Jason Anderson, Expedience Software
Introduction
Good morning and good afternoon, everyone.
I’m Jason Anderson with Expedient Software, and I’m excited to share today’s presentation on proposal technology investments—specifically how to avoid the common pitfalls that prevent these investments from delivering success.
This is the same presentation I recently delivered at the APMP Bid & Proposal Conference. I’ll not only walk you through the material, but also share insights from conversations I had with attendees.
The Current Landscape
At this year’s conference, one thing was very clear:
there is more focus on proposal technology than ever before.
Organizations are actively looking for ways to improve efficiency, and many now have mandates to explore technology solutions. This is a big shift—proposal teams have historically been underserved when it comes to tech investment.
A major driver of this trend is the rise of Generative AI. Tools like ChatGPT, Copilot, Claude, and Gemini are creating huge excitement.
But with that excitement comes confusion:
- Which tools should we use?
- How do they fit together?
- What actually works?
Meanwhile, vendors are largely focused on “why their product,” but they’re not addressing a critical issue:
👉 Most technology investments fail to meet expectations.
- Studies show 70–84% of digital transformation initiatives fail
- Even recent GenAI investments often fail to deliver measurable ROI
Why?
Because technology itself is not the solution—how it’s implemented is what matters.
The Top 10 Reasons Proposal Technology Fails
Let’s walk through the most common reasons proposal software investments fall short.
10. Boiling the Ocean
Trying to solve every problem at once.
When organizations finally adopt technology, they often expect it to fix everything. Without prioritization, this leads to complexity—and ultimately failure.
9. Failing Our Friends
Ignoring the broader team.
Proposal teams rely on subject matter experts, legal, reviewers, and leadership. If the solution creates friction for them, adoption will suffer.
👉 Tools must make collaboration easier, not harder.
8. Solving the Wrong Problem
Starting with the solution instead of the problem.
Too often, companies say, “We need to use AI,” and then try to find a use case.
Instead, focus on the highest-impact bottlenecks—the 20% of issues causing 80% of delays.
7. Hype vs. Reality
Buying based on aspiration instead of behavior.
Organizations often choose tools based on how they think they should work—not how work actually gets done.
👉 Be honest about your current workflows.
6. Leadership-Forced Solutions
Top-down decisions without user input.
When leadership mandates a tool without involving users, it rarely aligns with real-world needs.
5. Lack of Leadership
No ongoing support or guidance.
Even with the right tool, adoption falters if leadership isn’t visibly engaged throughout the rollout.
4. Verification Tax
Reviewing AI-generated content.
AI can produce content instantly—but verifying its accuracy takes time. This effort is often underestimated.
3. Formatting Tax
Fixing presentation and branding.
AI-generated output typically lacks proper formatting, branding, and structure. Teams spend significant time correcting this.
2. The Silver Bullet Myth
Believing software will fix everything.
Technology doesn’t replace people, processes, or strategy—it supports them.
1. Change Fatigue
Too much change at once.
The more behavioral change required, the lower the adoption rate. Overwhelming users guarantees failure.
Key Insight: AI Comes with Hidden Costs
While AI is powerful, it introduces two major challenges:
- Verification tax – ensuring accuracy
- Formatting tax – aligning with brand and standards
In some cases, these can outweigh the time saved by automation.
A Better Way: The Success Formula
You can think of success like this:
Success = (Value × Adoption) ÷ (Friction + Cost)
To improve outcomes:
- Focus on high-value problems
- Drive strong user adoption
- Reduce friction and complexity
Proposal Automation vs. AI
It’s important to understand the distinction:
Proposal Automation
- Reuses vetted, approved content
- Ensures consistency and accuracy
- Template-driven
AI Tools
- Generate new content
- Require validation
- Flexible but less predictable
👉 The best approach is to combine both:
- Automation for consistency
- AI for iteration and enhancement
How Expedient Approaches This
At Expedient, we’ve focused on solving the core adoption challenges.
1. Zero Friction
We work directly within Microsoft Word—no new environment to learn.
2. Content Integrity
We use verified, pre-approved content libraries, eliminating accuracy concerns.
2. Built-in Formatting
Content is automatically branded and formatted correctly.
4. Flexible AI Integration
Customers can use Copilot if they choose—but it’s not required.
What This Looks Like in Practice
Inside Word, users can:
- Access a structured content library
- Insert approved content quickly
- Generate first drafts in minutes
- Maintain formatting automatically
- Personalize documents consistently
And if AI is used, it integrates seamlessly alongside this framework.
Final Thoughts
The biggest takeaway is this:
👉 Success isn’t about choosing the right technology—it’s about implementing it the right way.
To maximize your investment:
- Focus on your biggest challenges first
- Prioritize adoption
- Minimize change and friction
- Combine automation with AI strategically
Closing
Thank you for joining today.
If you have questions or would like to continue the discussion, feel free to reach out or visit our website at ExpedientSoftware.com.
We’d be happy to connect and help you explore what success could look like in your organization.
Transform Business Proposals
More than speed, winning proposals demand accuracy and control. Expedience delivers all three directly within Microsoft Word.
Book a demo to see how!