Q835

Many organizations launch multiple AI initiatives in parallel — pilots here, automations there — without a clear sequence. The result is often fragmented success, stretched teams, and diluted value.

Think of your domain or organization.
If you had to plan AI adoption over the next 12–24 months, what should come first, next, and last — and why?

How would you decide the right sequence so early wins build confidence and later initiatives deliver scale and sustainability?

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Manufacturing Domain: Aerospace subcontract precision machining shop

If I had to plan our AI adoption plan for upcoming 12 – 24 months, here is the exact sequence I would enforce, no debate.

Phase 1 (Months 1 - 6):- Quote-to-Order process — use of AI in RFQ analysis, pricing

(We already did this in 2025, which is why I am so adamant about starting here)

Why first?

Fastest, best ROI: increased quote response rate from 38% to 92%, added 20 million euros of backlog that would have been missed, and the system paid for itself 20 times in the first year.

No risks associated with production or quality. Everything front office. No headaches with certifications. No scrapping of a part if it’s incorrect.

Gives the customer an instant boost to confidence: Salespeople love it, the finance guys see the revenue, while the operations guys don't even notice the difference. A win for the management that they can tout without worrying about anything else

It creates cash and capacity room for all that comes afterwards.

Phase 2 (Months 6-12): Shop-Floor Scheduling and Real-Time Capacity Optimization

(AI that uses the confirmed order book, actual machine status, availability of tools, and skills of operators to create a schedule of the day/week dynamically)

Why Next?

Now, there are more orders than in the past (because of Phase 1), so the constraint shifts inside the shop.

Builds directly on the quoting data – the same models can take advantage of the increased pipeline visibility.

Moderate Risk: Doesn’t affect either NC programs or quality – it’s all about sequencing. Facile to implement parallel manual system for safety net.

Payoff: 15-25% gain in OEE, reduced overtime, faster delivery, happy customers, and therefore easier future sales.

Phase 3 (Months 12–18): Generating NC Programs and Prove them Out (complete AI-agent generated) NC program

(CAM + Collision-Free Path Search by virtualization simulation)

Why here in sequence but not before?

High technologic risk: Incorrect process parameters leads to crashed €4M machine or €800k scrapped part. Requires mature process data from Phase 1 & 2, i.e., actual cycle times, tool wear patterns.

"FAI (First Article Inspection) Buy off" needs customer buy-in, and we need wins before that can occur.

Massive payoff: preparation time from weeks to couple of days, but again only worth the effort if  we have adequate high margin orders coming in and money built up from earlier stages.

Phase 4 (Months 18–24): In-process quality prediction using AI monitored SPC charts and automated inspection disposition

(Vision + Sensor AI that predicts surface finish / defect risk and auto-release the good parts and hold bad once)

Why last?

This

Has the highest Risk to Quality and certification - one bad release can kill a project / program.

Needs a solid data pipeline from all previous phases (correct schedules and NC correct programs).

Biggest Long term financial Impact: Would reduce the head count of  people in our final inspection by 40% and cut lead time by a week, this will making us the preferred supplier of choice.

How we decide the sequence deciding criteria. The rules we actually use

a.      Fast, safe ’wins’ first—front office over shop floor, no quality risk, delta shows up in <6 months.

b.      Follow the constraint - solve the present bottleneck before moving on to the next one. Do not optimize your programming until you have orders.

c.      Data dependency. Later phases require clean, good data produced by earlier phases. Garbage in, garbage out later.

d.      Organizational energy --- begin where the crowd will cheers, not where they will fight (sales cheers when quoting AI, operators are afraid of inspection AI).

e.      Cash and confidence snowball - each phase funds the next one and proves to the doubters that ‘this AI thing actually works here’.

We did not follow this logic once – went directly to an AI-driven tool life predictor for the machines and it failed as it had poor uptake.

Now we're religious about sequencing. It’s like this:

Do it this way and it’s like we’re playing a series of games and we’re winning, while without it, it’s like Do it randomly, you will burn money and trust.

That is the plan I am implementing – and sleeping better because of it.

🏆 Excellent Answer: Adil Khan

Exceptionally strong and well-sequenced response with:

• A clear 12–24 month roadmap grounded in real aerospace manufacturing constraints

• Logical progression from low-risk, high-ROI front-office wins to high-impact, high-risk shop-floor applications

• Explicit sequencing logic based on constraints, data dependency, cash flow, and organizational adoption

• Honest reflection on a past misstep, reinforcing learning and credibility

This is a textbook example of how early wins build confidence, fund later stages, and reduce risk while scaling AI sustainably.