July 20 2025 at 06:35PM
Rushing to Buy AI Software? The Project Manager's Guide to Smarter Evaluation
As AI features become more common in enterprise software, project managers are being pulled into high-stakes decisions, often without clear guidance. Vendors promise transformation, but too often, the tools don’t deliver.
This guide helps you lead structured, confident AI software evaluations, even if you're not a deep technical expert. You'll learn how to reduce regret, clarify value, and guide your team toward smarter decisions.
You’ll learn how to:
- Recognize the root causes of software regret and how to avoid them
- Lead structured, cross-functional evaluations of AI software
- Clarify technical complexity and connect features to business outcomes
- Ask sharp, strategic questions, even without a technical background
The software regret problem, and how project managers can help
As AI hype fuels a surge in software spending, many organizations rush into purchases they later regret. While 3 out of 4 companies plan to increase software spending this year, fewer than half are fully satisfied with their recent purchases.
Software churn is the norm
A staggering 90% of companies have replaced at least one software tool in the past two years, and 81% plan to do so again. Each replacement takes an average of 9 months, time that could be spent on innovation instead of rework.
Regret is widespread
Nearly 7 out of 10 buyers say they regret at least one software purchase made in the last 18 months.
Project managers help reverse this tendency
When project managers lead the process, bringing structure, cross-functional alignment, and a focus on outcomes, teams make smarter software decisions, and regret drops.
By the numbers
- 75% of companies plan to increase software investments this year
- Only 40% are satisfied with recent purchases
- 68% regret at least one software decision in the past 18 months
- 90% have replaced at least one tool in the past two years
- Project manager involvement reduces regret by 13%
Why project managers are the key to smarter AI software decisions
According to Software Advice research, when project managers are involved in software evaluation, regret drops by 13% compared to the global average across all job roles. Even more compelling, project success rates double when project managers lead the process.
The disparity between the dramatic lift in success and the modest drop in regret underscores a critical insight: project success is largely operational, while regret is perceptual and experiential. Project managers excel at the former but can only partially influence the latter. Success is measurable, while regret is emotional.
Project managers reduce software regret not by chance, but by design. These core competencies position you to lead smarter, more strategic software decisions:
- Disciplined evaluation
You bring structure to what is often an unstructured process. With clear timelines, decision gates, and evaluation criteria, you help teams make deliberate, well-informed software decisions. - Cross-functional insight
Software decisions rarely affect just one team. You are skilled at bringing together stakeholders from IT, operations, finance, and end-user groups to surface diverse needs and avoid blind spots. - Risk awareness
From integration challenges to data privacy concerns, you are trained to identify and mitigate risks early. You ask the right questions about scalability, compliance, and long-term support before contracts are signed. - Outcome focus
You keep the team grounded in business goals. You help stakeholders look past flashy demos and focus on whether the solution truly meets the company’s needs now and in the future.
Example
The operations team at a mid-sized logistics company was excited to buy a new AI-powered scheduling tool after a vendor demo. The project manager, aware that the team already relied on a patchwork of tools, wanted to avoid overbuying. They initiated a structured evaluation process:
- Stakeholder interviews revealed that while the team liked their current tool, they needed faster and more advanced capabilities.
- Cost-benefit analysis showed that adding a low-cost extension to the existing system would meet 90% of the operations team’s needs.
- Risk assessment surfaced potential integration and training challenges of adopting a new tool.
Instead, they invested in a modular upgrade to their current platform, achieving the desired functionality with minimal disruption.
How to bring clarity to AI software selection
AI tools, from natural language processing (NLP) to predictive analytics and intelligent automation, promise transformative value. But with innovation comes uncertainty.
Many teams struggle to evaluate AI tools effectively, often overwhelmed by:
- Technical jargon that obscures real capabilities
- Unclear use cases that make it hard to connect features to outcomes
- Marketing hype that blurs the line between promise and practicality, leading to misaligned expectations and missed opportunities
This is where your perspective makes a difference. By applying structured evaluation frameworks, you can help teams clarify complexity and focus on what matters. Here’s how:
- Break down complexity
Use structured frameworks, such as SWOT analysis or a decision matrix, to simplify complex decisions into manageable components. Start with key criteria like cost, impact, and feasibility. - Guide thoughtful decision-making
Follow a step-by-step process to ensure all relevant factors are considered. Involve diverse stakeholders to gather a range of perspectives and reduce blind spots. - Guard against bias and hype
Use the framework to stay grounded in strategy. Pause to assess how each option aligns with long-term goals before making a final decision.
In short, you act as translator, strategist, and risk mitigator, ensuring that AI investments are clear, aligned, and sustainable.
Example
A healthcare provider was evaluating an AI-powered documentation assistant for clinicians. The vendor emphasized its advanced NLP capabilities.
During the discovery phase, however, the project manager surfaced a different priority: clinicians cared less about cutting-edge features and more about integration with their existing electronic health record (EHR) system and ease of use.
Instead of choosing the flashiest tool, the team selected a simpler solution with better EHR integration and higher user satisfaction scores.
The result? Faster adoption, fewer support tickets, and a smoother rollout.
Lead AI selection with confidence, no coding required
You don’t need to be a data scientist to lead an AI project effectively. But you do need to understand enough to ask thoughtful questions, manage risks, and translate technical decisions into business outcomes.
The key? Systems thinking over technical depth.
Instead of trying to master algorithms or write code, focus on understanding the bigger picture:
- Business impact
What challenge is the AI solving, and how will success be measured? - Data flow
Where does the data come from, how is it processed, and where does it go? - Dependencies
What systems, teams, or processes will this project touch or disrupt? - Risks
What could go wrong—ethically, operationally, or technically—and how will you monitor it? - Team momentum
How will you keep cross-functional teams aligned, engaged, and moving forward?
When evaluating AI tools, you should focus on four key areas:
- Clarity over novelty
Don’t let flashy features distract you. Ask: What problem does this solve? - Transparency and trust
Are the tool’s decisions explainable? Is the data secure? - User readiness
Do you have training resources, and are users willing to adopt the tool? - Integration and scalability
Will it work with existing systems? Can it grow with your needs?
With the right tools and mindset, you’ll be well-positioned to lead AI software decisions. By applying structure, asking the right questions, and staying focused on outcomes, you can reduce software regret and guide your team toward smarter, more strategic choices.
About Software Advice
Software Advice simplifies software buying. Through 1-on-1 conversations and trusted insights, industry-specific advisors guide buyers to top software options in as little as 15 minutes (and it’s 100% free). Founded in 2005, Software Advice has helped nearly 900,000 businesses find the right software for their specific needs. Software Advice also features over 1.5 million verified user reviews to ensure people feel confident in their technology decisions. For more information, visit softwareadvice.com.
Survey methodology
Software Advice’s 2025 Tech Trends Survey was conducted online in August 2024 among 3,500 respondents in the U.S. (n=700), U.K. (n=350), Canada (n=350), Australia (n=350), France (n=350), India (n=350), Germany (n=350), Brazil (n=350), and Japan (n=350), at businesses across multiple industries and company sizes (five or more employees).
The survey was designed to understand the timeline, organizational challenges, adoption & budget, vendor research behaviors, ROI expectations, and satisfaction levels for software buyers. Respondents were screened to ensure their involvement in business software purchasing decisions.
