How Value Engineers Can Make The Most Out Of AI + Three Sample GPT Prompts

When artificial intelligence (AI) burst onto the scene in late 2022 with the launch of ChatGPT, it had a big impact on the sales world. It wasn’t long before sales teams bought AI into large parts of their go-to-market motion, from outreach to customer management. 

But, one sales area in which AI hasn’t been fully leveraged is value engineering. 

Value engineers are one of the most important cogs in the enterprise sales engine. It’s their job to ensure that their company’s products align perfectly with the client's needs and provide tangible value to the customer. 

However, most companies don’t have the budget to scale their value engineering team. Even when they do, good value engineers are super hard to find. 

As a result, most value engineering teams are small and overstretched. They will typically focus on the most important and strategic deals, while limiting their efforts on roughly 80% of other sales opportunities. 

Fortunately, there’s a great opportunity for value engineers to use use artificial intelligence (AI) to deal with some of these problems and, ultimately, scale up the amount of sales opportunities they can work on. 

Use Case One: Drafting Value-Centric Executive Summaries

One of the most important aspects of value engineering is to link your product to the strategic objectives of your lead. You’ll get most of this information from a call, and there are already tools that use AI to generate summaries from the transcript. 

Unfortunately, most of these tools’ generated summaries tend to be a bit general and don’t specifically focus on value: 

• Why at all?
• Why now?
• Why us? 

But, if you have the raw transcript, you can run it through GPT to create value-centric summaries. Here’s a good example of a prompt: 

“Imagine I am a value engineering leader at a software company. I will attach two transcripts to this message. Please help me in creating the 3 Whys (Why at all, Why now, Why us?) based on the problem statement the customer outlined. I will give you more information on us below, with a summary of our value proposition. Please consider that each of the Why’s should have, at most, three bullet points with, at most, 2-3 sentences each.”

Then, you’d follow that up with some information regarding your value proposition and ask it to write the summary.

If you’ve got ChatGPT Plus, you can create a custom GPT, which is preloaded with all relevant information about your company and the previously mentioned prompt instructions. Then, you only have to send the transcripts with each new summary. You can also share the bot with your wider team. 

Remember, GPT won’t add everything. Think of it as a productivity tool to write first drafts and add all extra relevant value points yourself. 

Use Case Two: Proof-Read Value Drivers, Business Outcomes and Use-Cases

According to a report by Kaizen, only 35% of organizations can clearly define their value proposition. To make matters worse, only 15% of organizations have their value proposition clearly understood by their customers. 

If value propositions aren’t clear or understood, then they aren’t as useful as they could be. So, three of the most important responsibilities of value engineers are to:

• Clearly define the value proposition (create the value driver).
• Work out how to quantify the value proposition (link it to a business outcome).
• Explain how it all works in practice (give specific use cases).

The most important part of this is relevancy. How you define the value proposition, how you measure it, and how it works in practice must resonate with the specific prospect and company’s concerns and objectives. 

GPT can help you proofread this; it can’t do it for you (thankfully, or you might be out of a job!). 

Here’s a prompt you might use: 

“I’m going to give you my company's value proposition, some key measurables/business outcomes that it enables for buyers, and some specific instances of how it might be used (use cases). Please reword the prop and identify new metrics and use cases that make it relevant for this particular prospect:

{{myvaluepropinformation}}

{{audience}}”

Again, it helps if you have GPT Plus your company’s value proposition, but it’s not essential. 

Use Case Three: Help Quantify The Value Of Specific Use Cases

Okay, this is slightly similar to the last one, but it digs a little deeper into the specific use cases themselves. We already discussed quantifying business outcomes, but AI can help quantify the value of specific use cases too. 

Sales budgets aren’t as relaxed as they used to be, meaning that buyers will want to see tangible outcomes to your solution. If your use cases aren’t linked to specific metrics, then you’ll struggle to persuade a buyer that your solution is anything more than a “nice idea”. 

You’ll need to dip into your company’s case study pool for this prompt to give the AI a better idea since it can sometimes struggle with abstract concepts. Try this prompt: 

“I’m going to give you a case study of how my company helped another company {{insert use cases}} (use case). Create a benefit calculation/value calculation / ROI calculation that communicates the value of each use case in real, quantifiable terms.”

GPT can struggle with this one, so you might have to tinker with it slightly. In any case, once you get it working, it’s a great time saver in the long haul. 

Minoa

While AI certainly can’t do your job better than you, it can save you a decent amount of time. Moreover, we’re barely scratching the surface of what AI can do for value engineers more broadly. 

ChatGPT itself, however, is more limited – especially if you don’t have the full version. 

So, with Minoa, we created our own AI that combines all of these different functions with our unique value framework and customer collaboration capabilities. It’s a single unified platform that helps teams engineer value in every single deal. 

If you want to see it in action, hit us a note and we can help out and show you a demo of Minoa!