Cliff Simon:
Well, thank you all for coming today. There is a ton of confusion when you’re out in the market when it comes to AI. We all have this incredible mandate to go out and get it, enable our teams to use it, embed it into all of our go-to-market processes, and embed it into our technology. But there’s just so much of it, and you can’t actually sift through all of it yourselves.

We’ve got some great guys on the panel who are going to help us dive into the things they’re doing with their customers and with their teams to be successful with it because it is so noisy. We want to kick off with the guys here right now. Obviously, there’s all this stuff happening when it comes to AI adoption and the way we’re developing technology. If you go down the line, what’s the big picture of what you guys are seeing right now? Biggest pains, I think?

Randy Likas:
The biggest friction that we’re seeing comes from three market dynamics, which all relate to the same friction. This is both anecdotal from different leaders we’ve spoken with, as well as from some primary research we’ve done. Deals are taking much longer than they ever were before—deal elongation.

The second is that deals we thought were locks and were calling for the quarter are slipping. And the third is that the ability to attach additional products we’ve built isn’t happening at the rate that was projected. All three dynamics relate to the same friction: the stakeholders involved in deals are growing, often with competing priorities. Understanding the dynamics of who all the different influencers are coming in and out of a deal—not just the people tagged as the first contact because of a validation rule—is key.

Understanding those dynamics of the buying group and the changing buyer journey is the friction I see right now. Instead of just trying to optimize what we’re already doing, I think it’s time we think about how to do something different and how to do it better.

Cliff Simon:
Appreciate that. Charlie?

Charlie Wiebe:
First of all, great to be here. Great to see you all today. When I think about friction points, it really starts way up the funnel with, for lack of a better term, list building.

At Nooks, the first thing we did was try to automate cold calling and meaningful connections at scale. But that doesn’t actually solve the problem of, “Who am I connecting with, and why does it matter?” The first round of AI evolution in the last couple of years was about polish and summarization, but it lacked relevance—why this account, why this person, why now?

We built a tool for ourselves called AI Prospector, though there are plenty of tools out there that do this. The goal is to get sellers actually selling rather than spending two hours fumbling around a CRM. It takes three things:

1. A tool that does auto-prioritization based on not only third-party signals—like fundraising or hiring—but also first-party signals, like past engagement or competitor renewals.
2. Verified contact info for the right people.
3. Tools that help sellers engage in meaningful and relevant ways.

Regardless of what tool you’re using, the goal is relevance, not polish—to get high-cost sellers doing what we want them to do, which is selling, not searching.

Prakash Raina:
I’ll take it to the next step from what Charlie and Randy mentioned. Every company is now trying to add a new product or feature around AI, and that requires new pricing and packaging. Every week or month, there’s a new competitor, and buyers are asking what AI features you have and how you’re using them.

That requires figuring out how to package and price your product competitively, enabling your sellers to sell it. Traditional tools and processes need a new perspective. At Subskribe, we’ve built CPQ software that makes it easy to launch new products and pricing models—whether consumption-based or subscription-based—so teams can move faster.

Software isn’t being sold the same way it was even two years ago. Everybody’s looking for AI packaging, so companies need tools that help accommodate AI-driven demands in the market.

Cliff Simon:
With all this AI, there’s increased friction in the buying process. Charlie, I know you’ve done a lot at Nooks to help remove friction from client interactions. Can you walk us through that a bit?

Charlie Wiebe:
Sure. I’ll start with a metaphor related to my fun fact. My first job out of school was building houses up in Marin before I knew what a 401(k) was. Builders work with general contractors, and their main job isn’t to build the house but to hire and coordinate subcontractors—stonemasons, roofers, electricians, plumbers—and to hire them in the right order.

Your AE or SDR is the general contractor. Whatever suite of AI tools they have are their subcontractors. They’re the human-in-the-loop orchestrator guiding the most efficient selling path.

At Nooks, we use a lot of AI and automation, but our primary goal is to keep the SDR or AE in the driver’s seat. We coach them to ask strategic questions AI can’t—things about a competitor, a Slack thread, or usage data.

AI is great at summarizing, but not always at prioritizing. The goal is to support strategic decision-making, not dictate it.

Cliff Simon:
Awesome. Going from the top of the funnel down to mid and late stage—many deals are slipping. Over 83% are slipping a stage or two. How are you dealing with that on the deal room side?

Prakash Raina:
Traditionally, in every software business, there’s a deal desk function that acts as a checkpoint—reviewing, approving, chasing people, and giving a green light. That process doesn’t work in today’s AI world.

At Subskribe, we’re making the deal desk more of a guidance engine than a checkpoint. It’s not just about moving deals faster but smarter. Our CPQ looks at past data and suggests better deal structures so sellers can create win-win proposals quickly.

We’re also using AI to reduce manual approvals, eliminate bottlenecks, and help finance teams close deals faster—especially critical since 70% of deals close in the last 15 days of a quarter. The deal desk should enable, not block.

Cliff Simon:
One thing we see with many clients is bloated tech stacks. How do you drive efficiency without losing capability or overspending?

Randy Likas:
We’ve all made huge investments in sales and marketing tech, and they’re great at what they do. The challenge is partial data write-backs to CRM. Tools want users in their own UI, but that silos data.

For example, an opportunity record might show one contact, but we know six people should be engaged, and when they are, win rates rise by 15%. Marketing can’t help if they don’t see that data.

We need to liberate data and ensure full write-back to the opportunity object so all teams can act on it. That’s what we’ve built—AI that integrates data across platforms so everyone has access and deals move forward faster.

Charlie Wiebe:
If I boil it down, the point of a GTM tech stack is to turn insight into action. It’s not about dashboards or alerts—it’s about action.

You need three core things:

1. A single, clean CRM as your source of truth.
2. An automation layer that handles repeatable processes.
3. A central engagement hub where reps live—sequencer, dialer, call notes, history—all in one UI.

AI is woven throughout, helping summarize and prioritize. I don’t want SDRs spending time figuring out why to reach out to someone—hit them with the reason and let them sell.

Too many teams build “museum” tech stacks—beautiful dashboards, zero throughput. Focus on what drives reps to act and generate revenue.

Prakash Raina:
I agree. Before starting Subskribe, I was at Okta, and one thing I learned was to study every new tool before requesting budget. Why do you need it? Can something you already have handle 70% of it?

Companies often buy tools first and figure out use cases later. Instead, define the top five to ten things you want to solve. If a tool doesn’t meet those, you don’t need it.

This approach accelerates purchasing and ensures you only add tech that drives revenue or reduces costs—like shortening quote time from hours to minutes.

Cliff Simon:
When building for clients, we think in terms of systems: record, activation, and information, all tied together around the jobs to be done. Then we connect it all with an MCP layer, integrating across cloud platforms like Perplexity, ChatGPT, or OpenAI.

Prakash Raina:
Exactly. You can automate so much instead of filling gaps with new tools. Use what you have intelligently.

Cliff Simon:
Within that framework, it’s about knowing the actual jobs to be done so you don’t end up using 15% of one tool and 20% of another—and spending millions.

Randy, you’ve done a great job driving data visibility. How has AI improved pipeline visibility and forecasting accuracy?

Randy Likas:
The number one thing is using the opportunity object better and letting AI make it smarter. Reps are often too busy to add all influencers to opportunities.

We built automation that associates everyone sales is talking to with the opportunity record. When we audit Salesforce orgs, we see 75% of opportunities with one contact, 24% with none, and only a few with two or more—even though six to twelve people typically influence a deal.

AI on the back end can fix this—automating association, unifying data, and improving visibility. One woman at Salesforce told me what we do is “magical” because it gives marketing analytics teams the missing data they need for attribution.

AI doesn’t just belong in writing emails; it should strengthen the foundation. That’s why we built a headless platform focused on clean, unified data so GTM teams can take action.

Cliff Simon:
Fantastic. Rapid fire: imagine a GTM team wants to cut their sales cycle by 30%. From your vantage point, what’s the one workflow or tool you’d prioritize first?

Charlie Wiebe:
Inbound enrichment and lead routing. Speed to lead is the number one pipeline killer. You can have an all-star SDR, but if they reach out 20 minutes too late, it doesn’t matter.

If someone fills out a pricing form, within 15 seconds, I want them enriched—role, company, intent signals, ICP fit—and an alert flashing to the SDR to call immediately. If no answer, trigger an email or a website modal to book time right away.

Prakash Raina:
That’s great for new logos, but companies also lose revenue at renewal or expansion. Having systems that identify heavy usage or upcoming renewals lets you act quickly—generate expansion quotes or renewal proposals instantly. That’s how you accelerate deal cycles.

Randy Likas:
I’d say create a “stage zero” opportunity container early, where you pipe in all signals and intent before qualification. Include everyone who’s influenced past deals, lost or won.

Give SDRs a few key contacts to reach out to and swarm accounts. When teams do this—like at Palo Alto Networks—they see faster deal velocity, bigger deal sizes, and higher win rates. The hardest part is convincing Sales Ops to let you touch opportunities, but once you prove it drives results, they’re on board.

Cliff Simon:
Thanks, guys. We’re at time.