Most ERP implementations fail — not because of the software itself, but because of unrealistic expectations set right from the start.

In this video, I break down the “dirty secret” behind why so many companies pour millions into new ERP systems (SAP, Oracle, Microsoft Dynamics, NetSuite, etc.) only to end up over budget, behind schedule, and disappointed.

Key takeaways:

  • Why 70-85% of ERP projects miss timelines, budgets, and expected business benefits.
  • How vendors’ sales hype ( “out-of-the-box in months!”) sets customers up for failure.
  • The hidden costs most organizations ignore: data migration, customizations, change management, training, and long-term support.
  • Common pitfalls like undersized budgets/timelines, rushed testing, skimping on resources, and over-reliance on part-time internal teams.
  • Practical steps to set realistic expectations: talk to references, conduct site visits, benchmark with experienced third parties, dedicate proper resources, and plan for contingencies

If you’re planning or in the middle of an ERP/digital transformation project, this is essential viewing to avoid the most expensive mistakes.

  • Success requires clear-eyed planning, strong leadership, and realistic timelines — not best-case sales pitches.

Have you dealt with ERP implementation challenges?

  • Share your biggest lesson (or horror story) in the comments!
👇 #erpcommunity #erpfailure #saps4hana #oraclecontracts #softwarelawyer #sapservices #saphanacloudplatform #saas #erpcloud #teamtaft #sapcontracts #oraclelawsuit #oraclefailure #oracletermination #saptermination

AI is one of the few technologies I have seen in my career that will live up to the hype.

  • It goes without saying that using AI for the sake of AI makes no sense.
  • You must have a business case for it.

But what is the cost?

  • What rights are you giving up in your data in exchange for utilizing AI functionality in the context of your ERP system?
  • Who owns not only your data, but your customer’s data that is input into the ERP system? Are you putting confidential information at risk?
  • Are you losing trade secrets?
  • Who can use the data you put into the AI tool?

I discuss these issues in my latest video

#erpimplementation #digitaltransformation #ai #artificialintelligence #openai #generativeai

Make no mistake: if you are about to start a digital transformation, the cards are stacked against you.

  • As an ERP customer, you are at an incredible disadvantage when trying to successfully implement ERP software.
  • To some extent, that is by design.

ERP vendors and integrators act in their self-interest to maximize revenue.

  • They minimize the complexity of the implementation process, which can result in unreasonable expectations.
  • ERP software consultants and salespeople act in their own self-interest.

An entire ecosystem is built up around each of the major ERP providers.

  • Often, partner networks, self-dealing, and commissions drive recommendations more than objectivity.
  • Large consulting firms have entire practice areas devoted to implementing Oracle, SAP, or Infor.

This sets up unreasonable expectations and prevents you from devoting the appropriate resources to your ERP software project.

  • Unreasonable expectations cause ERP software project failure.
  • I discuss these issues in my latest YouTube video
#erplawyer #erpcommunity #erpfailure #saps4hana #oraclecontracts #softwarelawyer #sapservices #saphanacloudplatform #saas #erpcloud #teamtaft #sapcontracts #oraclelawsuit #oraclefailure #oracletermination #saptermination

General counsel face growing pressure to support aggressive AI adoption. At the same time, they must protect their organization’s legal and governance posture.

This video directly addresses that tension.

Employee resistance to AI rarely stems from a dislike of technology. It stems from unmanaged uncertainty about job security, performance evaluation, and data use. That uncertainty can lead to shadow AI use and even employee sabotage.

Watch to learn how to:

  • Spot early warning signs of AI-related behavior risk among employees.
  • Understand how those behaviors threaten data integrity, privilege, and compliance.
  • Reframe AI risk management as a legal function, not an IT problem.

The discussion also covers practical steps for structuring policies, training, and communication. The goal: build AI systems that are trusted, auditable, and aligned with your organization’s employment, confidentiality, and governance obligations.


#ArtificialIntelligence #AIGovernance #ShadowAI #InHouseCounsel #LegalRisk #industriallawyer #insurancelawyer #commericallitigation #privacylaw #datasecuritylaw #whitecollardefense #defenselaw #classactionlawsuits #AIlawsuits #AIdatabreach

Artificial intelligence is moving into government contract performance.

  • AI now supports scheduling, logistics, and production planning on federal contracts.

Defense contractors are asking a critical question.

  • Does using AI on a Navy or other government contract give the government rights to your proprietary systems, methods, or data?
  • The short answer is no. The real risk lies elsewhere.

Vague AI transparency and explainability clauses can quietly expand disclosure obligations.

  • This contract language can erode trade secret protections that have existed for decades.

This video explains how government IP rights apply to AI-assisted performance and identifies the exposure points that general counsel need to know about.

  • Key actions include defining deliverables precisely, managing explainability requests without revealing proprietary logic, and treating AI workflows as intellectual property assets.
#GovernmentContracts #ArtificialIntelligence #NavyContracts #ShipRepair #DefenseIndustry #FederalContracts #TradeSecrets #AICompliance #industriallawyer #insurancelawyer #commericallitigation #privacylaw #datasecuritylaw #whitecollardefense #defenselaw #classactionlawsuits #AIlawsuits #AIdatabreach

Artificial intelligence is reshaping how lawyers research, draft, and advise.

But AI has introduced two legal risks that users cannot ignore.

  • Courts are sanctioning attorneys for AI-generated errors in court filings.
  • And a February 2026 federal decision held that a client’s independent use of a public AI chatbot was not protected by attorney-client privilege.

This video explains both risks, outlines the decisions behind them, and identifies steps in-house counsel can take now, including AI-use policies and guidance for legal departments.


#ArtificialIntelligence #LegalEthics #AttorneyClientPrivilege #AILaw #LegalTechnology #industriallawyer #insurancelawyer #commericallitigation #privacylaw #datasecuritylaw #whitecollardefense #defenselaw #classactionlawsuits #AIlawsuits #AIdatabreach

If you are a Navy contractor, ship repair company, or defense supplier wondering whether using AI puts trade secrets at risk, you are asking exactly the right question. I see why this concern is growing. Federal agencies now operate under formal AI governance and acquisition guidance, which means contractors should expect more questions about oversight, documentation, and how AI-assisted decisions are made.

My short answer is this: AI itself does not automatically hand your trade secrets to the government. The real risk usually comes from contract language, deliverable definitions, disclosure obligations, vendor terms, data handling, and how your team responds when the government asks for an explanation. That is where contractors can lose control of valuable methods without realizing it until the project is already underway.

In my view, the better question is not simply whether using AI puts trade secrets at risk. The better question is what part of your workflow could turn an internal advantage into something you are forced to disclose, deliver, or defend. When I look at this issue, I focus on the contract first, then the data, then the process.

#GovernmentContracts #ArtificialIntelligence #NavyContracts #ShipRepair #DefenseIndustry #FederalContracts #TradeSecrets #AICompliance #industriallawyer #insurancelawyer #commericallitigation #privacylaw #datasecuritylaw #whitecollardefense #defenselaw #classactionlawsuits #AIlawsuits #AIdatabreach

Does using AI put trade secrets at risk by itself?

Trade secret law can protect valuable business and technical information, including internal methods, processes, programs, planning systems, and engineering know-how, as long as that information is actually treated as confidential. That can include scheduling logic, labor forecasting assumptions, optimization rules, and internal AI-supported workflows that give a contractor a competitive advantage.

The key point is that using AI alone does not automatically mean those protections disappear. If a contractor uses AI as an internal tool to help perform work, that does not automatically transfer the underlying model, logic, or proprietary process to the government. The more important question is what the contract requires the contractor to deliver or explain. If the contract is vague about AI outputs, transparency, or explainability, that is where the real risk begins. In other words, the problem is usually not the use of AI itself, but rather that it is unclear contract language that can blur the line between the final work product and the protected internal methods behind it.

Where the real exposure begins

The real exposure usually starts with vague drafting. If a contract requires a schedule, a forecast, a recommendation, or a report, that sounds straightforward. But once a clause starts using broad phrases like AI outputs, explainability, transparency, validation materials, model documentation, or auditable decision support, the scope can widen quickly. Federal AI acquisition and governance memoranda have pushed agencies to strengthen risk management and visibility into AI systems, so contractors should expect more scrutiny around how AI-assisted outputs are generated and controlled.

Now that doesn’t mean the government automatically owns your internal methods. However, sloppy definitions can create leverage for broader disclosure requests. When explainability is undefined, a request for an explanation can turn into pressure to reveal assumptions, weighting, workflow logic, source inputs, or other confidential details that give your company its advantage. That is the moment when using AI becomes a practical contract problem instead of a theoretical one.

Why this matters so much on Navy work

Navy and ship repair work is especially sensitive because competitive advantage often lives inside the process rather than the final paper you hand over.

A schedule may look simple on the surface. But behind that schedule may be years of institutional knowledge about drydock congestion, supplier slippage, rework patterns, labor efficiency, overtime impact, sequencing risk, and how to compress an availability without creating failure elsewhere. If an internal AI-assisted tool helps your team turn that experience into better performance, the trade secret value often sits in the method, not just the final output. Under trade secret law, that kind of technical and business process information can qualify for protection when properly safeguarded.

That is why I don’t tell contractors to stop using AI. I tell them to define the boundary between the deliverable and the engine that produced it. If that boundary is not clearly drawn, using AI can shift from a smart business question to an expensive dispute.

A real-world example contractors should recognize

Imagine a ship repair contractor managing overlapping availabilities across limited drydock space. The company uses an internal AI-assisted scheduling tool trained on its own proprietary historical labor patterns, supplier reliability data, rework history, and sequencing behavior. The contract requires the contractor to deliver an updated schedule, and that is exactly what the contractor provides.

At that stage, the issue is not simply that AI was used. The more important question is whether the contract clearly defines what must be delivered and what must be explained. Problems can start when performance pressure builds, and the government wants to know why one availability was sequenced ahead of another. If the contract includes broad or unclear language about AI transparency or explainability, the discussion can quickly move beyond the final schedule and into assumptions, scenario testing, internal weighting, or decision-making logic that the contractor never intended to disclose.

That is how this risk often develops in practice. It isn’t because AI automatically strips away protection. Unclear contract language can blur the line between the final work product and the proprietary methods behind it.

What general counsel and contract teams should do now

1. Map where AI is actually being used

You cannot protect what you have not identified. Many companies discover too late that program teams are already using AI in scheduling, forecasting, logistics, quoting, maintenance planning, document review, or reporting. Start with visibility. Determine what tools are in use, what data they touch, and whether any output is flowing into a contract deliverable. Recent federal AI guidance makes visibility and risk management a practical necessity, not just a governance exercise.

2. Define deliverables with precision

Be explicit about what the government receives and what remains internal. If your AI system is merely an internal support tool, the contract should say so where possible. If only the final schedule, report, or recommendation is deliverable, define that clearly.

3. Control explainability before it controls you

I encourage contractors to prepare an explanation framework that describes outcomes without disclosing proprietary logic. That may include business justification, process summaries, validation results, or performance metrics that answer the government’s concern without exposing the underlying method. In many disputes, the problem is not that an explanation was required. The problem is that no one defined the safe level of explanation in advance.

4. Mark and segregate proprietary data

If your company is going to assert limitations, it needs disciplined marking, clean documentation, and a process to support those assertions. Also, separate government data from proprietary training data wherever possible. If you blend everything together carelessly, you invite arguments later about rights, access, and scope.

5. Review vendor terms before adoption, not after a dispute

Your internal AI tool may not be as internal as you think if the vendor retains broad rights, stores prompts indefinitely, uses input for training, or imposes weak confidentiality terms. That is a trade secret issue and a contract risk issue.

6. Train program managers to stop oversharing

A surprising number of disclosure problems begin in meetings, status calls, slide decks, white papers, or well-intentioned technical explanations. If your team does not know the line between explaining a result and revealing a protected method, it’s much more likely that using AI can put trade secrets at risk. Internal training matters. So does a clear escalation path for disclosure questions.

The questions contractors are already asking

Does using AI put trade secrets at risk on a government contract?

It can, but not automatically. The main risk comes from what the contract requires you to deliver, disclose, validate, or explain, along with how well you protect secrecy in practice.

If I use AI in scheduling, does the Navy own my model?

Usually not just because you used it. Ownership and license rights depend on the contract, the applicable clauses, the source of development funding, and whether the software or technical data are actually delivered.

What counts as a trade secret in an AI workflow?

Potentially, your training approach, source code, prompts, assumptions, workflows, data curation methods, weighting logic, decision rules, or process architecture, if they derive value from secrecy, and you take reasonable steps to protect them.

Can explainability requirements force disclosure of proprietary logic?

They can if the contract language is broad and your company has not set boundaries around what explanation means. That is why precise drafting matters so much.

What is the safest first step for a contractor using AI today?

Do a contract and workflow review before the next proposal, modification, or deliverable cycle. That is the best time to narrow definitions, strengthen markings, and fix data practices before a dispute starts.

Protect the advantage before the contract gives it away

For contractors, the most useful answer to whether using AI puts trade secrets at risk is this: AI is not the automatic transfer event. Bad definitions, weak controls, careless disclosures, and unmanaged contract language are.

That is why I would treat AI workflows the same way I would treat any other valuable technical edge. Identify them. Protect them. Define them carefully in the contract. Control how they are explained. Keep your proprietary data cleanly separated. And do not assume that a smart internal tool will stay protected if your paperwork and process say otherwise.

If you are working through these issues now, you can review my experience handling complex commercial, technology, and AI related disputes, or contact me directly to talk through your contract language and workflow. Acting early gives you the best chance to protect your rights, preserve your competitive advantage, and avoid turning a useful AI tool into a disclosure problem later.

Introduction and Background

In Feb. 2026, a public-private partnership headed by the U.S. Department of the Treasury concluded an investigative process aimed at strengthening cybersecurity and risk mitigation for AI in the financial services sector. The partnership consisted of executives from over 100 financial institutions, U.S. and international agencies, federal and state financial regulators, and other key stakeholders. One of the partnership’s key deliverables announced at the conclusion of the investigation is the Financial Services AI Risk Management Framework (“Financial Services AI RMF”), which adopts and expands the AI Risk Management Framework provided by the National Institute of Standards and Technology (“NIST Framework”) for specific application to the financial services industry.

Framework Organization

The NIST Framework is organized into four “functions”: Govern, Map, Measure, and Manage. The Financial Services AI RMF adopts the NIST Framework’s functions, but then provides further controls under each function, which are aimed at tailoring the framework to the financial services sector. The Financial Services AI RMF contains 230 controls designed to be scalable and adaptable for financial institutions, including community banks, credit unions, national and multinational banks, insurers, investment firms, and their third-party providers.[1] Implementation of the Financial Services AI RMF is not mandatory; the framework is instead categorized as a tool that is “complementary to existing risk frameworks” and that “synthesizes global standards and supervisory expectations.”[2]

The Financial Services AI RMF consists of four components: 1) an AI adoption stage questionnaire, which businesses can fill out as a starting point to identify their current AI adoption stage; 2) a risk and control matrix, which lists the 230 controls; 3) a user guidebook for control adoption and implementation; and 4) a control objective reference guide, which provides further information on each control, as well as examples of “effective evidence” of implementation.

Key Controls

Below, we highlight and summarize a sample of certain controls that legal counsel can help financial services organizations assess and address:

  • Govern 1.1.1: The organization identifies, monitors, and integrates applicable laws, regulations, contractual obligations, and sector requirements into policies, procedures, and operations.
  • Govern 1.1.3: The organization implements procedures to validate AI system compliance with law, including audits and impact assessments.
  • Govern 6.1.1: The organization establishes processes for evaluating and selecting third-party AI technologies based on criteria that assess security and privacy implications, due diligence, and contracting practices.
  • Govern 1.2.3: The organization develops an AI Acceptable Use Policy.
  • Map 4.1.1: The organization documents processes for identifying, mapping, assessing, and managing potential legal risks associated with the AI systems, including risk related to data privacy, intellectual property, third-party rights, and use of service providers.
  • Map 4.1.3: The organization communicates identified legal risks associated with the AI system, and changes in laws, regulations, and industry standards, to relevant stakeholders.
  • Map 5.2.2: The organization engages with stakeholders to solicit insights and develop action plans that detect, prevent, and mitigate potential risks, costs, or adverse impacts.
  • Measure 2.10.1: The organization conducts an initial examination of the privacy risks associated with AI systems and documents the results. The organization establishes mechanisms for managing risks and incidents, such as data breaches.
  • Measure 2.10.3: The organization establishes procedures for tracking and managing data subject consent, including handling data subject rights requests.
  • Manage 3.1.5: The organization monitors AI risks associated with third-party resources, including monitoring contracts and contract compliance.

The Taft Privacy, Security, and AI team stands ready to assist financial services organizations in implementing the Financial Services AI RMF and otherwise assessing and managing enterprise AI risk.

[1] See a description of the Financial Services AI RMF online here: https://cyberriskinstitute.org/artificial-intelligence-risk-management/.

[2] Id.

SAP is under formal EU antitrust investigation.

  • SAP is well known for using aggressive tactics to limit customers’ support options and creating questionable revenue-generating events.
  • What does this mean for you?

That’s what I discuss in this clip from my upcoming video.

#erpcommunity #erpfailure #saps4hana #oraclecontracts #softwarelawyer #sapservices #saphanacloudplatform #saas #erpcloud #teamtaft #sapcontracts #oraclelawsuit #oraclefailure #oracletermination #saptermination

If you are moving fast with generative tools, the best legal practices when you use AI are not optional. They are what keep a promising brand from turning into a trademark dispute, a false advertising claim, or a costly rebrand.

What are the best legal practices when you use AI for branding?

The best legal practices when you use AI for branding are to keep meaningful human control, clear names and slogans before launch, verify every marketing claim, review vendor terms, document creative decisions, avoid lookalike prompts, and require legal signoff before a campaign goes live. That answer matters because the legal risk is very real. Under Section 43(a) of the Lanham Act, a business can face claims for false designations of origin, misleading descriptions, and advertising that is likely to cause confusion or misrepresent the nature or qualities of goods or services. The FTC also says advertising must be truthful, non-deceptive, and backed by evidence. Essentially, if your AI-assisted branding looks too much like someone else’s mark or makes claims you cannot support, you can still be sued.

Why AI speed creates trademark risk

Most companies do not have a legal framework for AI-assisted branding. Teams prompt a tool, pick the output they like, and move forward. That speed feels efficient, but it can hide major problems.

AI tools can produce names, slogans, logos, and product copy that sound familiar because they are built to predict likely language patterns. In branding, familiarity can be dangerous. A slogan that sounds polished may also sound too close to a competitor’s long-used phrase. A logo concept may feel distinctive inside your design meeting, but look much less original when compared against the market. Product copy may sound persuasive while quietly making claims your business has never tested or documented.

That is why the best legal practices when you use AI should begin before launch, not after someone sends a demand letter.

The law still applies even when AI helped create the brand

Some business leaders assume that if AI generated the first draft, the usual rules are somehow softer. They are not.

The Lanham Act still governs confusing branding and false advertising. The FTC still expects truthful, substantiated claims. That means the questions remain the same for Indianapolis businesses and national brands alike. Will consumers be confused? Is the claim accurate? Can you prove it? If the answer is unclear, you need more review before launch.

7 best legal practices when you use AI to build your brand

1. Keep human ownership and control

The first rule is simple. A person, not a tool, should own the decision-making process. Your team should define brand strategy, choose among outputs, revise what the AI creates, and approve the final result. Human control helps show that the brand reflects business judgment rather than an unexamined machine output.

That approach also fits with broader federal IP guidance. The USPTO has stated that AI systems are tools used by human inventors, not inventors themselves, and the U.S. Copyright Office has explained that protection turns on sufficient human authorship rather than mere prompting alone. Branding is not identical to patent or copyright law, but the practical lesson is the same: human contribution matters.

2. Clear AI-generated names, slogans, logos, and domains

Every serious brand element should go through clearance. That means screening for similar marks, similar slogans, overlapping domains, and marketplace use that could trigger confusion.

Too many teams think AI output is new because it feels new to them. That is not how trademark risk works. The real question is whether your proposed brand conflicts with rights that already exist. Clearance should happen before launch, before ad spend, and before public rollout across multiple channels.

3. Substantiate claims before they go live

This step matters just as much as trademark clearance. If AI writes product claims, performance statements, comparisons, or guarantees, someone needs to verify each one.

The FTC’s rule is direct: ads must be truthful, advertisers must have evidence to back up their claims, and ads cannot be unfair. So if your AI drafted language like “industry leading,” “clinically proven,” “fastest,” or “guaranteed,” your business should pause and ask what evidence supports that wording. If you cannot prove it, do not publish it.

4. Review vendor terms and output rights

Not every AI platform gives you the same protections. Before relying on a tool for core brand development, review who owns the output, what restrictions apply, how the provider handles training data, and whether any indemnity exists.

For enterprise users, this is often negotiable. It is better to address those issues at the contracting stage than after a dispute arises over ownership, confidentiality, or third-party claims.

5. Create an audit trail

Save prompt history, revision notes, internal comments, search results, and the reasons your team selected one option over another. Documentation serves two purposes.

First, it helps prove thoughtful human involvement. Second, it shows your business acted responsibly if a dispute later arises. In litigation, a clean record can matter. It can show you did not blindly copy, did not ignore obvious risk, and did take legal review seriously.

6. Ban lookalike prompting

One of the riskiest habits in AI branding is asking for something “like” a competitor, a market leader, or a famous campaign. That shortcut may feel harmless in a brainstorming session, but it increases the odds that the output will echo protected branding or trade dress.

A better prompt focuses on your own personality, values, audience, and differentiators. Ask for originality, not imitation. This is one of the most practical and best legal practices when you use AI because it reduces confusion risk at the source.

7. Use launch gates before anything goes public

Build a final review checkpoint into the process. No brand element, campaign asset, or AI-drafted claim should go live without a confusion check and legal signoff.

This can be lightweight for lower-risk materials and more formal for major launches. The important point is consistency. A launch gate gives your company one last chance to catch a problem before the public sees it.

A realistic example of how this can unravel

Imagine an Indianapolis software company using AI to generate a new tagline for a product rollout. The internal team loves the result because it sounds polished, modern, and memorable. The marketing department places it on the website, in paid ads, and across sales decks.

A month later, the company receives a demand letter. A competitor has used a very similar tagline for years in the same industry. Now the competitor argues the new campaign is likely to confuse customers and also points to AI-drafted performance claims in the ads that were never substantiated.

This is exactly the kind of situation that can turn a fast launch into a costly legal problem. A basic clearance search, documented human edits, and a claim review checklist might have caught the issue before launch. That risk framework tracks the seven-step structure discussed in your transcript.

Questions I hear from business teams using AI

Can I trademark a name that AI helped me create?

Possibly, but the key issue is not whether AI helped. The real issue is whether the proposed mark is distinctive, available, and lawfully used in commerce without creating confusion with someone else’s rights.

Does using AI protect me if the output copies someone else?

No. If your branding creates confusion or includes misleading claims, the legal exposure stays with the business that used the material.

Do I need a lawyer before I launch an AI-generated slogan?

For a minor internal concept, maybe not immediately. But before a public launch, legal clearance is wise, especially if the slogan will appear in advertising, on packaging, or across multiple states.

What kinds of AI-generated claims are most dangerous?

Comparative claims, performance claims, superiority claims, scientific-sounding claims, and guarantees are all high risk when they are not backed by evidence.

Should my company have a written AI branding policy?

Yes. A written policy helps marketing, legal, leadership, and outside agencies follow the same review process. It also reduces inconsistency, which is where avoidable risk often begins.

A practical policy your team can use now

If your company is serious about AI-assisted branding, your policy should at least require the following:

  • A designated owner for brand decisions
  • Pre-launch trademark and slogan clearance
  • Review of all factual and performance claims
  • Approval of vendor terms for AI tools
  • Documentation of prompts, edits, and rationale
  • A prohibition on lookalike prompting
  • A final legal review before publication

That framework is not burdensome. It is efficient risk control. In many cases, it is the difference between scaling a brand confidently and backing up under pressure.

Protect the upside before the risk gets expensive

AI can absolutely help your business move faster. But speed without legal discipline is not a growth strategy. It is exposure.

The best legal practices when you use AI allow you to keep the benefits of speed while protecting trademark value, advertising accuracy, and long-term brand ownership. When your process includes human control, clearance, substantiation, documentation, and launch review, you give your business a much stronger foundation for growth.

If you are building or refreshing a brand with AI, now is the right time to review your process. You can learn more about my experience in commercial litigation, privacy, security, and artificial intelligence matters, explore my Taft profile and contact information, and read Taft’s related guidance on AI-powered fraud and business safeguards.