Q833

AI solutions are often praised for being “smart,” “fast,” or “innovative.”

But beyond technical success, organizations struggle to answer a tougher question: Is the AI truly creating measurable business value?

Think of a specific process in your domain where AI has been introduced (or is being planned).

How would you define, measure, and track value from that AI solution — beyond basic metrics like accuracy or response time?

What would convince you that the AI deserves continued investment?

⚠️ Any answer that is generic or does not connect with a specific, relevant process will not be approved.

🏆 The best answer will be selected on the basis of:

• Relevance of the chosen process

• Clarity in defining value (financial, customer, or operational)

• Practicality of the measurement approach

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Domain: Precision Machining + Special Process Aerospace supplier for Big OEM’s

The specific AI solution: AI agent developed for supportitng  quoting,  bid / no-bid decision  for incoming RFQs (rolled out at the beginning of Jan 2025 – current running)

Everyone from Top management to middle management loved it from day one of going Live after 6 month of trials. Quotation process that used to take us  about 8 – 14 days now completed in 4–6 hours. The tool is so close to accurate on cycle times for each operation (trained based on historic data), sales team is happy.

But during Board meeting came a killer question from corporate CFO, three months in: “Nice toy you have installed, but is it actually making us more money or are we just padding our backing using fancy terms AI-Enabled quoting? Can any one tell me what is a bottom line impact.”

A Team was defined to analyse and report to Top management if there is any  bottom line impact after installation of this so called new AI Agent.

How we defined “real value” after several rounds of some serious discussion (beyond speed or accuracy)

We agreed on three hard business outcomes (all about money) , not tech KPIs:

1. Revenue (Trun Over) impact — more won contracts from RFQs we actually chased for high margin once.

2. Margin protection — We don’t win more junk orders that makes us operations loses.

3. Capacity utilization — Better machines allocation we work only on more profitable work (no more using of 5 - Axis machines for low-margin jobs).

Everything other KPI (response time, prediction accuracy) is just a leading indicator to make us money.

How we measure now and track its perfomance — brutally practical

We created a simple monthly “AI Value Dashboard” that nobody can argue with:

• Baseline period : Jan – Apr 2024 (pre-AI installation)

• Post-AI: from Feb 2025 onward (gave one month ramp-up time)

Some key metrices we track every month:

1. RFQ hit rate (Quotes submitted / RFQ’s received) Pre – AI installation: approx. 38% (we cherry - picked because our quoting process was slow) Post-AI: 88 – 94% → We now chase almost everything viable finally decide what to Bud and what to No-Bid.

2. Win rate on submitted quotes Stayed stable at 31 – 34% — good sign we’re not just spraying low prices to win more otders.

3. Revenue from new won awards resulting to faster quoting We tag every new won contract with “would we have quoted in time without AI? Yes/No/Maybe”. 2025 YTD: €19.8M in awarded backlog that sales admits we would have missed pre-AI.

4. Average margin on AI - quoted wins vs pre-AI baseline Pre-AI average gross margin: 24.1% Post-AI: 24.6% (slightly higher because we kill lo margin quotes faster and have room to play with prices on tight-capacity periods or trighter capaity machies).

5. Machine utilization on high margin vs low margin work We dis bucketing each job into margin criteria. Top priceed machine utilization up from 42% to 58% of total spindle hours — because we can now say no to less margin jobs without leaving machines idle.

What convinces me the AI deserves continued investment (and more budget)

We set a simple rule: if the new AI tool pays for itself 5× per year, it stays and gets all the needed upgraded and maintenance (licnece cost so on).

2025 numbers so far (10 months in):

• Total cost of the tool so far (license + internal resource time + training): ~€380k

• Direct Sales revenue Boost (very conservative estimatiom): €19.8M backlog → ~€6.8M gross profit at current margins,

• Plus approx. €1.2M from capacity allocation (higher-margin products mix). We have choice what to go for.

That’s already >20× ROI in year one.

If next year the revenue bosst drops below €3M or gross margins start detoruting , we will kill the AI agent or fine tune or replace it.

Bottom line from the shop floor We don’t care if the AI is “intelligent.” All we case is is it adding more profitable high margin work for the machines and keeps the bad low margin stuff out.

Money in the bank is the only proof that matters. Everything else is just a slide deck.

AI solutions and the benefits we reap, and measure would depend on various factors as to how an Organization or a functional team would want to measure them , as the results and outcomes should align to the OKRs (Objectives and Key results) of the stakeholders and delivery owners.

Speed , Accuracy , Productivity , Efficiency are some of the generic but relevant KPIs that can be measured ant an overall level , but these high level Metrics will have to be broken down to relevant KPIs that define a functions performance and that off an organisation.

No if we elaborate on these generic Metrics to an organisation operating in a particular Industry and then a Functional Team operating within the Organization , these Metrics will have to be defined and tracked to establish that AI deployment was an actual Value add vs another fancy tool that everyone wants to flaunt.

 

Case Study – Metric - Speed

              At an Organisation Level – An employee attendance correction tool, that address specific queries and provides resolution steps, can help speed on organisational Metric targeted towards improving Employee experience on this crucial process as a delay or an error can impact the accurate payout for an employee. With Agentic Bot workflow that can provide and rectify those errors , would help an organisation deliver on their Commitments to their employees with a reduced TAT. That’s where an AI deployment will deliver on Speed measured in TAT (Turn Around Time) KPI. Its easy to track , benchmark vs the pre and post deployment periods and can be communicated objectively to the stakeholders.

              At a Functional Level – In a CX experience organisation , AI integrated into the case documentation process ( an Essential Non Value Add activity)  can speed up the process for experts capturing what was delivered during the transaction, which can speed up process to handle a transaction measured in Terms of AHT( Average Handle Time ) KPI. Speed in terms of Lower AHT, handling a call in less time for customers can help elevate the customer experience with the company and their product support and helping the agents meet their AHT Targets. These KPIs can be measured and tracked for Value over a period of time and can be converted to a $ Value ultimately.

 

Case Study – Metric – Productivity

 

At an Organisation Level – AI Tool integrated into the Staff planning and forecasting processes , highlighting the opportunity areas and gaps from previous runs can help in precise and accurate planning for near future , avoiding unnecessary bench and nonproductive roles in an organisation. Planning for Floating periods and optimizing the hiring windows. With the identified solutions around hiring and staffing , AI tool not only provide value from optimizing the current headcount but also help in avoiding hiring costs , that otherwise may have been difficult to avoid. Hiring Cost Saves resulting into better productivity management can be easily converted to $ Save value for an organisation.

              At a Functional Level – In a Contact Centre environment from operational delivery enablement ,  AI Agents deployed to help experts deliver quick , accurate and expert support during a transaction , helps overall operations to deliver an elevated experience to the customers , deliver the organisation on SOW agreed KPIs and even win more Volumes from the Clients basis the capacity they create in this hybrid model with AI and Human in loop processes. Improved productivity creates capacity which in turn either can lead to efficiency gain or more business , all these KPI can be correlated to Value being derived from AI deployment.

 

In conclusion , its essential we identify the right strategic goals while we deploy AI in our processes and define the OKRs that the initiative aims to achieve through measurable KPI which will help in tying back the value delivered through AI.

Evaluation Result

🏆 Best Answer: Adil Khan

Clear, outcome-driven response with:

• One specific AI use case (RFQ quoting)

• Direct financial metrics (revenue, margin, utilization)

• Before–after baselines and ROI logic
  Strong proof that AI is creating real business value.

✅ Also Approved: Sundeep Kailwoo

Strong strategic view on linking AI to OKRs and business KPIs.

Noted gap: Sundeep's response remains framework-level and does not tie value to a single AI solution with tracked financial outcomes, which the question explicitly asked for.