From Idea to Impact: Enpro’s 4-Step AI Deployment Blueprint
- Hao Dinh
- May 29
- 6 min read
Updated: Aug 10
At Enpro, we don’t just talk about AI, we deploy it with purpose. Our 4-step process helps teams turn bold ideas into real-world results. In this blog, you’ll learn how we move from identifying an opportunity to scaling AI solutions that deliver measurable business value. Along the way, you’ll get hands-on experience developing a business case, prototyping a solution, piloting with real data, and, if successful, scaling the AI solution across the enterprise. Let’s dive into how we make AI practical, impactful, and built to last.
This 4-step process is designed to deploy AI solutions that deliver real business value—while keeping costs low and risks minimal.
Develop a Business Case – Identify high-impact opportunities and define success upfront.
Prototype with Sample Data – Build a quick, low-cost model to test feasibility.
Pilot with Real Data – Validate the solution in a real environment using actual data.
Scale to Production – Roll out proven solutions across the organization.
Each step includes a checkpoint. If business value is confirmed, the process moves forward. If not, it stops, saving time and resources. This approach ensures AI investments are smart, scalable, and results driven.
Get a quick look at how AI goes from idea to impact in this short video! You’ll learn the 4-step process: build a business case, prototype fast, pilot with real data, and scale what works. With checkpoints at every stage, it’s a smart, low-risk way to turn AI into real results.
Dive into the 4-step AI deployment process through guided, hands-on activities. You'll learn by doing, developing a business case, prototyping a solution, piloting with real data, and exploring how to scale AI that drives real value. Each step focuses on validating business impact early, keeping costs low, and minimizing risk throughout the journey.
Step 1: Develop a Business Case
Goal: Identify a clear business problem where AI could add measurable value.
Activities: Engage stakeholders, define success metrics, estimate costs.
Checkpoint: Does the opportunity show strong potential business value?
✅ Yes: Proceed to prototyping.
❌ No: Stop here—no time or money wasted.
Hands On Business Case Assessment:
In the below videos, you’ll walk through an interactive assessment designed to quickly evaluate an AI opportunity. This triage step helps us identify ideas with real potential, so we can focus our efforts on what’s worth pursuing and set aside those that aren’t.
AI Opportunity Overview and Initial Business Case Assessment: 5 mins
Let’s take a deeper dive into the business case to determine whether this AI opportunity warrants further time, effort, and investment.
Step 2: Prototype a Solution Using Sample Data
Goal: Build a low-cost, no-risk prototype to quickly and inexpensively validate key assumptions from the business case.
Activities: Use synthetic or sample data to design and train a basic AI model.
Checkpoint: Does the prototype show promise based on early results?
✅ Yes: Move to pilot.
❌ No: Stop and reassess.
Watch this video to learn why prototyping matters and how it helps ensure your AI project is on the right track.
📈 Powerful Revenue Forecasts 🤖 Made Simple with AI
Below are the key assumptions, per the business case for Jane's AI enabled sales forecasting opportunity:
Let's walk through a simple step-by-step guide to develop a prototype to validate some of the assumptions.
Step 2a: Define the prototype scope
The prototype should be built quickly and cost-effectively, with the goal of validating key assumptions. The objective isn’t to deliver a production-ready solution, but to determine whether those assumptions are confirmed, or can reasonably be confirmed.
Let's build an AI prototype to address the below assumptions in red:
Step 2b: Obtain and Review Data
You can use AI to generate synthetic data for your prototype. Remember, the goal isn’t to build a production solution with real data, it’s to create a quick, low-cost prototype that tests whether the AI solution can validate your assumptions. Synthetic data is ideal for this purpose. Below is the synthetic dataset created for this prototype.
1. Historical Sales (Daily)
Daily transaction-level sales data per customer and product, including units, revenue, discount rates, prices, and sales channel.
2. Sales Pipeline (Opps)
Details of open and upcoming sales opportunities, including stage, probability of close, expected close date, quantity, and source.
3. Market Trends (Weekly)
Weekly demand, competitor pricing index, and search interest trends for each product, reflecting broader market conditions.
4. Customer Buying Patterns
Weekly insights on customer order frequency, average order size, preferred purchase channel, and repurchase probability per product.
5. Inventory Levels (Daily)
Daily stock on hand, demand, restock events, and backorders for each product, showing inventory movement over time.
6. Supplier Lead Times
Weekly average supplier delivery times in days for each product, used to assess supply chain responsiveness.
7. Production (Weekly)
Weekly planned production capacity and downtime hours per product, useful for identifying manufacturing bottlenecks.
8. Promotional Campaigns
Campaign-level data on promotions, including start/end dates, spend, discount rates, channel, and observed sales uplift.
Sample Data Details:
Assume the date is: Feb 16, 2025
Past Sales Data: Feb 17, 2024 - Feb 15, 2025
Current Pipeline Data: Feb 16, 2025 - May 16, 2025
Market Trends Data: Feb 17, 2024 - May 16, 2025
Customer Buying Behavior: Feb 17, 2024 - Feb 15, 2025
Data is for 10 customers and products A, B, C
Download the sample data here:
Step 2c: Use Microsoft Copilot AI to generate the prototype
Upload the sample data and let AI reveal key insights, testing whether the prototype truly validates or can confirm the assumptions
In Copilot type: "Using the ‘Historical Sales (Daily)’ and ‘Market Trends (Weekly)’ sheets, build a forecast for the next 3 months of units sold for each product. Factor in demand trends, seasonality, and recent promotional uplifts” (Make sure to upload the "AI_Sales_Forecasting_SampleData.xlsx" file)
Step 2d: Interpret the Results
Partner with the business (in this case, Jane and her finance team) to evaluate the accuracy and business value of the AI results.
Download a Copilot interaction from the sales forecast request, review the AI’s answer, and test the prompt yourself.
The sample data contains countless insights AI can uncover. Download a set of Copilot prompts to test and showcase the prototype’s full potential, an exercise that will demonstrate AI’s power and give you hands-on experience using it.
The goal is for you and the business to determine whether the AI prototype can validate, or, with further development, help validate, the key assumptions highlighted in red below.
For example:
Does the finance team believe the AI prototype can help reduce their forecasting cycle time?
Does the finance team believe incorporating external data can improve current sales predictions?
After interacting with the AI prototype, does the finance team feel confident they can use it to forecast effectively?
Assume Jane and the finance team conclude that the prototype has validated some of the assumptions and can deliver value. The next step would be to pilot an AI solution. However, if any key assumptions are invalidated, the business case fails and no further investment should be made.
Step 3: Pilot the Solution with Enpro Data
Goal: Test the prototype in a real-world environment using actual Enpro data.
Activities: Validate performance, refine workflows, assess ROI against business case.
Checkpoint: Does the pilot deliver measurable value and align with operations?
✅ Yes: Prepare to scale.
❌ No: Discontinue or pivot.
The purpose of this step is to pilot an AI solution. Unlike the prototype, the pilot will use Enpro’s actual data and be developed within the same environment as the eventual production solution. While the pilot will require investment, the prototype has already confirmed that there is business value in moving forward.
The pilot serves as the final checkpoint before full-scale deployment. If it validates the business case, confirming assumptions and delivering the forecasted ROI, we move to production, unlocking enterprise-wide value. If it fails to validate key assumptions, investment stops, avoiding wasted resources and ensuring disciplined decision-making.
Step 4: Scale the Solution into Production
Goal: Operationalize the AI solution across relevant functions or divisions.
Activities: Integrate into systems, train users, monitor performance, ensure governance.
Outcome: Realized business value with enterprise-ready AI, at scale.
Why The 4-Step Process Works
By embedding checkpoints throughout the process, we avoid sunk costs and ensure we only scale AI solutions that prove their worth. It’s a smarter, faster, and lower-risk way to turn innovation into impact.
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