threat profile: plaintiffs’ lawyers will test AI-related claims in advertising, employment, privacy, and product liability. You can blunt those risks by aligning your marketing to provable capabilities, validating employment tools, limiting retention of sensitive data used for training, and avoiding opaque decisioning in safety-critical applications. A measured approach today will reduce the stress of a subpoena tomorrow.

If you are a smaller Michigan company, the governance structures described above may sound like a heavy lift. The point is not to build a sprawling bureaucracy. It is to right-size your controls to the impact and sensitivity of the AI you actually use. A one-page inventory, a two-page policy, a simple risk intake form, and a standing half-hour review meeting each month may be enough to keep you safe, especially if your uses are internal and low risk. Lean on vendors for security and compliance artifacts, and favor tools that provide privacy-preserving settings by default. The goal is to avoid the twin errors of neglect and overkill: do enough to show your process was thoughtful and proportionate, but do not paralyze the teams that need to experiment to find value.

Technology and law set the boundaries, but culture determines how your organization behaves. When leaders insist on transparency about AI use, listen carefully to customers and employees, and make space for red-team exercises that surface uncomfortable truths, compliance becomes less of a box-checking exercise and more of a shared habit. Reward teams that surface risks early rather than punishing them for slowing down a release. Encourage product managers to include legal and security partners as design collaborators rather than gatekeepers. Over time, that posture flips the script: you stop asking whether compliance will block innovation and start asking how governance can accelerate it by revealing dead ends sooner and earning trust with customers who care about how your systems work.

The next two years will bring more, not fewer, AI obligations. Federal agencies will continue to publish guidance that translates general unfairness and discrimination principles into AI-specific examples. Congress may not enact a broad privacy or AI law soon, but sector-specific bills will continue to appear. States will experiment with disclosure rules for AI-generated political content, automated decision notices in employment, and safety standards for high-risk applications. Internationally, the EU AI Act will begin to take effect, and vendors will adjust their products to satisfy its transparency, data, and testing requirements. For Michigan businesses, the best response is not to chase every headline but to keep investing in the foundations: data discipline, vendor contracts that give you leverage, documentation you can defend, and a culture that treats AI as a powerful tool to be used carefully rather than a magical excuse to skip the basics.

If you are looking for a concrete way to begin, start by having one cross-functional meeting where product, legal, security, data, and HR map every AI use in the business, however small. From that discussion, draft a simple policy that reflects how your people actually work, not how you wish they would. Select one higher-risk use case and run it through a structured assessment, writing down the harms you worry about and the controls you will apply. Update your vendor contracts to cover training data, output ownership, and indemnities. Configure model and application logs so you can reconstruct decisions. Train your staff with your real examples. Schedule a quarterly review to refresh the inventory and iterate on the policy. None of this requires a new department or a fleet of consultants. It requires attention and discipline applied to the places where AI touches your customers and your employees.

AI will not wait for perfect laws, and your competitors will not wait for perfect clarity. But you do not need perfect clarity to act responsibly. Michigan’s existing privacy, security, and consumer protection expectations already give you most of the guardrails you need, and federal regulators have made plain that “AI-powered” is not a shield against old duties. If you treat AI as an extension of your existing obligations documented, validated, monitored, and explained you can move quickly without courting avoidable risk. In the end, the businesses that win with AI will be the ones that match velocity with judgment: fast enough to capture value, careful enough to keep customers, employees, and regulators on their side.

Please note this blog post should be used for learning and illustrative purposes. It is not a substitute for consultation with an attorney with expertise in this area. If you have questions about a specific legal issue, we always recommend that you consult an attorney to discuss the particulars of your case.

Sources:

  1. White House, Executive Order on Safe, Secure, and Trustworthy Artificial Intelligence (October 30, 2023). https://www.federalregister.gov/documents/2023/11/01/2023-24283/safe-secure-and-trustworthy-development-and-use-of-artificial-intelligence
  2. National Institute of Standards and Technology, Artificial Intelligence Risk Management Framework 1.0 (January 2023). https://www.nist.gov/itl/ai-risk-management-framework
  3. Federal Trade Commission, Business Guidance: Keep your AI claims in check and avoid unfair or deceptive practices (2023–2024). https://www.ftc.gov/news-events/news/press-releases/2024/09/ftc-announces-crackdown-deceptive-ai-claims-schemes
  4. U.S. Equal Employment Opportunity Commission, Technical Assistance: Assessing Adverse Impact in Software, Algorithms, and AI used in Employment Selection Procedures (May 2023). https://www.lawandtheworkplace.com/2023/05/eeoc-releases-technical-document-on-ai-and-title-vii/
  5. U.S. Copyright Office, Copyright Registration Guidance: Works Containing Material Generated by Artificial Intelligence (March 2023, updated 2024). https://www.federalregister.gov/documents/2023/03/16/2023-05321/copyright-registration-guidance-works-containing-material-generated-by-artificial-intelligence 

This article is for general informational purposes only and does not constitute legal advice. For guidance tailored to your organization, consult Michigan counsel.