# Kenneth Carter

Kenneth Carter writes about contract negotiation and building Ozeki Technologies, which Carter describes as using AI to help deal teams find trades between contract terms and reach better outcomes.

- Profile: https://b2binfluencer.marketing/creators/kenneth-carter/
- LinkedIn: https://www.linkedin.com/in/kennethrcarter
- LinkedIn followers: 6,096
- Topics: artificial-intelligence, sales
- Based on 17 dated posts, latest 2026-08-18

## Summary

Kenneth Carter is most useful for explaining contract negotiation as trading deal points rather than accepting or rejecting redlines.

- Best for: A contract management software thought leadership campaign on negotiation strategy, deal friction, and legal and sales alignment.
- Not for: Consumer financial wellness campaigns, short form video, or lifestyle content about saving money.
- Check first: Confirm audience analytics by job function, seniority, and industry, plus results for negotiation, legal AI, and procurement posts.

### Takeaways

- Negotiating a contract is a different skill from redlining one.
- Identify deal points first, then trade them.
- A higher liability cap can be traded for a longer contract term.

## Main ideas

- **Negotiating a contract is a different skill from redlining one**: Carter argues that most lawyers learn to redline contracts but never learn to negotiate. Repetition builds the redlining habit: spot the issue, flag the risk, mark up language outside your tolerance, and learn over time what gets accepted and rejected. Negotiation is different: it means discovering what both parties want and looking for trades. A redline to a limitation-of-liability clause, for example, is not an accept-or-reject decision; it is a prompt to look for something to exchange elsewhere in the deal. Without deliberate training, Carter warns, teams negotiate by instinct, gut feeling or inertia, letting the other side set the agenda.
- **Identify the deal points first, then trade them**: Carter's method starts by listing deal points, not clauses. A deal point is anything either side can give or take — pricing, shelf space, promotions, discounts, payment terms, indemnification. Once identified, each becomes a tool for resolving a demand the other side calls unacceptable, instead of a binary fight over wording. Carter notes that this takes time: teams must decide which deal points matter and how to value them. Most organisations, they say, skip the work and let their teams wing it.
- **Contract friction is a growth problem, not a legal cost**: Carter argues that treating negotiation as legal overhead leads to the view of the general counsel as the department of no. The real cost, in their account, is not legal fees but deal friction: delayed revenue, lost upsells, burned reps, dead deals and unpriced risk. Their alternative is to treat the negotiator as a driver of revenue growth and deal velocity. Carter describes trying to change the incentive structure directly, with a risk-adjusted award for sales reps that tied the paper a deal was signed on to the credit a rep received.
- **Most contracts were written for war because changing documents used to be expensive**: Carter offers a historical explanation for why standard contracts read the way they do. Before word processing, even changing a document was costly, let alone renegotiating it, so the market settled on contracts of adhesion built for a world that could not negotiate at scale. That left two bad options: a standardised template that protects one side but fits nobody, or a bespoke negotiation that fits the deal but takes weeks and costs a fortune. Carter argues that the industry avoided nuance, over-lawyered the boilerplate and underpriced the risk. Drafting contracts for business rather than conflict, they say, is where legal can create value.
- **AI for deals should model the next move, not generate the next word**: Carter rejects the idea that AI's role in contracting is producing or cleaning up language, and instead frames Ozeki as negotiation intelligence: modelling what each side cares about, running trade-off scenarios and mapping a deal to mutual value. Their slogan for the distinction is that most AI tools predict the next word while Ozeki predicts the next move. The posts describe a system that can work from counterparty paper alone, with no historical deal data, by running simulations and recommending opening positions. To assess these product claims, ask for the underlying simulations and measured outcomes.
- **Variability is the blocker for AI in deal work; discretion is the next step**: In the shortest post, Carter calls the AI moment the age of “Artificial Insanity”, because the same prompt can return a different answer each time. Their argument is commercial rather than philosophical: a business cannot scale on that kind of variability. Carter names “Artificial Discretion” as the next big thing in Agentics, suggesting that deal teams need judgement applied consistently, not more fluent generation.
- **The best outcome comes from fusing data, technology and human intuition**: Carter does not argue that AI replaces dealmakers. Reporting a takeaway from a legal tech panel at SF Tech Week, they say the best use of AI is to produce an outcome better than a human could reach alone, and that dealmakers who generate outsized results for clients combine data, technology and human intuition. The test for the technology is commercial: did the lawyer's involvement get the client a better price on what was actually being bought or sold, not just a cleaner set of legal terms?

## Work to read

- [How I negotiate a contract: look for trades, not redlines](https://www.linkedin.com/posts/kennethrcarter_dreadeddepartmentofno-negotiationssuck-contracts-activity-7419025344868978690-0SeJ)
- [Three ways I reduce contract friction](https://www.linkedin.com/posts/kennethrcarter_dreadeddepartmentofno-negotiationssuck-contracts-activity-7429536377941217281-cENm)
- [Why do most contracts feel like they were written for war, not business?](https://www.linkedin.com/posts/kennethrcarter_dreadeddepartmentofno-negotiationssuck-contracts-activity-7353084342962675714-reBC)
- [A customer negotiated with no historical deal data](https://www.linkedin.com/posts/kennethrcarter_dreadeddepartmentofno-negotiationssuck-contracts-activity-7351276178516709377-DZdU)

## Brand relationships

- **Ozeki Technologies** (owned_business)

## Partnerships

Kenneth Carter's posts show no sponsorship, advertising, or paid-collaboration offer from them. They include no media kit, rate card, audience specification, or partner policy, and no post announces a sponsored placement or names a brand partner. Instead, one recurring commercial call to action promotes Ozeki Technologies: posts about negotiation strategy end by pointing readers to Ozeki, inviting them to “reach out for a demo,” and, in one case, to drop a target emoji for a preview by direct message. That is a product demo route for the software, not a route for a brand to commission content, a webinar, or a byline. Ask whether they offer sponsored placements and request their terms.

## Alternatives

- Wesley Schnell, AIGP, CIPP: Choose Schnell when the series should argue that contract negotiation is about leverage, not clause-by-clause polishing. They make the case that fewer than 0.1% of contract disputes are ever formally litigated, that vendors often keep operating according to default practices regardless of what the signed contract says, and that the practical goal is the contractual ability to be disruptive when something goes wrong. They also address AI clauses directly, walking through new AI terms appearing in enterprise agreements and how legal, contract, and procurement teams should navigate them. That gives a contract software vendor whose buyers draft or receive AI vendor terms a workable session subject. The material also shows a live session format already in use: an announced AI legal session on AI-related enterprise contract terms.
- Amy Grubb, J.D.: Choose Grubb when the series should serve legal teams buying contract or legal AI rather than negotiating the underlying deal. Her post draws on hundreds of evaluation calls with law firms and in-house teams and reads as a buyer's checklist: insist on a live, real-time demo; understand post-sales support and turnaround before buying; check whether lawyers are on the product team; test how much lawyers can self-serve without vendor involvement; and look for tools that fit existing legal workflows. For a contract management vendor selling into in-house legal, that closely matches the purchase decision a thought leadership series usually aims to influence, and gives a byline a structure readers can apply in their next trial.
- Eric Seader: Choose Seader when the series should make data control and vendor independence the reason to care—for example, a series aimed at legal teams weighing whether to let an AI vendor process their contracts. The posts offer two concrete frames: the Spirit Airlines bankruptcy example, in which internal emails, messages, and operational records were sold and a buyer paid $10 million to train models on them; and AI sovereignty, which shifts the buying question from what a tool does today to who controls the engine and what happens if that changes. That fits a contract management vendor seeking to argue for a particular data-handling position. The sovereignty framing also gives a webinar a natural discussion question for buyers evaluating legal AI vendors.

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AI-assisted summary of public posts. Audience demographics are not available. Engagement appears only where we checked recent posts. Method: https://b2binfluencer.marketing/methodology/