I bought a competitive intelligence platform for $40,000 a year because the demo was beautiful.
The sales rep showed me automated battlecard generation, real-time competitive alerts, AI-powered positioning analysis, integration with Salesforce, Slack, and Gong. It looked like exactly what our team needed. I signed the contract in March.
By September, our login count was 47. For the entire team. Over six months.
The platform wasn't bad—it was actually quite good. The problem was that it didn't fit how our team actually worked. We already had a competitive intel process built around Slack channels and Google Docs. The new platform required everyone to change their workflow to use a separate tool. Nobody wanted to.
Worse, I'd bought it without talking to the people who would actually use it. I evaluated the tool based on what I thought the team needed, not what they'd actually adopt. The result was $40K worth of shelfware and a seriously damaged credibility when I had to explain the wasted spend in our next budget review.
That experience taught me the difference between buying impressive tools and implementing useful ones.
Why Most PMM Tools Never Get Adopted
After the competitive intel disaster, I audited our entire PMM tech stack. We had subscriptions to eleven different tools. Average utilization across all of them was 34%.
We were paying for Crayon but using a Slack channel for competitive intel. We had a content management system but still stored everything in Google Drive. We subscribed to a customer research platform but conducted interviews through Zoom and tracked insights in Notion.
The pattern was obvious: we bought tools that solved problems in theory but didn't fit our actual workflows in practice.
I interviewed the team to understand why. The answers were brutally consistent:
"The tool requires too many steps to do something I can do faster another way."
"I can't remember to check another platform when I'm already in Slack and email all day."
"It doesn't integrate with the tools I actually use, so I have to copy data between systems."
"The setup was complicated and nobody trained me, so I just went back to what I knew."
We were buying tools based on feature checklists, not adoption likelihood. Every tool we evaluated had impressive capabilities. None of them were evaluated on whether our team would actually use those capabilities.
The RFP That Actually Worked
A year after the competitive intel purchase, we needed a new sales enablement platform. Our current system was a SharePoint site that Sales hated and nobody updated. We needed something better, but I was terrified of repeating the same mistake.
This time, I built an RFP process that started with a different question: not "What features do we need?" but "What behavior do we need to change?"
The behavior problem was clear: sales reps couldn't find the content they needed when they needed it. They'd search SharePoint, find nothing, ask in Slack, wait for responses, and eventually send prospects outdated materials or make something up.
I observed five reps during actual deal cycles to understand the real workflow. When they needed a case study, they'd:
- Check their email for recent sends from Marketing
- Ask in the sales Slack channel
- Google "[our company] case study [industry]"
- Give up and send a generic deck
The new tool needed to fit into this actual workflow, not require reps to learn a new process.
I wrote an RFP with three sections:
Section 1: Must-Have Capabilities - The bare minimum features without which the tool couldn't solve the core problem. For enablement, this was: searchable content library, integration with Salesforce, metrics on what content is used in deals, mobile access.
Only four requirements. If a tool didn't have all four, it was eliminated immediately. This kept me from getting distracted by shiny features that didn't matter.
Section 2: Workflow Fit - How the tool integrated into our existing systems and processes. Would reps access it through Salesforce, Slack, or a separate platform? How many clicks to find a case study? Could it surface recommended content based on deal attributes?
This is where most tools failed. They had great features but required reps to go to Yet Another Platform and search Yet Another Content Library. Adoption would be terrible.
Section 3: Adoption Enablers - What made the tool easy to implement and easy to maintain? Integration capabilities, admin burden, training requirements, support quality, user permissions.
I'd learned from the competitive intel failure that sophisticated tools with complex admin requirements never get maintained. If I needed to spend 5 hours a week managing the platform, it wouldn't survive my first vacation.
The Questions That Exposed Bad Fits
The RFP document was table stakes. The real evaluation happened in vendor demos, where I asked questions designed to reveal how the tool actually worked versus how the marketing site claimed it worked.
"Show me how a sales rep finds a case study at 10pm on a Sunday while working from their phone."
This question revealed mobile experiences, search quality, and whether the tool was designed for how Sales actually works. Half the vendors couldn't demo this scenario because their mobile app was an afterthought.
One vendor's answer: "They'd log into the platform, navigate to Resources, filter by content type and industry, then download the PDF."
Another vendor's answer: "They'd open Salesforce on their phone, and our app would automatically recommend relevant case studies based on the opportunity details. One tap to attach it to the email."
Same feature—content library. Completely different user experience. The second vendor won.
"Walk me through what happens when someone uploads a new battle card. How does Sales find out about it?"
This exposed whether the tool had proactive distribution or required people to check for updates. The worst tools required Sales to periodically browse the library hoping to find new content. The best tools pushed notifications through Slack or email when new content matched their territory or product focus.
"Show me the analytics dashboard you'd give me to prove this tool is working."
This revealed whether the vendor understood PMM success metrics. Bad vendors showed page views and download counts. Good vendors showed content-to-close rates and which assets appeared in winning deals versus losing deals.
One vendor's dashboard showed "Top 10 Most Downloaded Assets." Useless—I needed to know what content won deals, not what content was popular.
Another vendor showed "Content Performance by Deal Stage" and "Win Rate by Content Usage." That's actionable data I could use to improve our enablement strategy.
"What happens if our team decides we hate this tool after three months? How hard is it to get our data out?"
This question made vendors uncomfortable, which was the point. I wanted to know if they locked us in or made migration easy. The honest vendors explained their export capabilities and limitations. The dishonest ones dodged the question.
I'd learned from the competitive intel purchase that if you can't easily leave a tool, you're stuck with it even when it's not working. Export capabilities were now a must-have.
The Mistake I Almost Made Anyway
Even with a better RFP process, I almost repeated the same mistake.
There was a vendor whose tool had incredible AI capabilities. Automated content recommendations. Natural language search. Predictive analytics about what content would work for each deal. The demo was stunning.
I was ready to buy it. Then I ran a pilot.
We gave fifteen sales reps access for two weeks. I watched actual usage instead of asking for feedback. Here's what happened:
The AI recommendations were often wrong because our Salesforce data was messy. Opportunities were miscategorized, deal stages were out of date, and competitor fields were rarely filled in. Garbage in, garbage out.
The natural language search was impressive but didn't understand our internal terminology. Reps searched for "competitor X comparison" and got irrelevant results because we called it "competitive positioning" in our content titles.
The predictive analytics required three months of historical data we didn't have in clean form. The insights would have been valuable eventually, but not for months.
The tool was sophisticated, but our operations weren't mature enough to support it. We needed something simpler that worked with our current state, not our aspirational future state.
I killed the deal and bought a simpler tool with worse AI but better manual search and basic Salesforce integration. Adoption in the first month was 89%. The fancy AI tool would have sat unused while we spent six months cleaning up our data.
What Actually Predicts Adoption
After evaluating tools for three years, I've learned that adoption comes down to three factors that have nothing to do with features.
Fits existing workflow. The best tools slot into how people already work. The worst tools require people to develop new habits.
If your sales team lives in Salesforce, your enablement tool needs to work inside Salesforce. If your marketing team lives in Slack, your content calendar needs Slack integration. If your PMM team already uses Google Docs, your tool needs to complement Docs, not replace them.
I stopped evaluating tools in isolation and started evaluating them in context. Not "Is this a good tool?" but "Will my team actually use this tool given how they currently work?"
Solves immediate pain. Tools that solve tomorrow's problems don't get adopted. Tools that fix today's headaches get used immediately.
We evaluated a roadmap planning tool that would have been perfect for strategic planning. But our immediate pain was launch coordination—making sure all the content shipped on time. We bought a project management tool instead, then added strategic planning capabilities later.
If you can't point to a specific painful task that becomes easier on day one, adoption will be a struggle.
Reduces complexity. The best tools eliminate steps from existing workflows. The worst tools add steps.
I now count clicks during demos. If it takes more clicks to do something in the new tool than in our current process, it won't get adopted no matter how much better the output is.
We almost bought a customer research tool that had incredible analysis features but required 12 clicks to schedule an interview versus 3 clicks in Calendly. The analysis would have been better, but nobody would have scheduled enough interviews to benefit from it.
The ROI Calculation That Actually Matters
Vendors always show ROI calculations in their sales decks. They're always garbage.
"Based on industry benchmarks, our tool will improve your win rate by 15%, generating $2M in additional revenue for an investment of only $50K annually!"
These calculations assume perfect adoption and optimal usage. Reality is messier.
I started calculating ROI based on realistic adoption scenarios:
Pessimistic case (30% adoption): If only the power users adopt the tool, what value do we get?
Realistic case (60% adoption): If about half the team uses it regularly and the rest use it occasionally, what's the value?
Optimistic case (90% adoption): If adoption is great and usage is consistent, what's the value?
For most tools, even the optimistic case barely justified the cost. For the tools we actually bought, the pessimistic case was still clearly positive.
The competitive intel platform that failed this test. In the optimistic case, it would save maybe 3 hours per week across the team. Value: ~$10K annually. Cost: $40K annually. The math never worked, even at 90% adoption.
The enablement platform we bought instead passed this test easily. In the pessimistic case—30% of reps finding content 20% faster—it saved roughly 40 hours per month of rep time. At $200/hour of sales rep fully-loaded cost, that's $96K in value annually for a $35K tool. The math worked even if adoption was mediocre.
The Questions I Ask Before Every Purchase Now
I've developed a checklist I run through before signing any contract over $5K:
Have I observed the team doing the task this tool claims to improve? If I haven't watched someone struggle with the current process, I don't understand the problem well enough to evaluate solutions.
Did the people who will use this tool help evaluate it? If I'm buying a tool for Sales and Sales wasn't involved in the decision, adoption will fail.
Can we pilot this for 30 days with a representative group before committing? If the vendor won't let us trial it with real usage, they don't have confidence in their product.
Does this tool reduce the number of platforms the team uses, or increase it? Every additional platform reduces the chance of adoption. If this tool doesn't replace something, the bar for value is much higher.
What's our plan to drive adoption in the first 30 days? If the plan is "buy it and they'll use it," we're going to waste money.
What happens in month 13 when the renewal comes up? If I can't articulate how we'll measure success and what would cause us to cancel, we're not ready to buy.
These questions have killed more tool purchases than they've approved. That's the point. Every tool we didn't buy is budget we saved for tools that actually matter.
What I'd Tell My Past Self
If I could go back to the competitive intel purchase, I'd start with a different question. Not "What's the best competitive intelligence tool?" but "What's the smallest intervention that would make our competitive intel process 20% better?"
The answer probably would have been: hire a contractor to update our battle cards monthly and create a Slack bot that surfaces competitive news. Cost: $15K/year instead of $40K, and it would have fit our workflow instead of requiring us to change it.
I've learned that the best tech stack isn't the one with the most sophisticated tools. It's the one where every tool gets used consistently to solve a real problem.
That usually means fewer tools, simpler tools, and tools that integrate with systems people already use. It means being willing to walk away from impressive demos if the tool doesn't fit the team's actual behavior.
Most importantly, it means accepting that the person buying the tool usually isn't the person using the tool. If you're not willing to involve the actual users in the evaluation, you're gambling with budget you probably can't afford to waste.
I've bought a dozen tools since the competitive intel disaster. Half of them got adopted and delivered value. Half got replaced within a year. The difference was never the tool's capabilities—it was whether the tool fit how the team actually worked.
Now I optimize for adoption, not features. It's a boring strategy, but it's the one that keeps my tech stack budget from getting cut.