Cold Calls, Warm AIs: Business Development is Dead...Long Live Business Development!

The Death of the SDR?

In the last few months, something quietly remarkable happened in B2B sales. A few of the companies that built their entire businesses on enabling SDR teams — names like Salesloft and Outreach — continued laying off their own SDRs. If that sounds ironic, it’s because it is. It’s like McDonald’s announcing the end of the burger. These are the companies that industrialized prospecting — fueled by sequences, cadences, and dashboards. Now they’re admitting what many of us believed (but got shouted down for saying) – that all their technology enabled people to do was to continue to commit marketing & sales crimes and misdemeanors, just in greater numbers!

I know, I did it

In ’93 (1993, NOT 1893, thanks!) we started a company that was built to do this – outbound B2B lead gen for tech companies, and it was new back then and fabulous! Other things (like email) emerged, but what really put the hammer to it was …you guessed it, everybody’s friend…Offshore. What happened next? The application of technology to compete with cheap labor offshore - the same as is happening now in the manufacturing space. SalesLoft (and the like) convinced companies to use their tech so their SDR’s could make 200 daily calls as opposed to 100 calls. As “lead rates” fall (as people become fatigued with the increasing volume of calls they receive as well as the declining quality ()people who can’t speak English)), then the “promise” of a Salesloft and many others was to enable you to keep cramming bigger numbers at the top. The math was misleadingly simple – when lead rates half, simply make twice as many calls! A race to the bottom.

The False Promise of the SDR Machine

The Sales Development model was, for a while, the closest thing to a religion in SaaS. Hire a small army of young, ambitious SDRs. Arm them with sales tech, email automation, and phone-dialing software. Then measure them — mercilessly — on volume.

More calls. More emails. More sequences. Because, as the logic went, “sales is a numbers game.” Except… the math stopped working. (never worked.) Buyers installed spam filters, ignored cold calls, and tuned out generic outreach. SDRs became noise machines — activity-rich, outcome-poor.

The tools that promised efficiency delivered fatigue — both for the prospects and the SDRs themselves. The model started to resemble a treadmill: lots of motion, very little progress.

Even worse, the function began cannibalizing trust. The more automation crept in, the more robotic outreach became — until prospects stopped answering altogether. And now, the irony is complete: the companies that powered that model are abandoning it internally.

Go to the Pod AND the video!

AI Blows Up the Math

The arrival of AI has been like pulling the plug on a faulty machine. Suddenly, sellers can do what used to take entire teams weeks. And they can do it better, faster, and with infinitely more context and relevance.

In other words, AI doesn’t just make prospecting faster — it makes random prospecting obsolete. It actually delivers real personalization, at (more) scale. In other words you can now achieve something that was a contradiction!

The Return of the Self-Sufficient AE

The pendulum has swung back to where it arguably always should have been — to the Account Executive who owns both creation and conversion. But this isn’t the “lone wolf” AE of the past. This is an AI-augmented AE — a seller who uses technology to exponentially increase their capabilities. Want a visual? Think Sigourney Weaver in Aliens, Tom Cruise in Edge of Tomorrow and Matt Damon in Elysium.

The emerging AE doesn't rely on inbound lists or SDR hand-offs. They use AI to surface insights, build narratives, and leverage context. They sound human because they are human — supported by tools that understand details and nuance. And in crowded, loud, homogeneous markets its ALL about details and nuance.

In this model, business development doesn’t disappear. It becomes part of the AE’s rhythm — a seamless blend of discovery, preparation, and engagement. The difference is that machines do the mining; humans do the meaning.

What Business Development Really Means Now

The phrase “business development” came to mean volume. An arbitrary number of opens, click, dials, touches led to “leads” and meetings booked. Now it’s about value discovery — finding where your offer intersects with a prospect’s opportunity or threat.

It’s not about who can contact more people. It’s about who can contextualize faster.

And that’s exactly where well applied AI excels.

AI can digest a target account's information, find and connect synergies and contextualize this across the landscape. That's not so much personalization as it is relevance. We've moved from the information age to the interpretation age. It doesn’t mean no one picks up the phone anymore. It just means they do it at the right time, for the right reason, and with something righteouse to say!

The ADR Era

So, if the SDR is fading, what replaces it? Maybe it’s something new: the ADR — AI (enabled) Development Representative.

An AI-driven system that:

• Researches markets and accounts.

• Flags opportunities based on synergies and timing.

• Drafts smart, relevant outreach communications.

• Hands the human a complete, contextualized picture of an opportunity.

  
  

The ADR doesn’t send 1,000 emails. It identifies the five conversations worth having, and gets the AE prepared faster and smarter. And in that world, “business development” doesn’t vanish — it finally grows up.

Full Circle

Ironically, this evolution brings B2B sales back to where it began. real people, having real conversations, about real business problems. When I started in this business sellers were responsible for their own prospecting right way through to their own closing – from first call to final close. The difference now is that the preparation is automated. AI doesn’t replace the human; it redeems them from bad process.

Maybe that’s the final twist in this long story. The SDR era didn’t die because of technology. It died because of its own lack of humanity. And AI — the very thing people feared would make sales less human — might be what makes sales more human…again.

AI's First Success is Exposing Our Failure!

The "perfect storm" happened when three forces collided. Are we seeing the same in AI?

Artificial intelligence promised transformation. Yet for all the hype, the reality is sobering: most AI (particularly in-house) projects are apparently failing.

AI is a messy marriage of technology, data, and human behavior. Too often, companies run headlong into this complexity, only to find that their projects stall, sputter, and eventually get shelved.

So why is this happening? Three overlapping issues as MIT, The Economist and Pavilion point to in recent articles and posts —human denial, structured resistance, and dirty data. These three forces create a perfect storm of failure. And together they raise a bigger question: is AI actually showing us that we’re not ready for it, by quickly revealing  all these deficiencies we have.

The Human Response: Denial and Distraction

The first roadblock is simply human nature. The MIT article highlights the very human reaction of denial. Leaders greenlight AI projects, but when the going gets tough, they retreat into old habits. Teams convince themselves that maybe the project was “too ambitious” or “ahead of its time.” The novelty wears off, enthusiasm wanes, and resources drift to shinier initiatives.

This isn’t a purely technical failure, it’s psychological. Humans resist confronting the uncomfortable truth that transformation requires hard, messy, sustained effort. We’d rather believe the problem lies elsewhere than face the reality that our own systems, processes, and even leadership resolve are inadequate.

The Economist’s Take: Structured Failure

If denial is the emotional response, inertia is the structural one. The Economist recently argued that organizations often engage in what amounts to “structured sabotage” of AI initiatives. Why? Because many employees—and sometimes managers—are invested in the status quo, and AI can be seen as an existential crisis.

AI, by its very nature, challenges the way things have been done. It shines a spotlight on inefficiencies, redundant roles, and outdated practices. For many, that feels threatening. So resistance builds quietly, sometimes consciously, often unconsciously. Meetings stall, committees debate endlessly, and pilots never scale. Leaders, eager to maintain consensus, allow inertia to set in.

It’s easier to preserve a quiet life than to confront existential fears. The possibility that AI might one day make us irrelevant only deepens the instinct to slow-walk adoption.

Dirty Data: The Silent Killer

Even if organizations overcome denial and inertia, they still face a technical trap: dirty data. As Sam Jacobs of Pavilion pointed out in a post on LinkedIn recently, AI is only as good as the data that feeds it. Yet many corporate data sets—especially CRM systems—are riddled with inaccuracies, duplications, missing fields, and outdated information.

The result? AI systems trained on polluted data deliver polluted outputs. Predictions become unreliable. Recommendations fall flat. Users lose trust in the system, and the project collapses under its own weight.

In other words, the biggest barrier to AI success may not be futuristic fears of “superintelligence.” It may be the mundane reality that we’ve spent 50 years building sloppy, fragmented data environments that AI ruthlessly exposes.

Is AI Showing Us the Problem Behind the Problem?

When you put these three forces together—denial, structured resistance, and dirty data—it can feel like success in AI is out of reach. Maybe AI isn’t failing us at all. Maybe it’s holding up a mirror to decades of systemic deficiencies: weak leadership resolve, consensus-driven indecision, and decades of (expensive, but) neglected data hygiene.

Seen this way, AI’s “failure” is actually its first success. It reveals the rot in our organizational foundations. It forces us to confront questions we’ve long avoided: Are we as clever as we think we are? Is our data trustworthy? Are our leaders willing to push through resistance? Do we really want transformation, or just the appearance of progress?

So Where Do We Go From Here?

The path forward is uncomfortable but clear.

1. Outsource and Leverage Domain Expertise

   
   

   For most companies, building AI in-house is a recipe for disappointment. The skill sets, infrastructure, and specialized expertise required are immense. Outsourcing to third-party experts—especially those with deep domain knowledge—offers a faster, safer, and more effective path. You wouldn’t build your own ERP system from scratch. Why should AI be different?

2. Leaders Must Lead, Not Manage by Consensus

   
   

   Transformation requires conviction. Leaders must be willing to push past resistance, make what might appear as unpopular calls, and carry the organization forward. Even AI projects can’t survive death by committee. They need champions.  People only fall into one of three camps here – they’re either part of the solution, part of the scenery or part of the problem. It's time for leaders to figure out who’s where and act accordingly.

3. Admit That Much of Our Data Is Junk

   
   

   Instead of endlessly cleaning up decades of bad CRM data, maybe it’s time to start fresh. Too many organizations have apparently wasted millions trying to polish the unpolishable. Accept that most legacy data is unreliable, and focus instead in the current on cleaner, leaner, more purposeful data sets going forward. Think addition by subtraction.

   
   

Final Thought

AI is not failing—it’s success lies in exposing our failure. It shows us the denial in our culture, the weakness in our leaders, the inertia in our systems, and the dirt in our data. Whether we treat that as a reason to retreat or advance is up to us.

The first step on the road to redemption is admitting the problem (actually there's three!!)

The companies that succeed will be those that accept the hard truths AI reveals, take action, and rebuild stronger foundations. The real question isn’t whether AI is beyond our capabilities, it’s whether we've got the stones to admit and confront the flaws it has exposed.

Why 95% of In-House AI Projects Fail (and Why Companies Keep Doing It Anyway)

We live in a "blame" culture and in-house AI projects are all about that!

This is a sobering reality in enterprise AI: despite the hype, 95% of in-house AI projects are failing. MIT’s State of AI in Business 2025 report makes it clear that while generative AI has transformative potential, most corporate efforts stall out well before delivering meaningful impact.

Executives may point to regulation, data quality, or immature technology, but the research suggests otherwise. The problem isn’t the models themselves—it’s implementation. And too many companies are treating AI like a shiny toy, not a serious business transformation.

Why In-House AI Projects Fail

So, why are so many companies falling into the 95%?

1. The Learning Gap – Generic tools like ChatGPT are good for individuals because of their flexibility. But they stall in the enterprise because they don’t integrate with workflows, don’t adapt to organizational processes, and lack the “stickiness” to drive change.

2. Resource Misallocation – The MIT report highlights that more than half of enterprise AI budgets are being spent on sales and marketing tools, even though the greatest ROI lies in back-office automation (eliminating outsourcing, cutting agency costs, streamlining operations) – more on this later.

3. DIY Mentality – Internal builds succeed only one-third as often as purchased solutions. Yet many firms still insist on going solo. It’s the corporate equivalent of assembling Ikea furniture without instructions—you might eventually get something resembling a table, but it’ll wobble every time you touch it.

4. Lack of Ownership – Successful AI adoption isn’t driven by central AI labs alone. It’s driven by empowering managers and frontline teams to embed tools into their daily work. Without that, the tech sits in a corner gathering dust.

   
   

And beyond these organizational barriers, there’s some very human factors:

·         People assume AI is easier than it is.

·         The novelty fades and teams get distracted or re-assigned.

·         AI is too often seen as “cheap” or even “free,” it receives less serious attention than it should. The result is a graveyard of half-finished pilots and underwhelming outcomes.

·         Structured failure – people don’t want to hear it, but failure is sometime deliberate. People fear AI and see an internal project as an easy way to prove it wrong!

The Psychology of the 95%

Here’s the kicker: while only 5% of in-house projects appear succeed (according to the MIT report), almost no company believes they’re in the failing majority. Statistically, of course, most of them are. But human nature kicks in, and no one wants to admit they’re part of the herd, lest they get “cut” from it!

This collective denial means organizations keep making the same mistakes: underestimating complexity, overestimating their capabilities, and clinging to the idea that “this time will be different.” It’s like watching 95% of drivers skid off the same icy corner and insisting, “We’ll be fine.”

In-House vs. Packaged: Why the Odds Are Stacked Against DIY

The second layer to this conversation is the difference between building AI in-house versus deploying purpose-built solutions. The MIT data already shows purchased solutions succeed twice as often as in-house builds, but the reasons go even deeper.

• AI Is Not Just About the LLM – Tools like Shadow (blatant sales pitch!) for example, don’t just sit on top of GPT. They blend an LLM with procedural code, custom workflows, session memory, and domain-specific expertise. In Shadow’s case, that means sales strategy, behavioral psychology, objection handling, and real-world B2B sales scenarios. You can’t replicate that from scratch with a general-purpose model.

• Security and Governance Are Already Solved – Enterprise AI requires airtight security: SOC2, GDPR, HIPAA compliance, SSO, access controls, and audit trails. Shadow, for example, is deployed in Microsoft Azure environments and uses enterprise-grade APIs where no customer data is retained. Building that level of governance from scratch is harder than people think, it’s expensive and risky.

• Integration Matters – A packaged solution integrates with Outlook, Teams, Salesforce, or whatever stack your team uses. More importantly, the UX is designed for how sellers actually work—so adoption is far smoother. A homegrown chatbot might “exist,” but if no one uses it, you haven’t solved anything.

• Time, Cost, and Risk – A proper in-house build isn’t weeks or months—it’s 6–12+ months of hard development time, requiring cross-functional teams of engineers, AI specialists, UX designers, and subject matter experts. Even then, the risk of missed edge cases and lack of adoption is high. In contrast, packaged solutions are live today, tested in real-world use, and refined through hundreds of iterations.

• Depth of Functionality – Beyond text generation, packaged solutions often include scraping engines, enrichment, retrieval-augmented generation (RAG), structured usage tracking, and clean export options. Replicating that stack from scratch is like building a skyscraper because you think the office rent is too high.

  
  

The Harsh Truth

The dream of in-house AI is seductive: control, customization, bragging rights. But the reality is that most companies don’t have the talent, time, or resources to pull it off. And the numbers don’t lie: 95% of in-house AI projects are failing.

Yet because no one wants to admit they’re part of the 95%, companies keep wasting money, attention, and momentum. The paradox is brutal: the very confidence that drives firms to build in-house is the same overconfidence that ensures their failure.

In contrast, packaged solutions—especially those built with domain expertise and hardened through real-world use—offer a path out of the cycle. They’re not just tools; they’re accelerators, designed to integrate into workflows, safeguard data, and deliver measurable business outcomes quickly.

Unless your core business is developing AI SaaS platforms, building in-house is at best a distraction and at worst a slow, expensive failure. Or put more bluntly: why try to reinvent the wheel when someone’s already built a Formula 1 car?