Modernising In-House Trademark Strategy at a Global Ingredients Leader

The starting point

The in-house legal team was supporting more than one hundred new-product launches a year across beverages, nutrition, ingredients, and agri-food. The historical process — a mix of manual register searching and external counsel review — was thorough but slow, and it was bottlenecked on a single senior paralegal who triaged every incoming request.

Cycle times for a basic clearance had drifted to roughly three weeks, and the more complex multi-jurisdiction reviews were running six to eight. Outside counsel was being engaged for first-pass searches that the team felt should be possible to handle internally. The brand teams had stopped routing some shortlists through legal at all, which surfaced as opposition surprises later in the cycle.

Leadership signed off on a one-year rebuild with a clear constraint: no headcount increase. Whatever changed had to come from process, tooling, and a different distribution of work between in-house and external counsel.

What changed in the workflow

The team replaced ad-hoc search routing with a four-stage pipeline: intake, knockout, similarity screen, and full clearance. Each stage had a published target turnaround and a defined exit criterion, which made it possible to measure where time was actually being lost. The first audit showed that more than half of all elapsed time was sitting in queue between stages, not in the review work itself.

An AI-assisted similarity engine was introduced at the knockout and screening stages. The engine ran candidate marks against the relevant registers and produced ranked similarity outputs that the paralegal team could triage in a fraction of the time the previous manual review took. Crucially, the engine was tuned conservatively — false positives were preferred over false negatives, because the cost of a missed conflict was much higher than the cost of an extra five minutes of human review.

Outside counsel was kept in the loop for substantive opinions on the finalists and for filing strategy, but was no longer running first-pass searches. The intake form was rewritten in business language so that brand managers could submit shortlists without translating their requests into class numbers.

Outcome numbers

Twelve months after rollout, basic clearance cycle time had dropped from roughly three weeks to under five business days. Multi-jurisdiction reviews fell from six-to-eight weeks to about two. The team was processing nearly forty percent more clearance requests with the same headcount, and the share of requests that reached outside counsel had fallen by more than half.

Outside-counsel spend on routine clearance work dropped by an estimated thirty to thirty-five percent on a like-for-like basis. That spend did not vanish from the budget — a portion was redirected toward more strategic work, including portfolio rationalisation in markets the company had been quietly accumulating registrations in for years.

Refusal rates on filed applications also moved. The substantive refusal rate on first-action filings dropped by roughly a third, which the team attributed to better candidate triage rather than to any change in examiner behaviour. Opposition exposure followed the same direction at a smaller magnitude.

What the team learned

The first lesson was that the bottleneck was almost never where people thought it was. Everyone assumed the search work itself was slow. The audit showed it was queue time and rework. Once stages had owners and turnaround targets, throughput improved before any new tooling was switched on.

The second lesson was that AI-assisted search worked best as a wide net, not a narrow filter. The team initially set the engine's similarity threshold high, hoping to get a clean shortlist. They lost a conflict that way and recalibrated. The engine now surfaces more candidates than a strict filter would, and human reviewers do the narrowing.

The third lesson was about change management. Brand managers did not care about the new pipeline; they cared about predictable answers. Publishing turnaround commitments and hitting them did more for adoption than any internal training session.

Implications for similar in-house teams

The pattern transfers reasonably well to any in-house function supporting a high-volume launch pipeline across multiple regulated markets. The structural ingredients are the same: a defined intake, staged review with measurable handoffs, machine-assisted triage on the wide end of the funnel, and human judgment concentrated on the narrow end.

Two cautions are worth flagging. First, the gains are larger if you fix the workflow before introducing tooling. Teams that bolt an AI search engine onto a broken process tend to make the broken process faster, not better. Second, plan explicitly for the redirected spend. The savings on routine clearance will be visible quickly; the question of what the team should do with the recovered capacity is the one that determines whether the program is judged a success a year later.