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AI adoption has moved faster than almost any other technology in modern business. While it took decades of development, AI has become part of everyday work in a matter of years.
In go-to-market (GTM) strategy – the systems and processes companies use to bring products to market and drive revenue – AI is quickly becoming a core layer. It’s helping teams connect data across functions, automate execution, and surface insights that shape how they plan, sell, and forecast.
Recently, the Pigment team spoke with Maggie Hott (GTM Leader at OpenAI), and Wade Jastremski (Senior Manager of Revenue Management & Forecasting at Spotify) to discuss how AI is changing the mechanics of forecasting, planning cycles, and GTM strategy. The conversation explored how modern teams are using AI to accelerate go-to-market processes and rethink the way decisions are made.
Watch the full webinar here, or read on for the key takeaways.
1. AI has become available to anyone with an internet connection
The rapid rise of AI over the past few years may feel sudden. But, in reality, the technology became widely accessible with the launch of ChatGPT.
“Three years ago, AI was really trapped with researchers and developers – people with PhDs who knew how to use machine learning models,” explained Hott. “For the average analyst or salesperson, AI was kind of just a mythical thing.”
Essentially, AI technology existed, but it wasn’t usable for most business cases. Planning teams, sales leaders, and analysts still relied on spreadsheets, dashboards, and manual workflows.
That gap disappeared once AI tools became usable through simple interfaces. Today, anyone with an internet connection can ask an AI model to summarize research, analyze data, or generate insights. What was once specialized technology has quickly become part of the everyday workflow for professionals across planning, sales, and operations.
2. The first gains come from the bottom of the task list
When Jastremski's team at Spotify first started embedding AI into their workflows, the wins came from the bottom up. Weekly business reviews that once took hours to compile – pulling data from 400 salespeople across global regions, building out visualizations, and distributing forecasts – are now assembled and sent automatically. Call recordings are transcribed, summarized, and converted into action items without anyone lifting a finger.
"We started with administrative tasks. but now we've moved into leveraging AI to provide automated insights, from preparation of the data to actually using it to mitigate risk or accelerate opportunity."
At OpenAI, Hott’s team built a GPT that pulls SEC filings for any publicly traded company and synthesizes them into a full executive briefing, including strategic priorities, competitive landscape, executive compensation structure, and recommended questions for the sales call. What used to take one to two weeks of full-time research now takes less than 10 minutes.
These are the kinds of gains that compound. When the run-of-business runs itself, teams have room to go deeper.
3. Forecasting improves when AI helps teams weigh what matters
Forecasting in volatile markets has always required balancing multiple signals.
In Spotify’s advertising business, those signals include historical trends, pipeline data, delivery pacing, and input from frontline sales teams. Each indicator carries a different weight depending on where the team is in the quarter.
AI is now helping operators adjust those signals dynamically. Jastremski explained that his team uses AI to determine when different pacing indicators should matter more within their forecasting models.
“What we’re now leveraging AI for is to help us know when to weight different pacing indicators at different points in the quarter.”
Earlier in the quarter, historical trends may provide the strongest signal. As deals progress through the pipeline, pipeline indicators become more predictive. Closer to the end of the quarter, delivery pacing data can offer the clearest view of where revenue will land.
Rather than relying on static models, AI allows forecasting systems to continuously adjust those inputs so forecasts evolve alongside the business.
4. AI is collapsing planning cycles
Strategic planning has traditionally been slow work. Building territory strategies, researching markets, and developing vertical go-to-market plans has historically required weeks of research and coordination across multiple teams.
AI is compressing that process at an incredible rate.
Hott shared an example from building OpenAI’s healthcare and life sciences strategy. Gathering the data needed to size markets, segment companies, and identify global opportunities would typically take months of analyst work. But with AI-powered research, the timeline shrank dramatically.
“That same process probably would have taken one to two months, and we were able to bring it down to quite literally one night.”
That doesn’t mean that strategic thinking has gone out the door. AI removes the time teams once spent collecting and stitching together the raw inputs required to start.
5. The best salespeople are becoming business coaches
As AI handles more of the research, documentation, and preparation that used to fill a seller’s day, the job itself is changing.
Hott estimates that salespeople have historically spent 30% of their time actually selling. The rest has gone into prep, including building decks, researching accounts, writing follow-ups, and updating forecasts. With that administrative burden lifting, what buyers expect from a sales conversation has shifted accordingly.
"It's not enough to be consultative about your product anymore," Hott said. "You need to be consultative about the customer's entire business." She described spending four hours in an executive briefing with a large pharmaceutical company, fielding questions about how AI is changing education and workforce development. This is territory that would have been unimaginable in a traditional sales call. "We almost need to be business coaches now," she concluded.
Hott’s bar for what makes for strong salespeople has shifted, too. The candidates who stand out in job interviews are the ones who can make a buyer feel like they learned something.
"People will always say yes to a call if they feel like they're going to come away smarter."
The tools now exist to help every salesperson reach that standard. Crafting a follow-up email that leads with a relevant industry trend positions the sender as a thought partner and earns a response. What used to be a skills gap is now a prompt away.
Conclusion
AI is expanding who can forecast, plan, and analyze. It’s also changing the depth and speed at which it gets done.
Forecasting models that required teams of analysts now run continuously and adjust in real time. Market strategies that demanded months of research get built in an evening. Salespeople who once struggled to personalize at scale now show up to executive meetings with full command of their buyer's entire business. AI tools have moved from specialist infrastructure to everyday capability.
Pigment gives go-to-market and finance teams the platform to take that next step. It creates a live, connected environment where AI-powered insights inform planning, forecasting, and decision-making in real time, so teams can move faster, go deeper, and build with more confidence.
Watch the full conversation
Review our in-depth discussion on how AI is reshaping forecasting, planning, and go-to-market – and learn what that means for the teams using it.
