The Effective Seller

The Broken Promise

The sales industry made a promise over the last few years. It went something like this: give your team AI tools and they'll book more meetings, close more deals, and hit quota more consistently. The technology would handle the grunt work. Your people would be freed up to sell.

It was a compelling promise. Companies believed it. They invested accordingly. And then the numbers came in.

77% of sales reps aren't generating enough pipeline to hit quota. 91% of SaaS sales teams failed to hit quota expectations in 2024. According to CaptivateIQ's 2026 State of Sales report, 90% of reps faced obstacles hitting their targets last year, citing economic shifts, longer sales cycles, and competitive pressure.

These numbers didn't improve when teams added more tools. In many cases, they got worse.

Gartner predicts that by 2028, AI agents will outnumber human sellers by a factor of ten. But fewer than 40% of sellers will report that those agents actually improved their productivity. Meanwhile, 70% of sellers already say they're overwhelmed by the number of technologies required to do their work.

We were promised liberation. We got a more sophisticated kind of drowning.

The Silver Bullet That Wasn't

The belief that AI would fix sales was never really about AI. It was about a deeper, more seductive idea: that technology can solve human performance problems. That if you just find the right tool, the right automation, the right "digital worker," you can engineer your way around the messy, unpredictable, deeply human reality of selling.

This idea has been tested at scale. And the most dramatic test came from a company called 11x.

Founded in 2022, 11x raised $74 million from Benchmark and Andreessen Horowitz to build AI "digital workers" for sales teams. Their CEO claimed each digital worker would replace eleven full-time employees. The pitch was bold: you don't need SDRs. You need Alice, their AI agent that would prospect, personalise, and book meetings autonomously.

Then TechCrunch investigated. What they found was instructive. Most early customers had left during break clauses. ZoomInfo, listed publicly as a marquee customer, stated the product performed significantly worse than their own human SDRs. Users reported hallucinations, messages that missed the mark entirely, and in some cases, zero meetings booked after months of use. The company faced allegations of inflated revenue figures and claiming customers it didn't have.

The 11x story isn't just about one company's stumble. It's a parable for the entire industry's relationship with AI in sales. The technology was promising. The vision was appealing. But the fundamental assumption, that you can remove humans from a human process and get better results, was wrong.

And it's not just 11x. Here's a real message that was sent to Jake Cerulli, who leads the North America Sales team at memoryBlue, a company with 600 SDRs:

Jake's observation captures the entire problem in one sentence. The machine found the signal. But nobody, and nothing, did the thinking that should have come next.

The Misallocation

The deeper problem isn't that sales teams are using AI. It's that they're using it in exactly the wrong places.

They're automating the parts of selling that should be personal, like crafting messages and engaging with prospects. And they're manually doing the parts that should be automated, like building lists, enriching data, and tracking signals.

They're investing in tools before fixing the processes those tools are supposed to support. And they're neglecting the human skills that determine whether a conversation becomes a deal or a dead end.

The result is a generation of sellers who are faster at doing the wrong things. They can reach more inboxes, generate more activity metrics, and produce more dashboards, but none of it translates into more qualified conversations or more closed deals. And the result for buyers is an inbox full of AI-generated noise that sounds identical, feels identical, and gets deleted identically.

Something has to change. Not the technology. The thinking behind it.

What Nobody's Saying

There's a conversation happening across every sales floor, every LinkedIn feed, and every revenue leader's mind right now. It's about AI. How to use it. Which tools to buy. How to get more from it.

But there's a more important conversation that almost nobody is having. It's about the other side. The parts of selling that are becoming more valuable precisely because everything around them is being automated.

Think about it this way. When every company has access to the same enrichment data, the same AI-generated messages, the same sequencing platforms, and the same intent signals, what's left as a differentiator?

The human.

The quality of a question asked on a cold call. The ability to make a CFO feel understood in a fifteen-minute discovery session. The instinct to know when a prospect is politely brushing you off versus genuinely considering your proposal. The courage to tell a buyer "I don't think we're the right fit for you right now" and mean it.

These things cannot be automated. And they're not being talked about with anywhere near the seriousness they deserve.

The Appreciation of Human Skills

In economics, when supply of something increases, its value decreases. AI has flooded the market with automated outreach, AI-generated content, and machine-driven prospecting. The supply of "touches" has gone through the roof. So the value of any individual automated touch has collapsed.

But the inverse is also true. When the supply of something decreases, its value increases. And what's becoming genuinely scarce in B2B sales is human attention, human insight, and human connection. The supply of thoughtful, relevant, clearly human outreach is lower than ever, which means its value to the recipient is higher than ever.

This is the dynamic that most sales leaders are missing. They're optimising for volume when they should be optimising for signal. They're investing in the layer that's depreciating, automated activity, and underinvesting in the layer that's appreciating, human capability.

The Uncomfortable Implication

If human skills are the real differentiator, then the way most sales organisations allocate their budget is backwards.

The average company now spends over $10,000 per rep per year on sales technology. That's before you count the CRM. A mid-market SaaS company with 20 sellers is spending $200,000+ annually on tools alone. Industry benchmarks suggest companies allocate 15-20% of a rep's base salary to their tech stack, which for a mid-market AE earning $80,000 means $12,000-$16,000 per year in software just to support one person. And according to Forrester, only 53% of reps say their sales technology actually improves their productivity.

Meanwhile, those same companies spend a fraction of that, sometimes literally nothing, on developing the skills of the people using those tools.

It's like buying a Formula 1 car and never teaching the driver how to take corners. The machine is extraordinary. But the machine isn't the constraint. The human is.

And this is what nobody wants to say out loud, because it's harder to solve than a technology problem. You can buy a tool in a week. You can't develop a seller in a week. Building genuine human capability takes time, coaching, repetition, and sustained investment. It doesn't fit neatly into a quarterly business review. It doesn't have a clear ROI line item. And it requires leaders to admit that their people, not their stack, are the bottleneck.

That's uncomfortable. But it's the truth. And until more leaders face it, the promise of AI in sales will remain exactly that. A promise.

The Effective Seller

So what does it actually look like when someone gets both sides right? When a seller has figured out where the machine ends and the human begins, and is exceptional at both?

The Effective Seller isn't a job title. It's a standard. It applies whether you're an SDR booking meetings or an AE closing deals. The balance shifts between the two roles: SDRs lean heavier on the automation layer, AEs lean heavier on the human layer. But both need both.

Rather than describe this in the abstract, let me show you what it looks like in practice.

A Day in the Life: Alison, The Effective SDR

Alison sells payroll technology to mid-market companies. Her buyers are typically CFOs, Heads of Finance, and Payroll Managers at companies with 100-500 employees who've outgrown their existing payroll provider.

It's 8:45am. Alison opens her laptop. Her overnight signal alerts have surfaced three accounts that matter: a logistics company that just expanded into Germany (triggering multi-country payroll complexity), a retail chain that posted a Head of People Operations role last week (suggesting they're professionalising HR infrastructure), and a construction firm whose CFO mentioned "manual payroll errors" in a comment on a LinkedIn post two days ago.

She didn't search for any of this. Her system flagged it, enriched the contacts, verified emails, and drafted first-pass messages referencing each signal. The drafts are decent. They're not perfect. She spends eight minutes turning three AI-generated templates into three messages that sound like her. For the logistics company, she adds a line about the specific payroll compliance headaches of employing staff in Germany. For the construction firm, she references the CFO's own words about manual errors and asks whether it's a process problem or a provider problem. She removes a sentence from the retail message that felt too salesy.

By 9:00am, she's done with admin. She hasn't opened a spreadsheet. She hasn't manually searched for contacts. She hasn't copied and pasted a single data point.

From 9:00 to 11:00, she's on the phone. Not reading scripts. Making calls with context. She knows why she's calling each person, what signal triggered the outreach, and what problem she wants to explore. Most calls go to voicemail. She leaves voicemails that reference the signal and ask a genuine question. Three people pick up. The first, a Payroll Manager, is polite but says they renewed their contract three months ago. Alison doesn't push. She asks if she can check back in six months and whether there's anything about their current setup that would make them open to a conversation before then. The Payroll Manager pauses and mentions that their provider still can't handle contractor payments properly. Alison makes a note.

The second call is the CFO at the construction firm. He picks up, slightly surprised. Alison opens: "I saw your comment about manual payroll errors. I'm curious, is that a scale problem, as in too many employees for the current system, or is it more of a process gap?" The CFO talks for five minutes. It's both. They're running payroll for 280 site workers across three regions on a system built for 50 office staff. Alison doesn't pitch. She asks what the cost of an error looks like in construction payroll, where compliance mistakes can mean penalties. They book a meeting. The CFO agrees to bring the Head of Operations.

The third call is quick. A Head of Finance who's curious but in back-to-back meetings. She asks Alison to send something short by email. Alison does, referencing the signal and asking one question. It takes two minutes.

At 11:00, she reviews her pipeline. Three meetings booked this week, all from signal-triggered outreach. Two are strong: the prospects articulated a real problem and agreed to bring a colleague. One is weaker. She makes a note to qualify harder at the start of that call.

The rest of her morning is LinkedIn. Not mass connection requests. Five thoughtful comments on posts from CFOs and payroll professionals in her territory. A direct message to a Head of Finance who posted about switching accounting systems, asking whether the payroll integration was part of that decision. A short post sharing an anonymised insight from this morning's call about the hidden costs of outgrowing your payroll provider.

By lunchtime, she's had five genuine conversations. She's booked a meeting. She's added value to five people's feeds. And she's spent zero time on data entry, list building, or copy-pasting between tabs.

That's what effective looks like.

A Day in the Life: James, The Effective AE

James works for a legal technology company that sells contract lifecycle management software. His buyers are General Counsels, Heads of Legal Operations, and Chief Legal Officers at mid-market and enterprise companies drowning in manual contract processes.

It's 9:00am. James has three calls today. His first is a discovery call with a Series B fintech whose General Counsel started eight weeks ago. Before the call, James reviews the AI-generated brief: company background, recent Series B raise of $40M, current headcount of 180 and growing fast, the GC's LinkedIn history showing she came from a large bank where she led a legal ops transformation, plus a summary of the SDR's notes from the initial conversation mentioning "contract bottleneck slowing product launches." The brief took the machine ninety seconds to produce. James spends ten minutes reading it and writing down the three questions he really wants to ask.

The call starts. James doesn't open with a deck. He doesn't run through a slide of company credentials. He asks: "You're eight weeks into the role. What's surprised you most about how this team handles contracts?"

The General Counsel talks for six minutes. James listens. He hears that every commercial agreement goes through a single legal counsel who manually redlines in Word. Product launches are being held up by two to three weeks because legal can't turn contracts around fast enough. The CEO is frustrated. The GC was hired specifically to fix this, and she knows what good looks like from her previous role, but she hasn't had time to evaluate solutions yet.

James doesn't pitch a solution. He asks what the GC thinks the root cause is: is it a volume problem, a process problem, or a capability gap in the team? They explore it together. The GC realises mid-conversation that it's all three, but the volume problem is the one creating immediate pain because the sales team is closing deals faster than legal can paper them. James suggests involving the CEO in the next call, framing it as: "If contract turnaround is blocking product launches, the CEO probably wants visibility on the fix." The GC agrees. They book it before hanging up.

His second call is a follow-up with a deal that's been running for three weeks. The champion, a Head of Legal Ops at a healthcare company, is engaged but the CFO has concerns about timing. James doesn't push. He asks what would need to be true for the timing to feel right. The champion reveals that there's a board meeting in six weeks and the CFO wants to present a compliance risk reduction plan. James reframes the engagement as the plan itself. The contract management platform becomes the evidence for the CFO's board presentation: reduced risk, faster turnaround, audit trail. The deal progresses.

His third call cancels. Instead of filling the gap with busywork, James spends thirty minutes reviewing call recordings from yesterday and writing notes on what he'd do differently. He sends a short voice note to the SDR who booked his morning discovery call, thanking her for a well-qualified meeting and sharing one thing the GC said that the SDR might use in future conversations with legal buyers.

By 4pm, James has progressed two deals, strengthened a relationship with an SDR, and invested in his own development. His CRM is updated, not because he spent twenty minutes on data entry, but because his system captured the call summaries and next steps automatically.

That's what effective looks like at the AE level. Less activity. More impact.

The Pattern

Notice what Alison and James have in common. The machine handled everything that didn't require judgement: data, signals, logistics, sequencing, CRM. They handled everything that did: conversations, questions, listening, decisions about when to push and when to pause.

Neither of them is anti-technology. They're deeply confident with their tools. But they're not dependent on them. If the automation layer disappeared tomorrow, they'd be slower but still effective, because their real value lives in the conversations, not the systems.

That's the standard. That's what organisations should be building towards. And most aren't, because they're stuck in a trap.

The Automation Trap

I'm not anti-automation. I spend a significant part of my working life helping teams set up Clay workflows, design signal-based prospecting systems, leverage the latest LLMs to build richer understanding of their clients and personas, and construct the automation layer that frees sellers to do their real job.

But I've also seen, dozens of times, what happens when automation is applied without thought. When it's layered onto broken processes. When it replaces judgement instead of supporting it. When the goal becomes "automate as much as possible" rather than "automate what's appropriate."

The automation trap is seductive because it feels like progress. You're doing something. You're implementing. You're modern. But activity and progress aren't the same thing.

What Should Be Fully Automated

Let me be specific. There are parts of the sales process that should be handed to the machine entirely, because they're data-processing tasks where speed and accuracy matter more than nuance.

What Should Be Augmented

Then there are tasks where AI does the first pass and a human refines it, because the output needs a human eye before it reaches a prospect or informs a decision.

What Should Never Be Automated

And then there's the third category. The parts of selling that must remain human, not because the technology isn't sophisticated enough yet, but because the human element is the value.

Live conversations. Cold calls. Discovery sessions. Negotiations. Objection handling. Relationship building. Coaching. The moments where trust is built or lost, where deals advance or stall, where a seller's ability to read the room and respond in real time is the entire point.

Every time I hear about a company automating their cold calls or letting AI run discovery, I think of the 11x story. Not because the technology will always be bad. It'll get better. But because the buyer on the other end is a human being who can tell the difference. And in a world drowning in automated outreach, the human touch isn't a nice-to-have. It's the whole competitive advantage.

The Real Trap: Premature Automation

Premature Automation scales problems. If your targeting is off, automation reaches the wrong people faster. If your messaging is generic, automation sends generic messages at volume. If your follow-up sequence has no value in steps two through five, automation delivers that emptiness on schedule.

The fix isn't less automation. It's automation in the right order. Fix the foundations. Then automate the things that free up human time. Then invest that freed-up time in the skills that actually drive revenue.

In that order. Always in that order.

The Human Premium

If the automation layer is about efficiency, the human layer is about effectiveness. They're not the same thing. You can be extraordinarily efficient at reaching the wrong people with the wrong message. Effectiveness is about the quality of what happens when you're in front of the right person at the right time.

The skills that follow aren't comprehensive. They're the ones I see making the biggest difference between average sellers and exceptional ones, across both SDR and AE roles. They're also the ones I see most consistently neglected.

Cold Calling as a Differentiator

I know. Nobody wants to hear this. But cold calling has become one of the most effective channels in outbound precisely because so many teams have abandoned it in favour of automated email.

Think about it from the prospect's perspective. Their inbox is full of AI-generated messages. Their LinkedIn is full of connection requests followed by automated sequences. But their phone? It barely rings for prospecting anymore. Which means that when it does, and when the person on the other end says something relevant, specific, and clearly human, it stands out.

The Effective SDR doesn't read a script. They open with context. "I noticed you've just posted two SDR roles. Is that because you're scaling the team or because the current approach isn't working?" That's a question that earns the next sixty seconds. And those sixty seconds are where meetings get booked.

Cold calling is a human skill. It requires composure, curiosity, the ability to read tone, and the confidence to ask questions that most people would avoid. These are not things you develop by buying a tool. They're things you develop by practising, getting feedback, and doing it again.

Discovery That Changes Thinking

Most discovery calls follow the same pattern. The seller runs through a checklist of qualifying questions. The prospect gives polite, surface-level answers. Both sides go through the motions. The deal moves forward on momentum rather than conviction.

The Effective AE does something different. They ask questions that make the prospect think about their problem in a new way.

Not "What are your biggest challenges?" which gets a rehearsed answer. But "You mentioned your team is processing 200 orders a day manually but fulfilment errors have doubled since last quarter. Is that a capacity issue or a systems issue?" That question forces reflection. It invites honesty. It positions the seller as someone who's trying to understand rather than someone who's trying to qualify.

Great discovery isn't about gathering information. It's about creating clarity. When a prospect finishes a call understanding their own problem better than they did before it started, they don't need to be sold to. They've already started selling internally.

Qualification as a Discipline

This applies to both roles. For SDRs, it's the discipline of not booking a meeting just because someone said yes. A meeting with a prospect who has no authority, no budget, and no urgency is worse than no meeting, because it wastes the AE's time and creates pipeline fiction that damages forecasting.

For AEs, it's the discipline of walking away from deals that don't fit. Of telling a prospect "I don't think we're the right solution for you" when the evidence points that way. Of optimising for the right conversion rather than any conversion.

This requires a kind of professional courage that's easy to admire and hard to practise. It means saying no to revenue in the short term because you know it'll save you from churn, misalignment, and reputation damage in the long term. It means trusting your own judgement over your pipeline report.

Storytelling and Framing

Facts inform. Stories persuade. Every seller knows this in theory. Very few practise it consistently.

The Effective Seller doesn't present features and benefits. They tell a story about a company like the prospect's that faced a similar challenge and what happened when they addressed it. They frame the conversation around the prospect's world, not the product's capabilities. And they create urgency not through artificial deadlines or discounting but through a compelling narrative about what's at stake if nothing changes.

Storytelling is trainable. It's not a personality trait. It's a skill that improves with structure, practice, and feedback. But it has to be invested in, which means someone has to decide it's worth investing in. That decision is what separates teams that close deals from teams that present and hope.

Curiosity, Trust, and Authenticity

In a market saturated with automated outreach, authenticity is the ultimate unfair advantage. Prospects can feel it. They can tell when someone is genuinely curious about their situation versus following a playbook. They can tell when a message was written by a person who thought about them versus generated by a system that scraped their LinkedIn.

Curiosity is the engine behind all of this. A genuinely curious seller asks better questions, listens more carefully, and follows threads that a less curious person would miss. They learn faster, adapt quicker, and have better conversations because their interest is real, not performed. You can't fake curiosity. Prospects know the difference between someone who wants to understand their business and someone who wants to qualify them into a pipeline stage.

Trust is earned through consistency, honesty, and demonstrated competence. Not through forced friendliness. Not through manufactured rapport. Through being reliably useful, reliably honest, and reliably yourself.

The Effective Seller sounds like themselves. They have a voice that prospects remember. They're the person in the inbox or on the phone who doesn't sound like everyone else, because they're not trying to.

The Resilience Question

There's one more human skill that deserves attention, and it's not about selling at all. It's about durability.

Sales is rejection at scale. An SDR can do everything right and still get ignored. An AE can run a flawless process and still lose the deal. The Effective Seller isn't the one who never faces setbacks. They're the one who faces them without losing their edge.

This means building confidence on preparation rather than outcomes. Walking into every call knowing you've done the work, regardless of whether the last five calls went well. It means managing energy, not just time, understanding your own rhythms and protecting your best hours for your highest-value work.

And it means having the emotional intelligence to recognise when AI anxiety is driving your decisions. SDRs worry about being replaced. AEs worry about relevance. Managers worry about what their team's purpose becomes as automation expands. The antidote isn't denial. It's development. Build the skills that machines can't replicate, and you'll always have a seat at the table.

How to Build This

If you're a founder, a VP of Sales, or an SDR manager reading this and thinking "this is what I want my team to look like," the obvious question is: how?

The honest answer is that building Effective Sellers isn't a project. It's a culture. It's not something you implement in a quarter and move on from. It's a set of ongoing decisions about where to invest time, money, and attention, and where not to.

What follows isn't a step-by-step plan. It's a set of principles I've seen work across the teams I've worked with. The specifics will vary by company. The principles don't.

Fix Before You Build

This is the principle that gets ignored most often, because it's the least exciting. Before you invest in new automation, new training, or new hires, look at what you've already got and ask: is this working?

Is your targeting tight? Are you reaching out to the right companies and the right people within those companies? Is your messaging relevant, does it lead with the prospect's problem or your product's features? Is your timing intentional, are you using signals to prioritise, or is everyone getting the same sequence at the same time?

Most teams I work with have at least two of these fundamentals broken. And no amount of AI will compensate for them. A brilliantly automated sequence reaching the wrong people is still reaching the wrong people. Just faster.

Fixing foundations is unsexy but high-leverage work. It typically costs nothing except honest assessment and the willingness to acknowledge that something you built isn't working. Start there.

Invest in Both Layers Simultaneously

The automation layer and the human layer aren't sequential investments. They're parallel ones. You need to build the infrastructure that frees up seller time and invest in what sellers do with that freed-up time.

I've seen teams build beautiful automation systems and then wonder why pipeline didn't improve, because their sellers had more time but no better skills. And I've seen teams invest heavily in training while their reps still spent three hours a day on manual data work, which meant they never had enough time to practise what they'd learned.

Both layers matter. Budget for both. Staff for both. Measure both.

Make Coaching Non-Negotiable

If there's one thing that separates high-performing teams from average ones, it's the presence of a coaching rhythm. Not occasional feedback. Not annual reviews. A weekly, structured, non-negotiable practice of reviewing calls, providing specific feedback, and creating space for sellers to improve.

Coaching doesn't have to be complicated. It can be as simple as listening to one call per rep per week and sharing two things they did well and one thing to try differently. What matters is that it happens consistently, that sellers know their work will be reviewed, that feedback is normal, and that improvement is expected.

The teams I've seen transform fastest are the ones where the manager treats coaching as their primary job, not an addition to their primary job. When the manager's calendar shows it, when coaching sessions are booked and protected the way pipeline reviews are, the team responds.

Measure What Matters

Most sales dashboards measure activity: emails sent, calls made, meetings booked. These metrics tell you whether people are busy. They don't tell you whether they're effective.

The Effective Seller model asks you to measure differently. Not just how many meetings were booked, but what percentage of them converted to qualified pipeline. Not just how many emails were sent, but what the reply rate was on signal-triggered versus non-signal outreach. Not just whether AEs hit revenue targets, but whether their discovery calls resulted in multi-threaded deals that closed on time.

Meaningful Conversations as a Metric

There's one metric that almost nobody tracks but should: meaningful conversations.

Here's why. Chet Holmes's Buyer's Pyramid tells us that at any given time, only about 3% of your market is actively buying. Another 7% are open to it. The remaining 90% aren't thinking about buying right now, but they might be in six, twelve, or eighteen months.

Most outbound metrics only value the 3%. A meeting booked counts. A reply that doesn't convert to a meeting doesn't. But that reply, if it was a genuine exchange where the prospect learned something, shared a challenge, or remembered your name, has value. It influences what happens when that prospect does enter the market. It creates awareness, familiarity, and trust that pays off later.

A meaningful conversation is one where the prospect engaged, even if they didn't convert today. They asked a question back. They shared context about their situation. They said "not now, but reach out in Q3." They commented on your LinkedIn post after your call. These are the leading indicators that your outbound is working on the 97%, not just the 3%.

Track them. Report on them. Celebrate them alongside meetings booked. Because the team that builds relationships with the 97% today will own the pipeline when those prospects are ready to buy.

Prepare for the Future

The way sales teams are structured is about to change fundamentally. Not might change. Will change. The only question is which organisations move first and which get forced into it later.

I see two shifts happening simultaneously, and the companies that thrive will be the ones that understand how they work together.

Prediction 1: The Return of the Full-Cycle Rep

Ebsta's 2025 GTM Benchmarks Report found that 46% of SaaS and tech companies are already returning to a full-cycle sales model, where one seller manages the entire customer journey from prospecting through closing to post-sale expansion. That's not a fringe experiment. That's nearly half the market.

The reasons are compelling. Today's buyers expect a seamless, consultative experience. They don't want to repeat their story three times to three different people as they're handed from SDR to AE to CSM. The traditional role-based model creates disjointed handoffs and repetitive conversations. Buyers feel like they're on a factory line rather than being guided by a trusted partner.

The economics back it up too. Every handoff introduces friction: delays, miscommunication, dropped context. Full-cycle sellers operate with fewer handoffs, which means faster deal cycles, lower cost per acquisition, and stronger customer relationships. Forrester's research suggests this approach drives a 38% higher win rate. McKinsey estimates it reduces the sales cycle by 20%.

And here's a stat that should make every revenue leader pay attention: 52% of new revenue in 2024 came from existing accounts. Expansion is no longer an afterthought. A full-cycle seller who stays involved for the first twelve months after the sale is uniquely positioned to identify upsell and cross-sell opportunities, because they've built the relationship, know the account, and can act before the customer even realises what they need next.

This won't happen everywhere. Complex enterprise sales will likely retain specialised roles. But for mid-market SaaS, the direction is clear: fewer handoffs, stronger relationships, and sellers who own the entire motion.

Prediction 2: The Rise of the GTM Engineer

The second shift is the emergence of a dedicated role that builds and maintains the automation layer so that sellers don't have to. This role is already exploding. An analysis of over 1,000 job postings found that GTM Engineering roles grew 205% year-over-year in 2025. Companies like Vercel, OpenAI, and Ramp are paying $200,000+ for this talent. The average salary sits at $127,500, with top-tier companies offering well over $200,000.

The GTM Engineer (sometimes called RevOps Engineer or Revenue Operations Engineer) sits at the intersection of sales operations and technical implementation. They're the person who builds the machine so the sellers can focus on the conversation. Clay appeared in over 90% of GTM Engineer profiles analysed, making it the closest thing to a universal tool for this role. SQL and Python show up in 38% of job postings, showing the technical depth expected.

The most common career path into GTM Engineering is instructive: SDRs and BDRs who got frustrated with manual prospecting and broken data, taught themselves automation tools, built workflows that their whole team started using, and formalised into a dedicated role. They understand the pain because they lived it.

In practical terms, a GTM Engineer owns:

The GTM Engineer doesn't replace sellers. They make sellers dramatically more effective by removing the technical and operational burden that currently consumes a huge portion of a seller's day. With a GTM Engineer in place, the ratio of SDRs to AEs can shift, because each SDR becomes significantly more efficient and effective when they're not building their own lists, managing their own data, and configuring their own tools.

What Won't Survive

Here's the uncomfortable truth: the traditional SDR-to-AE organisation, as most companies run it today, won't survive.

Not because the roles themselves are wrong, but because the way they're typically structured, with SDRs manually prospecting, AEs manually managing deals, and nobody owning the automation layer, is fundamentally inefficient. The companies that layer AI and automation on top of this broken structure without fixing the foundations will accelerate their problems, not solve them. They'll send more irrelevant messages faster. They'll fill pipelines with unqualified meetings more efficiently. They'll burn through their total addressable market in months instead of years.

The companies that will thrive are the ones that do everything this manifesto describes: fix the foundations first, build an intelligent automation layer (ideally owned by a GTM Engineer), develop sellers who are confident with AI but exceptional in conversations, and invest in both the machine and the human with equal seriousness.

Whether that looks like full-cycle reps supported by a GTM Engineer, or a leaner SDR-AE structure where automation handles the grunt work and coaching develops the human skills, the principle is the same. The future belongs to organisations that get the balance right. The ones that don't will be outpaced by smaller, smarter teams that did.

Conclusion

The thesis of this manifesto is simple, even if the execution isn't.

The future of sales doesn't belong to the teams with the most sophisticated technology. And it doesn't belong to the teams that reject technology in favour of old-school hustle. It belongs to the teams that figure out the right balance between both, that automate what should be automated, keep human what should be human, and invest in both layers with equal seriousness.

The Effective Seller is the person who embodies that balance. They use the machine to eliminate the grind. They show up to conversations prepared, present, and genuinely skilled at the human parts of selling that no algorithm can replicate. They're not threatened by AI because they've figured out how to work with it. And they're not dependent on it because their real value lives in the conversations, not the systems.

Building a team of Effective Sellers requires three things:

The order matters. Fix first, automate second, train third. Most teams do it backwards. The ones who get it right don't just hit quota more consistently. They build something durable. A team that's effective regardless of which tools they're using, because the skills live in the people, not the platform.

Where to Go From Here

If this manifesto has made you think differently about how your team is investing in technology versus people, that's a start. But thinking and doing are different things.

The next step is honest assessment.

If you know the answers and you know what to fix, this manifesto has done its job.

If you'd like help figuring it out, I'd welcome the conversation.