This editorial appeared in the May 29th, 2025, issue of the Topline newsletter.
Want all of the latest go-to-market insights without the wait? Subscribe to Topline and get the full newsletter delivered to your inbox every Thursday.
When Salesforce reimagined the structure of their Go-to-Market (GTM) team in the early 2000s — a shift popularized by the 2011 book Predictable Revenue — they revolutionized GTM efficiency for themselves and every tech company there after. Instead of the traditional full-cycle sales role that dominated organizations like Xerox in the 1970s and '80s, Salesforce created a specialized, assembly-line model. Sales Development Representatives (SDRs) focused on outbound prospecting, Business Development Representatives (BDRs) handled inbound leads, Account Executives (AEs) closed deals, and Customer Success Managers (CSMs) ensured renewals and expansion. Solution Engineers, meanwhile, partnered with AEs to add technical depth to the buying process.
This structural shift mirrored broader changes in technology, market dynamics, and buyer behaviour. The key lesson? Every major technological transformation doesn’t just improve performance — it reshapes the very architecture of how we sell.
Which leads to today’s question: What will the next structural transformation of the Go-to-Market team look like in an era shaped by AI?
Looking Back to Look Forward
The Xerox-style sales model was built around territory management and personal relationships. Sales professionals managed every stage of the deal cycle — prospecting, discovery, demos, closing, onboarding, and account management. This model worked in an era where information asymmetry favoured the seller. Buyers needed salespeople to educate them, and longer sales cycles afforded time for relationship building.
But as the internet democratized access to information, the balance of power shifted. Buyers came to the table more informed, with defined needs and timelines. Sticking with the old full-cycle model in this new environment risked inefficiency, missed opportunities, and friction across the buyer journey. Salespeople were stretched thin across too many responsibilities, and no single part of the process was optimized.
Salesforce's model disrupted that. It recognized that buyers were increasingly self-educating and time-poor. Specialization improved responsiveness and performance at each stage of the funnel. This new structure allowed for clearer metrics, more scalable onboarding, and better alignment between marketing and sales.
Crucially, it also made sales a more inclusive profession. Companies could now bring in much younger and less experienced talent — often in their first or second job — and teach them a single part of the process at a time. This created career ladders, boosted team scalability, and fueled talent development in a way that the old full-cycle model never could.
Perhaps most importantly, the assembly-line approach unlocked significantly more enterprise value. It was a simpler, more repeatable model to deploy — especially in fast-growing, venture-backed environments. More companies could build effective GTM motions, faster. It democratized access to sales success and fundamentally changed the scale at which organizations could compete.
The takeaway: clinging to a familiar structure while the market evolves is a dangerous bet. Companies that adapted to the new buyer reality were the ones that grew fastest and built repeatable, scalable sales engines. Which brings us to today. Just as digital information changed the game twenty years ago, AI is poised to do the same now. And once again, sticking with old GTM structures could leave companies behind.
AI’s Disruption: Rewiring the GTM Org Chart
Artificial Intelligence is now playing a similar disruptive role. It’s automating data entry, enabling intelligent prospecting, writing first-draft emails, summarizing calls, and even forecasting pipeline more accurately than some sales managers. The result? Many current GTM roles are already being reshaped—and in some cases, their value is being questioned.
Just as the SDR role was born from a desire to offload prospecting from closers, AI will likely consolidate or eliminate roles that are heavily task-based. Consider the following likely implications:
- SDRs and BDRs: AI tools can now identify and prioritize target accounts, personalize outreach, and even execute multi-channel sequences. However, AI cannot legally initiate unsolicited phone calls to prospects who haven’t opted in — meaning the cold calling portion of the SDR’s role will likely remain human-led. In this evolving landscape, we may see a return to SDRs spending more of their time on phone-based outreach, while simultaneously managing and refining AI-driven campaigns. This represents a shift away from recent years where SDRs were primarily focused on deploying mass outbound email sequences. The traditional role of an SDR may evolve into something more hybrid and strategic, like an "AI Outreach Coordinator" who cold calls and also supervises automated engagement flows.
- Account Executives: Rather than spending time on qualification and repetitive follow-ups, AEs may shift focus to high-value conversations, complex negotiations, and aligning solutions to nuanced business problems. Today, AEs spend only about 25% of their time actually selling, with the rest eaten up by administrative tasks, internal meetings, and CRM upkeep. AI co-pilots could radically rebalance that equation—handling much of the prep, follow-through, and data entry—potentially freeing up sellers to spend the majority of their time in live selling conversations where they add the most value.
- Customer Success: AI-driven usage analytics and sentiment detection could replace some manual check-ins, allowing CSMs to focus on high-impact interventions. Or, in some cases, AI might do the intervening directly.
- Revenue Operations: As AI becomes more deeply embedded in CRM systems, the RevOps function could move from system maintenance and reporting to being strategic orchestrators of AI insights across the GTM motion.
New Roles for a New Era
Just as Predictable Revenue made SDRs a staple hire, AI will create new GTM roles we’re only beginning to understand. Here are a few we are already starting to see in the:
- GTM AI Engineer: Focused on technically deploying AI solutions across the GTM stack—ensuring integrations are implemented correctly, data flows cleanly between systems, and AI tools are configured to align with business goals. This role is becoming increasingly common as organizations move from experimentation to operationalizing AI at scale.
- AI Enablement Manager: Responsible for implementing, training, and continuously optimizing AI tools across the GTM function.
- Sales Process Designer: Tasked with redesigning workflows to integrate human and AI contributions seamlessly.
Thriving in the Transition
Every technological wave in GTM — from the Rolodex to Salesforce to ChatGPT — has demanded new team structures. AI won’t eliminate the need for human sales talent, but it will redefine where and how that talent adds the most value. For pre-IPO companies building teams today, the question isn’t whether AI will reshape GTM functions — it’s how quickly, and how proactively, you’ll adapt.
Making that adaptation successfully won’t come down to tools alone. It will require visionary leadership — leaders willing to rethink roles, reimagine workflows, and invest in upskilling their teams. It will demand a culture that values experimentation, continuous learning, and a willingness to challenge the status quo.
Organizations that cultivate this kind of environment will be better equipped to harness AI’s potential and turn disruption into a competitive advantage.
The companies that succeed in the next era won’t be the ones with the largest teams, but the ones with the most adaptable ones.
