CEO Perspective: Manufacturing's 'Process' is the Key in the AI Era
Recently working in manufacturing, talking with nearly 20 partners in manufacturing frontline and management, I found what really keeps enterprises awake at night often isn't how hard the technology is, but how chaotic the processes are.
And I'm often asked by these seniors:
"Tim, what AI should we deploy to see immediate results?"
My experience is, this question itself might lead you down the wrong path.
▍First, a Common Misconception
Many companies treat AI as a "tool project": business departments propose requirements, IT departments find solutions, then hope to find AI that can automate existing processes once and for all.
But reality is often: after going live, they discover processes themselves are stuck in reports, Excel paperwork, even departmental authority and responsibility conflicts. AI not only didn't solve problems, it made original black holes bigger.
Where's the problem? Usually not technology, but organization.
* Authority fragmented: Processes cut too scattered, each department only manages their segment, no one responsible for final results.
* Incentives misaligned: Bonuses only tied to sales volume, production volume, yield rate, no one responsible for "are processes stable," "is logic correct." People naturally choose daily firefighting, not stopping to redesign.
* Resources misallocated: Most budget spent on hardware and new systems, process organization and restructuring gets no investment. Result: using faster AI to run a chaotic process.
Finally, KPI reports might look good, but hard metrics like gross margin, cash flow, delivery dates show little movement after three years.
▍Starting from Two Anonymized Cases
Case One: Semiconductor Equipment Manufacturer's "AI Firefighting Team"
A top semiconductor equipment manufacturer deployed AI for predictive maintenance last year, model accuracy reached 90%. Goal was to reduce downtime 30%.
Six months later, downtime only dropped 8%. Field engineers complained: too many AI alerts, can't handle them all.
Deep dive revealed the real bottlenecks:
1. Maintenance work orders require layers of approval, AI alerts say handle immediately, but tickets still stuck three days before next step.
2. Production scheduling relies on Excel manual adjustment, employees make typos, high-risk machines can't get scheduled into maintenance windows.
3. Parts inventory scattered across different systems, AI checks ERP says parts available for immediate replacement, warehouse replies no stock.
Conflict is typical: IT team thinks model is accurate, it's field's problem; field thinks processes unchanged, AI just adds to their workload.
Later adjustments:
Board decided to form a cross-departmental process team, led by operations VP, IT, production, procurement, maintenance all sit down.
1. First don't do more AI, but map complete value stream, cut nearly 40% of steps that don't create real profit.
2. Redefine AI's role: from "issuing parts replacement alerts" to "auto-triggering work orders, linking scheduling, reserving parts."
3. Change KPIs from "model accuracy" to "average maintenance response time."
18 months later, downtime dropped 35%, maintenance efficiency improved 25%. Now AI truly became process stabilizer, not alarm system.
Case Two: Consumer Electronics OEM's "Capacity Expansion Bottleneck"
An OEM decided to expand capacity and deploy AI scheduling system with ERP integration to handle new orders. But six months later, capacity utilization stuck at 60%, yield fluctuates, delivery dates unstable.
Problem root isn't AI algorithms, but foundation:
1. Demand forecasting and capacity planning are two departments using two sets of data and systems, scheduling foundation is wrong.
2. When switching production models, rely on paper and verbal handoffs, AI dashboard shows machines idle, but field waiting for engineers and fixtures.
3. Supplier delivery dates unstable, but data not real-time, AI scheduling can only assume everything on time.
Later adjustments:
They found consultants to convince management to pause blind expansion, stabilize processes first.
1. Use AI first for "diagnosis," simulate different scenarios, let executives see trade-offs between capacity, delivery dates, costs.
2. Digitize key SOPs, link with production systems, ensure can't start without personnel, fixtures, materials in place.
3. Build data bridges with core suppliers, let scheduling dynamically adjust based on real arrival status.
12 months later, capacity utilization improved to 85%, yield fluctuation reduced 40%, delivery achievement rate improved from 70% to 95%.
▍Reflection and Recommendations
These cases gave me several clear insights:
1. AI is process "truth mirror" and "magnifying glass," not "firefighting team": Rather than first asking "what can AI do," first ask "where do we most rely on manual firefighting, Excel, and phone coordination." When processes are clear, where to put AI becomes obvious.
2. Process restructuring should be treated as "strategic investment" not optional "consulting fees": Only investing in hardware and software without investing in process organization is like buying the best engine, but installing it on a machine with stuck gears.
3. Stable processes themselves are the deepest moat: Being able to deliver consistently and reliably far wins more customer trust and pricing power than having a flashy technology.
"The Art of War" says "The expert in battle moves the enemy, and is not moved by him." In management, my understanding is: you need to control business operation rhythm, not be led by endless problems.
In the next few years, manufacturing's real watershed might not be who has the most cutting-edge AI, but who has the most stable, hardest-to-replicate operational processes.
AI itself isn't scary, chaotic processes are the biggest risk. When processes are stable, AI will naturally become your most powerful leverage.