Two plant workers reviewing a record on a tablet beside a snack production line
Weever
2026 AI in Manufacturing Report

The State of AI Data Readiness in Manufacturing

AI is moving into manufacturing faster than frontline data is ready for it. We asked 169 manufacturing leaders where their frontline data really stands. See the gaps, and where your plant should start.

169 leaders · 28 questions · A 20-minute read
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What you get

One report. Four gaps. A clear place to start.

Most plants are already running AI on frontline data that has gaps in it. The report shows you which gaps, how common they are, and what to fix first.

Five findingsThe data behind each one, from 169 leaders.
The four gapsHow each one shows up on the plant floor.
Three readiness segmentsWhich one your plant is in, and what that means.
Four recommendationsIn the order to tackle them for your segment.
Cover of The State of AI Data Readiness in Manufacturing report
Key findings

AI adoption in manufacturing is ahead of the data it runs on

We asked 169 manufacturing leaders 28 questions about AI and the frontline data it relies on.

63%

of leaders say their organization is already running or piloting AI in manufacturing operations.

99%

report at least one of four gaps in their frontline data, and 59% report three or more.

66%

say limited trust in frontline data restricts how far their organization relies on AI.

Four gaps undermining frontline data

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Get the full report

Five findings · the data behind each one
The four gaps · how they show up on the plant floor
Three readiness segments · where each one should start
Four recommendations · in the order to tackle them
Download the report A 20-minute read
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Three segments of AI readiness

Which of these describes your plant?

We grouped leaders by how confident they are in their frontline data and how many of the four gaps they report. Three segments of similar size emerged.

0 to 2 gaps 3 or 4 gaps Confident
33%Ready and consistent56 leaders
27%Confident with gaps45 leaders
Not confident
8%Too small to profile14 leaders
32%Aware and held back54 leaders
Ready and consistent Start with recommendation
Confident with gaps Start with recommendation
Aware and held back Start with recommendation

Each segment has a different place to start. The report shows where each one should begin, and why.

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What to do about it

Consistent frontline capture is what separates ready plants from the rest

82%of leaders whose frontline software is used consistently say their data is prepared for AI
6%of leaders whose frontline software is used inconsistently say the same
1

Make capture easier than paper

Closes gap one
2

Standardize how issues get recorded

Closes gap four
3

Close the loop

Closes gap three
4

Connect it

Closes gap two
What each step looks like on the floor, and the order to tackle them for your segment, is in the report.
Customer stories

Manufacturers already closing the gaps

Make capture easier than paper
46%Increased completion rates for audits and quality checks

“Having a tool that’s so adaptable and easy to use has not only saved us time but has also made our work environment much more enjoyable.”

Mars · Waco plant
Standardize how issues get recorded
100+Abnormalities logged by 60+ active users within 90 days

“Weever removed barriers, making it easy for associates to report issues, and the data’s accuracy gave everyone confidence in the process.”

Royal Canin
Close the loop
92%Close rate on submitted behaviour-based safety observations

“Weever helped us increase engagement and participation instantly.”

Baywater
Connect it
3–4 hrsReporting time saved every day

“We could also see where issues were happening and make adjustments quickly.”

Monin
About the report

Questions before you download

Who took the survey?

169 leaders at manufacturing organizations with 500 or more employees, including plant managers, production managers, continuous improvement managers, VPs and executives.

What does the report cover?

Five findings on AI adoption and frontline data, the four gaps behind them, the three readiness segments, and four recommendations with a starting point for each segment.

How long is it?

About 20 minutes end to end. Each finding stands on its own, so you can go straight to the sections that matter to your plant.

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Get the full State of AI Data Readiness report

Five findings · the data behind each one
The four gaps · how they show up on the plant floor
Three readiness segments · where each one should start
Four recommendations · in the order to tackle them
Download the report Free · A 20-minute read
Download the reportFree · A 20-minute read · October 2026