Startup CRO tells us where revenue leaders are going wrong with AI — and how to fix it
Discover how a SaaS CRO uses AI to redesign revenue workflows, improve forecasting and account research, and help teams move faster without sacrificing judgment or quality.
Career Path: Bill Dwoinen emphasizes that a sales career path is often non-linear, involving consistent small choices.
AI Integration: Integrating AI in workflows can enhance sales precision and consistency, not replace human judgment.
Renewal Strategy: AI can transform renewal strategies into proactive workflows, flagging risks before critical deadlines.
Data Importance: Successful AI adoption hinges on having a solid data foundation and consistent, trustworthy information.
Workflow Redesign: The true potential of AI lies in rethinking and redesigning high-value workflows, beyond fixing friction points.
Bill Dwoinen is the CRO of the growth-stage B2B SaaS company, Mural. He's worked in revenue for 20 years, and now oversees Sales, Customer Success, Revenue Operations, and GTM systems — and he's all-in on AI.
We spoke with Bill about why revenue leaders must stop fixing friction points with AI and start redesigning entire workflows. Here's what he told us.
Never A Straight Line To The C-suite
I'm Bill Dwoinen, CRO at Mural. I've spent about 20 years in sales and sales leadership. I started in the human capital space at CareerBuilder, then transitioned to SaaS at LinkedIn, and then spent nearly five years leading Slack's enterprise business at Salesforce in the strategic tech vertical. I stepped into the CRO seat at Mural in early 2025.
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My journey was never a straight line. I tell high-school kids this when I go back and speak, and I mean it. Life doesn't move in a straight line, and neither does a sales career. I came up as an individual contributor, got into leadership because I genuinely cared about the people around me more than the credit, and eventually built the track record that opens doors to the C-suite. Small, consistent choices over 20 years — that's what it was.
Leading The Revenue Organization
I lead the full revenue organization at Mural, a growth-stage B2B SaaS company with a recurring subscription model and an expanding services layer through our LUMA partnership. The organization spans sales, customer success, revenue operations, and GTM systems. I intentionally brought GTM Systems into the revenue org to align our systems, data, and workflows with how customers buy, adopt, and grow with us, rather than optimizing them in silos.
We serve enterprise customers globally, with the strongest concentration in AMER and EMEA. Our go-to-market motion focuses on both new logo acquisition and expansion within our existing customer base, and we actively expand into new use cases and buyer personas beyond Mural’s traditional footprint.
What matters most to me is that the revenue organization operates as one connected engine. New business, retention, and expansion must not operate as separate agendas. My job is to ensure the operating model reflects that and that every part of the organization, from the first sales conversation through long-term customer value, pulls in the same direction.
Why AI Is A Gift To CROs
I've always believed in process: forecast accuracy, pipeline discipline, understanding your leading indicators. AI is now turbocharging everything I've built my leadership philosophy around.
Teams that used to manually review pipeline and make gut calls can now get signal faster, act with more confidence, and coach more precisely. For a CRO, that's not a threat; that's a gift.
Leaders who will win in this moment aren't afraid of it. I've never been afraid to fail, and I'm not afraid of this. At Mural, I'm focused on building a revenue engine where AI amplifies what great people already do, not replaces judgment, relationships, or the culture we're building. That's the work now, and I couldn't be more locked in on it.
How AI Can Redesign High-value Revenue Work
I used AI to redesign how high-value revenue work gets done, specifically for account research, POV creation, and communication.
This work sits closer to revenue than people sometimes realize. It shapes how well the team shows up to customers, how relevant the outreach is, and how consistently the organization frames value. Historically, it has also been too manual, too uneven, and too dependent on individual effort. The best reps do it well. Others do something more generic. This creates a quality problem, not just a productivity problem.
My new workflows are custom GPTs built for specific commercial use cases. These are not generic AI applied broadly, but tools designed around how we think about accounts, how we frame value, and what strong commercial output looks like in our context.
Specificity creates leverage. Generic tools produce generic output.
The clearest example is our account research and outreach preparation workflow. A rep inputs the account, the relevant context, and the objective. The workflow produces a structured account view: what is likely happening in the business, where the priorities and pain points are, what signals matter, and how to frame our value against that specific situation.
AI In Account Research And Outreach Prep
The rep walks away with a real point of view built around the customer, not a recycled template. Work that used to take 10 to 30 minutes now takes closer to three without reducing quality. And quality is more consistent across the team, regardless of who runs it.
Work that used to take 10 to 30 minutes now takes closer to three without reducing quality. And quality is more consistent across the team, regardless of who runs it.
AI In Forecasting Workflows
Another example is the Forecast Center, a custom GPT I built to change how leaders prepare for and run forecast conversations. It solves the problem that most forecast calls turn into leaders reading data back to the room instead of driving strategy from it. Forecast Center takes run-the-business data, pipeline inputs, and customer health information and helps leaders turn that into a clear narrative and a defensible point of view before they ever get on the call.
Our internal standard is simple: If it sounds like a GPT summarized a spreadsheet, it is not done. It should sound like a CRO who has done the homework and is ready to drive the business.
Results
What makes both work is not the model. It is the workflow design, the built-in context, and the bar we hold for what good output looks like. The rep and the leader still own the judgment. AI removes the friction from the preparation so they can show up sharper, faster, and more consistently than before.
We are still early, so I caution against overclaiming the downstream revenue impact. But across these workflows and more, we have already improved speed and responsiveness across the organization without sacrificing quality. Teams can get closer to customers faster because they no longer wait for a meeting or a long manual process to research an account, build context, and form a point of view. This has improved preparation, raised the consistency of customer-facing work, and made the revenue organization more effective at engaging accounts.
Why Renewals Need An AI Redesign
Renewals are also in need of a redesign.
Most revenue organizations still treat renewals reactively. Teams get close to the date, check the health score, and scramble to cover the right relationships.
But by the time teams do that work, they are already behind. I would push for redesigning renewal intelligence as a continuous workflow, not a quarterly fire drill. AI is well suited for that because it can surface risk signals, flag coverage gaps, and help leaders form a point of view on where to act before the window closes.
Why The Biggest Opportunity With AI Isn't What You'd Expect
But remember, the biggest opportunity with AI is not fixing individual friction points. It is rethinking the workflow itself.
Early on, I was probably too focused on solving immediate pain, which created value but was more incremental than transformational in some cases.
Had I understood that earlier, I would have spent less time optimizing isolated tasks and more time redesigning the highest-value workflows end to end, linking clearly to GTM priorities and measurable business impact from the start.
That would have helped me avoid fragmented experimentation. This can lead to useful tools that do not add up to real strategic leverage.
The lesson is that AI creates the most value when it connects to how the business generates revenue — not just where people feel pain in the moment.
Where AI Informs Revenue Tasks
I rely on AI most in areas where better synthesis and faster preparation create real leverage, such as account research, POV creation, messaging, communication, and pattern recognition across large amounts of information. It excels at processing inputs, structuring thinking, and improving the quality and consistency of execution across the organization.
Humans come in where real judgment and accountability are required: forecasting, pricing, final pipeline calls, territory decisions, and major decisions regarding customer risk or investment. AI can inform those conversations, but it does not replace leadership judgment.
The reason is straightforward. I want AI helping us think better and move faster, not owning decisions where nuance, context, and accountability matter most. In a revenue organization, speed matters, but judgment is still the differentiator.
Revenue Leaders Must Keep A Close Eye On AI Slop
Sometimes, AI creates more output instead of better output. The clearest example is AI slop: generic outreach, generic messaging, generic points of view, and much activity that looks productive but does not improve commercial quality. In those cases, AI creates noise, not leverage.
I have also seen AI overapplied in areas where judgment, context, and nuance matter more than speed. If the inputs are weak or the standards are low, AI does not fix that. It amplifies it.
The issue is less the technology itself and more how easily people can confuse volume with value. That is the failure mode I watch most closely because it leads to trust erosion.
I address this by setting a clear standard across the organization, leading by example, and scaling the discipline I used to build my own workflows. This means reinforcing that AI should sharpen thinking, not replace it, and that context, iteration, and judgment still matter.
This is what separates revenue teams that gain real leverage from AI from those that just create more noise.
Why Scale Doesn't Always Have A Quality Tradeoff
I thought scale always came with a quality tradeoff. In go-to-market, automation has historically meant more output, but usually at the expense of relevance, judgment, and quality.
AI changed my assumption that this tradeoff is fixed. If used well, with the right context, workflow design, and standards, AI can increase speed and scale without automatically lowering the bar.
The best example is the account research and preparation workflows I shared earlier. That does not happen by default, but it is possible. And it marks a meaningful shift from how most of us have experienced automation in revenue organizations.
If used well, with the right context, workflow design, and standards, AI can increase speed and scale without automatically lowering the bar.
Why Good Data Is Key For Successful AI Adoption
Good data matters more than tooling. If the data is incomplete, inconsistent, or hard to trust, AI does not solve that problem. It amplifies it.
You may get faster output, but not better output. That has been one of the biggest lessons for me.
Successful AI adoption is not just about access to the right model. It is about having the data foundation, operating discipline, and confidence in the inputs to use the model effectively. The tools matter, but good data is what makes any of it real.
How CROs Should Approach AI-driven Changes
I advise CROs to use AI to sharpen thinking, improve execution, and scale judgment, not to flood the business with more output.
Start in the areas where speed and synthesis create real leverage: account research, POV creation, messaging, and pattern recognition across the revenue engine. Be clear about what AI should inform and what should remain human. And set a high bar early; otherwise, people will default to generic output that looks productive but does not improve commercial quality.
The leaders who will benefit most are not the ones who claim to have it figured out. They are the ones who engage with it seriously, apply it thoughtfully, and keep raising the bar on what it does for the business.
Follow along
You can follow Bill Dwoinen's work on LinkedIn.
More expert interviews to come on The CRO Club!
