Jillian OrRico, VP of Strategic Growth at QualityAI chats about today’s AI powered salestech and sales ecosystem in this Q&A:
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How will sales teams need to evolve and adapt in the age of AI?
For decades, sales was built on information asymmetry. The seller knew something the buyer didn’t. Today, AI is rapidly eliminating that advantage.
Customers can research products, compare competitors, analyze market trends, generate business cases, and educate themselves long before they ever speak to a salesperson.
I believe the role of sales is shifting from providing information to providing interpretation. The best sales teams won’t win because they know more. They’ll win because they can help customers make sense of an increasingly overwhelming amount of information and translate it into action.
We’re already seeing this with many enterprise organizations adopting AI. The technology itself is rarely the biggest challenge. The harder questions are around prioritization, governance, adoption, risk, organizational alignment, and measuring business value. Those are not product questions. They’re business questions.
As AI takes over more administrative and analytical work, sales professionals will need to become stronger strategists, communicators, and advisors. Their value will come from helping customers navigate complexity, align stakeholders, and make confident decisions.
In many ways, AI will make information cheaper. Human judgment, trust, and the ability to create clarity will become more valuable.
Which AI-powered sales technology capabilities should sales professionals prioritize learning and developing expertise in?
I don’t think sales professionals need to spend too much time worrying about whether they’re using the “right” AI tools. I’d focus more on how AI can help them think better, learn faster, and create more value.
The tools are changing so quickly that today’s favorite platform may be irrelevant a year from now – or even a week from now. What matters is developing a habit of curiosity and experimentation.
A few months ago, my family sat down together to learn how to use OpenAI’s Codex. We weren’t trying to become developers overnight. We were simply curious. What is it? How does it work? What problems can it solve?
The next day, I watched my 13-year-old use it to build a science quiz to help her prepare for an upcoming test. She wasn’t focused on learning a tool. She was focused on solving a problem and using an AI tool to do it.
I think the same principle applies to sales professionals. Use AI to research faster, understand customers more deeply, identify patterns, translate technical concepts into business language, challenge your own assumptions, and prepare more effectively for conversations.
The competitive advantage won’t be mastering a particular AI platform. It’s developing the ability to continuously learn, adapt, and apply new technology to real-world problems. The people who will benefit most from AI aren’t necessarily the technical experts. They’re the ones who remain curious enough to keep experimenting.
How can SaaS sales leaders stay ahead of emerging AI and technology trends to remain competitive in a crowded market?
I think most leaders are asking the wrong question.
The challenge isn’t keeping up with AI – it’s understanding why so much AI never makes it into production.
Over the last few years, I’ve sat in countless conversations where organizations were excited about what AI could do. Far fewer conversations focused on what it would actually take to operationalize it.
Governance. Adoption. Quality. Trust. Compliance. Change management. That’s where most initiatives stall.
The leaders who stay ahead won’t necessarily be the ones who know the most about the latest models. They’ll be the ones who understand how technology moves from a demo to a business process and ultimately to measurable business outcomes.
Every company can tell you what AI is capable of. Far fewer can tell you how they’re going to scale it responsibly.
In fact, that’s one of the most common conversations we’re having with many of our largest clients today. The question is no longer, “Can we build it?” It’s, “How do we operationalize it, govern it, measure it, and trust it at scale?”
And to be fair, most organizations are still figuring that out. That’s where the real competitive advantage is emerging.
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Where do the most common communication gaps occur throughout today’s technology-driven sales cycle?
The most common communication gap occurs when the conversation becomes centered on technology instead of outcomes.
Technology buyers today are inundated with information. They can research products, compare capabilities, watch demos, and evaluate features long before they ever engage with a sales team.
The challenge isn’t access to information – but rather understanding how a technology decision will impact the business.
Too often, vendors are talking about features while buyers are trying to understand impact.
How will this help me achieve my goals? How will this affect my team? How will success be measured? What happens if this initiative doesn’t deliver the results we expect?
A vendor is talking about reducing costs by 30%. The buyer is wondering whether they’ll be able to hit the goals their CEO gave them this quarter.
The sales teams that will win in the age of AI are the ones that continue to lead with solution and value-based selling. They understand not only what the technology does, but why the customer is exploring the technology in the first place and tie that back at every turn.
Every technology conversation is ultimately a human conversation. Behind every AI initiative, automation project, or digital transformation effort is a leader trying to solve a business problem, improve an outcome, reduce risk, or achieve a personal objective they are accountable for.
The best sellers never lose sight of that. They connect technology to business value and business value to the people responsible for delivering it.
What skills and experiences should sellers strengthen to stand out and build stronger customer relationships in an AI-driven environment?
Curiosity.
Not product knowledge. Not prompt engineering. Not even technical expertise.
Curiosity.
A senior technology executive teased me once, “You love interrogating people.” I laughed and said, “No. I’m genuinely curious about what’s going on with them.”
That mindset was shaped by some of the best advice I received early in my career:Â stop trying to be the smartest person in the room.
The best salespeople I’ve worked with aren’t the ones who have all the answers. They’re the people who are endlessly curious about how a customer’s business works, how decisions get made, and what’s really driving the conversation.
They’re asking questions like:
“Why is this important right now?”
“What changed?”
“If we solve this problem, what does that unlock for the business?”
“What happens if nothing changes?”
“What’s the concern nobody has brought up yet?”
Those questions often tell you more than any requirements document ever will.
AI can help us gather information faster than ever before. It can summarize meetings, analyze data, and surface patterns. What it can’t do is replace genuine human curiosity.
I’ve found that most customers will tell you exactly what’s standing in the way of a decision if you stop trying to sell them and start trying to understand them.
The strongest customer relationships are built when people feel understood, not analyzed. When they believe you’re interested in their success, not just your solution.
Technology may continue to change how we sell. Curiosity is what will continue to determine who customers trust.
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About QualityAI
QualityAI is a major AI-first quality engineering and assurance firm (formerly known as Qualitest). It helps enterprises in highly regulated industries—such as healthcare, finance, and manufacturing—safely embed AI into their business systems. Additionally, it offers a mobile digital auditing platform for plant floors.
About Jillian
Jillian OrRico is VP of Strategic Growth at QualityAI. She advises enterprise leaders on AI adoption, quality engineering, and large-scale transformation, with a particular focus on the human, operational, and business challenges that determine whether innovation succeeds or stalls.
