The Sales Adventures of Shadow Episode II - An Unexpected Turn

Like it or leave it...

& why what you see isn't usually what you get!

Do You Really Need to Be Liked in Sales?

A while back, I was a rookie sales rep at an American computer firm in London. Like many fresh hires, I believed a great salesperson had a “gift of the gab,” a bottomless well of enthusiasm, and a big personality. The “good time Charlie.” One colleague joked that the best sales rep was someone who lived way beyond their means—a lifestyle that forced them to sell relentlessly. We all laughed, but there was truth beneath the humor: desperation and motivation can intersect.

Then there was Frank, the brash rep who we all loved. He closed a colossal deal by appearing to befriend a prospect who seemed in need of a pal. I’d watch them linger in pubs near Fleet Street and wonder, Doesn’t this guy see Frank is “playing” him? But the sale got signed, and Frank rode off into the sunset with a hefty commission. It raised a timeless question: Must you like someone to do business with them? And from the seller’s side, do I need to make you like me to seal the deal?

Listen to the Pod:

The Role of AI in B2B Sales

Enter modern B2B sales, where artificial intelligence has made an impressive splash. AI can highlight a seller’s expertise right away, analyzing, crunching, summarizing and presenting insights to show real value before the “liking” part even has time to blossom. Trust—is in part based on confidence in someone’s ability—can be built faster if your prospect sees you know your stuff. AI helps here, if you use it right, it helps – big time! AI cannot make you more likable. It simply frees you to showcase genuine insight instead of wasting time on guesswork or repetitive tasks.

Why Being Liked (Still) Matters

Relationships in business are complicated. Firms set up strict ethics policies while simultaneously using posh corporate suites to wine and dine clients. Humans crave both rational fairness and a personal edge—it’s in our DNA. So, even if you bring stellar value, it usually doesn’t hurt if customers can stand being in the same room with you.

But how much do they need to like you? It’s true that sometimes, a business relationship can thrive on pure practicality: if your solution is that good, people might swallow their dislike of your personality. Yet more often, “liking” functions as a social lubricant that makes deals less cumbersome.

Psychologists point out that “ people like people who are like they are,” holding common interests, shared values, or even trivial coincidences like rooting for the same sports team. Studies show we’re drawn to similarity because it validates our beliefs and helps us feel less alone. We also assume people who are similar will like us back.

The Classic Advice: Carnegie and Bunnell

In How to Win Friends and Influence People, Dale Carnegie outlined key principles: show genuine interest, smile, remember names, be a good listener, talk about others’ interests, and make them feel important. Great advice, but if applied mechanically, it can come off as fake.

Mo Bunnell’s Snowball Effect highlights five “Drivers of Likeability”: commonality, frequency, mutuality, balance, and uniqueness. Essentially, the more meaningful interactions you share, the more likely people are to feel comfortable and valued. Genuine conversation (not shallow flattery) is key.

Can You “Make” Someone Like You?

It depends on how you define “make.” You can absolutely dial up your friendliness: ask questions, remember details, share a laugh. But if you’re transparently manipulative, people sense it. Worse, trying too hard might backfire, causing prospects to wonder why you’re so eager to be their best friend.

Still, in a B2B world loaded with choices, personal rapport can be a deciding factor—especially when competing solutions feel similar. People gravitate toward those they trust to deliver not only results but also a decent working relationship.

Where AI Fits In

AI can accelerate trust by promoting capability fast: generating insights, presenting ideas, backing them up, or identifying market gaps. Buyers see you’re prepared and knowledgeable, which can nudge them to invest time in getting to know you. But AI won’t fake a shared sense of humor or sincerely empathize with a client’s struggles. That’s where your human side remains irreplaceable.

Being True to Yourself (Without Being a Jerk)

Ultimately, “be yourself” might be the best (if clichéd) advice. If you’re naturally warm and funny, let that shine—but don’t treat every interaction like open-mic night. Likewise, if you’re a no-nonsense type, trust that some customers appreciate a direct approach. Just be aware that extreme behaviors can alienate people. Manage your quirks to avoid sabotaging your own efforts.

Final Thought: Balancing Trust, Value, and Personality

At the end of the day, people buy from those who meet their needs—whether that’s a solution to a pressing problem, a spark of inventive thinking, or even just a relief from a day of tedium. This is further confused by social media, personal brands and virtue signaling. Authenticity is hard to recognize. Being liked won’t close a deal that offers zero real value, but showcasing real value is easier if people don’t dread your presence. AI can do the heavy lifting on data and logic, freeing you to refine your human touch.

So, can you make someone like you? Perhaps not entirely—but you can be a trustworthy, competent person who’s enjoyable to do business with. When you add AI’s ability to display expertise right from the start, you create a powerful formula: trust plus value sets the stage, and likability keeps the conversation rolling. In the end, authenticity and skill work together to make real connections—and those connections often lead to the best business relationships of all.

The Reality of AI in Sales: Why ‘Off-the-Shelf’ GenAI Apps Will Fail

The more I work with AI—especially our AI-powered sales readiness tool, Shadow—the more I realize just how difficult it is to truly productize these applications. That’s not say other people, and us, won’t try. GenAI and large language models (LLMs) are vast, and in order to extract real value from them, you have to dig deep. Unlike traditional software, where prescriptive applications dictate what users need, AI operates differently—it’s a tool that requires more active engagement, iteration, and refinement to deliver the right output. Companies need to adopt a more “innovation” (rather than “invention” ) approach to using AI, as McKinsey point out in a recent article. Again it’s not “that” you use AI, it’s “how.”

The Pitfall of Prescriptive AI Apps

Most off-the-shelf GenAI solutions promise ease of use and instant value, but they fundamentally misunderstand the complexity of modern business functions—especially B2B sales. The reality is, most developers of these AI tools have little to no experience in B2B sales.

Here’s the problem: sales teams are expected to differentiate themselves in increasingly sophisticated ways. They don’t just need answers—they need nuanced insights enabling them to shape conversations, drive preference, and influence decision-makers. AI models that assume a one-size-fits-all approach will fail to deliver this, leading to frustration and rejection by sales teams who, after a poor first impression, may dismiss AI entirely.

Listen to the pod:

The Trade-Off: Quality vs. Speed in AI

One of the biggest challenges in developing AI-powered business tools is balancing response quality vs. response speed.

When using a private GPT for sales enablement, the amount of external data retrieved at query time impacts both response quality and speed. Expanding the indexed knowledge base generally leads to richer, more accurate answers but may slow down response times due to the retrieval process.

This means users often need to engage in a back-and-forth with AI to refine their results. While this is natural for those willing to invest time, it conflicts with the expectation set by off-the-shelf AI solutions: “It should just work.”

Most vendors prioritize speed over substance, leading to shallow, generic outputs. But in the details and nuances is where B2B sellers create differentiation—the very thing that drives deals forward. AI tools and the engagement models of those companies that fail to support this level of depth will be abandoned.

The CRM Fallacy: Inside-Out Thinking

Large software vendors like Salesforce are making similar mistakes. Their approach to AI is driven by an inside-out perspective—building AI as an extension of CRM rather than as an independent sales intelligence tool.

This results in AI that is:

• Data-centric, not seller-centric

• Process-driven, not insight-driven

• Built for efficiency, not effectiveness

The assumption that AI should fit neatly into existing workflows ignores the reality of how top-performing sales teams operate. They don’t need an AI that simply summarizes CRM data—they need one that helps them think, strategize, and sell more effectively.

Now, this is not to say there isn’t some value in this approach. Prospects will need to decide whether value in the “platform” approach outweighs value in the standalone app. It’s ERP (or CRM) Vs Point Solution all over again.

AI Success Will Require Deep Client Collaboration

For the foreseeable future, the most successful AI solutions won’t be off-the-shelf products—they’ll be tailored, fine-tuned solutions customized with customers. Companies that want to harness AI effectively must invest time in:

1. Training AI models on their unique business context

2. Iterating on prompts and use cases to improve outcomes

3. Teaching teams how to interrogate AI for better responses

This means the subscription-based AI model—where companies expect an AI app to work flawlessly out of the box—is fundamentally flawed. The vendors who engage deeply with customers to refine their AI models will win.

The Funding Dilemma: Chasing the Wrong AI Model

The push for AI startups to scale fast is further complicating the landscape. VCs and private equity firms are chasing “unicorns”—betting on the first mover rather than the best mover.

But history tells us that being first doesn’t guarantee success:

• Google wasn’t the first search engine.

• Facebook wasn’t the first social network.

• Apple didn’t invent the smartphone.

• Microsoft wasn’t the first operating system.

• Zoom wasn’t the first video conferencing tool.

The real winners in tech are the fast followers—companies that refine the business model, improve user experience, leverage ecosystems, or pivot at the right moment.

Many of today’s AI startups are overpromising and underdelivering in their rush to market. The result? A wave of AI fatigue as users lose faith in AI’s ability to drive real value.

The Future of AI in Sales & Business

For AI to truly transform sales, marketing, and other business functions, vendors need to shift their approach:

• Stop promising magic. AI isn’t plug-and-play; it requires iteration and refinement.

• Prioritize effectiveness over efficiency. Speed matters, but insight is what closes deals.

• Engage deeply with users. The best AI solutions will be those built with the people who actually use them.

AI has the potential to revolutionize business, but only if it’s deployed intelligently. Companies that chase hype and shortcuts will fade. The ones that embrace depth, nuance, and real user needs will thrive.

Which side will you be on?