Humans and AI: Finding the Balance

Aseem Asthana with Greta Remington, Kuldip Hillyer, and Archit Anand

Amolino AI gathered revenue operations experts to discuss how AI changes professional services. Here's what they shared.

The YouTube Experiment That Cut Support Costs

Kuldeep Hillyer from Kugamon found unexpected success through YouTube videos. His team created videos for their Salesforce CPQ documentation and saw immediate results.

"We had a lot of documentation. I asked someone on our customer success team to put videos behind the documentation and put it on YouTube. Our support case count went to zero because people figured it out themselves. Then our prospects viewed the videos, so our sales cycle dropped," explained Kuldeep.

His team now integrates videos directly into their app interface. "Now if you type 'create a quote from an opportunity' on YouTube, a Kugamon video is likely in the top three results." Creating Real-World Moments Greta Remington demonstrated the power of in-person events. For an artisan bakery platform, her team hosted "cake picnics" that generated over 1,000 responses and sold 150 tickets in 48 hours.

"If you have long conversion lags, create a moment," Greta advised. "The longer it goes between awareness and conversion, that drops off."

AI in Revenue Operations: Current Limitations Kuldeep, who has spent 16 years running Kugamon, highlighted where AI falls short in revenue operations.

"I've been looking at how to bring AI to make operations more efficient. What I'm finding is that at a certain size, there isn't an AI app to make life better. There's a lot of AI tools for getting prospects and data enrichment. But when a sales rep needs to create a quote, there's no AI app saying, 'Create a quote that's profitable but competitive.' That doesn't exist yet."

He added, "There is automation - sending reminders, renewals, past due notices. But having AI deal with math that you could just drop in doesn't exist yet." Commoditizing Cognitive Skills Vernon Keenan, enterprise AI analyst specializing in the Salesforce ecosystem, described how AI is democratizing consulting expertise.

"Many skills within consulting can be commoditized. You can take skills like requirements analysis and other cognitive skills that came from consultants that used to be a rarefied quantity. You had to pay that McKinsey guy $2,000 an hour to hear his wisdom. What happens when you get that $250-an-hour guy with a chatbot that's as good as the McKinsey guy?"

Vernon added, "The royal nature of these consultancies is going to be attacked. They're fat targets." AI's Impact on Pricing Models Omar Youssef addressed how AI affects service pricing:

"Pricing is a big deal. When people talk about these trillion-dollar markets, they're assuming people will pay AI agents exactly what they paid humans. That's hogwash. If an AI agent does a human job, people expect the AI discount right there. You'll be lucky to capture 50% of that value with your new agent."

He sees this as positive: "There's going to be downward pressure. Hopefully that's part of the abundance equation we're after with AI. The idea is to make everything cheaper and more abundant so we can use it more. This is what people call the Jevons Paradox - once cost goes down, there's more utility." AI as the Junior Team Member Greta compared AI to junior employees:

"AI is like having a junior level employee do background research, prepare an initial quote, do about 80% of the work. You still need that director of sales to refine it, personalize it, and add intelligence on top."

Manjit Singh from Salesforce observed growing VC interest in AI-augmented service businesses:

"VC has always seen service businesses as 'throw more bodies and that's how the business grows.' But now they see that if powered by agents, you drive more productivity. Ten people who were able to do five projects can now do 20 projects."

The Content Paradox A recurring theme was how AI-generated content becomes less effective as it proliferates. Kuldeep noted:

"Once AI started generating blog posts, nobody reads blogs. Once it hit LinkedIn posts, nobody read LinkedIn. For emails, there's automation, so nobody reads your emails."

Service as Software: The New Paradigm Vernon introduced the concept of "service as software" - the commoditization of labor that operates software.

"If you look at the Salesforce DevOps sector, all those companies together make about $200 million a year. But the consulting and labor market that could be targeted by AI is around $3.6 billion. Salesforce is a $38 billion company, but consider all the services and labor that goes with that - it's much larger."

Omar expanded on this concept:

"The entrepreneurial excitement is that consultants can bring new tooling and capabilities to their skill set. This would enable them to charge higher rates. It's about how much of this commoditized knowledge you can bring into your practice to create value for customers. People are still willing to pay for value."

Client Expectations and Pricing Pressure Manjit shared that clients already expect lower costs:

"I interviewed 15-20 consulting firm leaders recently. One theme was that customers are asking 'With all these AI tools, what are you doing to bring our bill rates lower? How can we go from time-and-materials to more fixed or outcome-based contracts?'"

Vernon predicted "heavy commoditization of cognitive skills" but saw opportunity:

"The value will be in delivering high-quality advice to new customers who before couldn't afford McKinsey but still need that level of advice to run their $50 million manufacturing operation."

The Next Generation of Consulting Both experts predicted dramatic shifts in how consulting firms operate.

"I fully back the prediction that an AI-first consulting firm with 10 employees will produce better outputs and deliver more projects than a traditional firm with 100 people," said Vernon.

He added:

"The typical consultant will be the ex-Accenture guy in his early 30s saying 'I can do much better with these tools than these guys are doing.' He'll go back to the customer with his 10-person team and say 'I've got a 12-month plan to replace that stupid Accenture three-year plan.'"

Omar noted that firms must adapt or be replaced:

"It's the classic innovator's dilemma to the nth degree. These consulting companies are getting feedback from customers now. They'll either figure out ways to do it to themselves, or other people will do it to them."

Making AI Reliable for Enterprise Manjit discussed the challenges of making AI dependable: