4 Steps to Shop for Your Next Enterprise Software Solution With AI
INSIDE THIS ARTICLE
In today’s fast-paced digital world, selecting the right enterprise software can be daunting. The right decision can solve problems, reduce company risk, and have your team celebrating. On the other hand, the wrong decision can create new problems, increase risks, and negatively impact your career.
Heatherly Bucher and Jonathan Cohn are both practitioners of artificial intelligence (AI) at work and in life. They use AI regularly to do substantial tasks—research topics, assist with longitudinal studies, and contribute to strategy conversations—and they use AI to help shop for business software solutions. In this blog, they share their process, prompts and all, in hopes that this helps your team find the best choice for your needs—whether that is PLM, QMS, supply chain, MES, ERP, CRM, or other solutions.
4 Steps to Get AI Assistants Busy Shopping for Your Next Solution
1. Choose the right AI suited to your task.
Not all AIs are equal, and many are designed for specific use cases, from finding the perfect restaurant for an offsite to developing revolutionary, eco-friendly, waterproof coatings. There are many types of AI (and it can get esoteric to try to agree on the number and divisions). Even within a category of AI, different AI offerings use different large language models (LLMs) with varying constraints and reach.
You may use Midjourney or Canva if you need cosmic squirrel art or new LinkedIn banner images. Grammarly’s powerful AI features are great for getting your first novel out of your brain and to the finish line. StyleAI might help you with that special outfit for your cousin’s wedding. For enterprise software shopping, you need a research-oriented AI that can perform web searches for the most up-to-date information possible.
Use an AI with a “research” mode turned on to make sure it is double-checking its work and providing sources for all facts that you can double-check and explore. It may take a while to generate output, but the quality will dramatically improve. Also, remember that cloud-based enterprise software like Arena by PTC continuously releases new features and updates. Using an AI with stale data may give you inaccurate information and should not be trusted.
We’ve provided some AI recommendations based on our testing to get you started.
Explore your AI’s settings
Start by asking your AI if it has special settings or access to private data. Some AIs, like Microsoft 365 Copilot and Claude, may have access to special settings that are designed for your specific use case. Your AI may also have access to your company’s internal documents and communications, which can help it tailor its findings to your specific needs.
2. Prompt the AI.
Before you can construct a good prompt, you need to identify business needs and capture your requirements. What your team needs is not identical to what other teams need. AI will provide back generic non-actionable results if you don’t provide the context of your business needs and requirements. If you aren’t entirely sure if your requirement list is complete or accurate, consider also using AI to help review it.
Good input equals better output
Prompt engineering is a skill, and every AI has its own personality and peculiarities. Getting one AI to generate useful information may require a very different prompt from another. Some may work best with a detailed, lengthy prompt while others generate more useful answers from shorter, general questions. Others are sycophantic and work best when you tell them to act as devil’s advocate. However, you don’t have to have a degree in machine learning or AI to construct a good prompt. You do need an understanding of what a good prompt is, an iterative experience, and the willingness in the community to share good prompts.
Try multiple AI Platforms
The results in your experiments can vary dramatically. Go wild and try several. If this is your first time using AI for a structured research project, running the prompt through multiple platforms will help you see the differences between tools and give you better overall content to use in your search.
3. Interpret the AI results.
AI is fallible and AI hallucinates
Hallucinations are when our sensory perception does not correspond to the external stimuli. AI hallucinations occur when the algorithmic system gives information that seems plausible but is false, inaccurate, or misleading. (Dive deeper into this topic with researchers here.) This is particularly true when asking questions about enterprise software, for which publicly available documentation is often unavailable. You can mitigate this issue by turning on “Research mode,” but even this is not a guarantee. Software selection is important to your company and, therefore, to you and your team. AI hallucinations can cause real damage to yourself, your colleagues, and your business when used unchecked. If something sounds too good to be true, make sure to verify by reviewing the AI’s sources. See step 4 for more recommendations on how to mitigate this risk.
Iterate with the AI to improve the results
Ask follow-up questions to dig deeper into a topic or have the AI consider the issue from a different perspective. You can have conversations with the AI to improve the results (until you run out of credits if using a free or limited version of the tool). You may also find that the AI’s clarifying questions or results give you improvements to the initial prompt that you want to re-run or run in another tool.
Software vendors do not publish everything about their companies, product roadmaps, functionality, and customer commitments. As AI-powered search and research tools have gained dominance and are fast replacing traditional web searches, all companies are now working to rapidly catch up to the new AEO (answer engine optimization) that is replacing traditional SEO (search engine optimization). What does this mean? All of us—including your own company—are busy re-evaluating the content breadth, depth, and structure on our public sites and other locations.
4. Engage people to continue the selection process.
Humans need to be in the loop at every step—from identifying business needs and requirements to running well-executed AI research to giving vendors the chance to respond to what you have learned so far in your research—answering your questions, providing customer references, and all the other critical information you need to make a great decision. Additionally, you may have a very detailed requirements list with weightings and other roll-up considerations.
Generic AI research tools often lack access to the proprietary or nonpublic data needed to fully understand your requirements. As a result, they may misinterpret what you’ve uploaded or fail to assess whether a specific vendor can meet your needs. Just like working with a colleague, asking the AI to rephrase your requirements in its own words can help surface misunderstandings and clarify intent.
Help vendors help you
Vendors can better respond to your search when you are transparent about your business needs and requirements. And, if you have used AI tools for research, let the vendors know and ask them to respond to the results and prove out their solutions.
How to Put AI Prompts to Work for Your Next Software Search
All-In-One Prompt to Identify Possible Solutions“You are an expert enterprise software consultant helping a team select the optimal software solution for their organization. Your role is to guide them through a comprehensive evaluation process and ask clarifying questions as needed. Include pros and cons. Also, provide clear next steps and a decision framework. Provide potential challenges and risk mitigation strategies. Include sources.” |
|
| Tone | Objective, analytical, and professional. |
|
Task |
Include the following in the comparison:
|
| Team Scenario | Enter details about your team’s needs. |
| Industry/Domain | Type of software (e.g., PLM, CRM, ERP, HR management, project management, marketing automation) |
| Organizational Context |
|
| Specific Needs and Examples |
|
What’s Next in AI for Software Research?
The development and evolution of AI is on an exponential path with no evidence that adoption and application will slow. We learn every time we use AI at work—each instance has the potential to make our work better or worse if we don’t know how to inspect the results for data gaps, poor prompts, or AI peculiarities. We see the continued use of AI for business research, including software needs, to grow and become more nuanced. What will the future include? Maybe we’ll have new AI tools trained on specific software categories. We’ll see improvements in AI overlays to existing software review sites. And we are already amid a brand and communication innovation cycle where all companies must now make the data about us and our solutions more AI-friendly.
Do you have an interesting way you’ve used AI to find software or a prompt you want to share? Drop Heatherly or Jonathan a note on LinkedIn! They’d love to chat about it.
Heatherly Bucher