What if mastering artificial intelligence didn't require becoming a technical expert?
The AI revolution in B2B isn't unfolding quite like anyone predicted. With billion-dollar investments and constant innovation, it looked like it would be a complex battlefield. Yet, the real revelation? Mastering AI might be simpler than we thought.
"Business leaders only need to understand 30% of AI technology to leverage it effectively," says Tim Sanders, VP of research and insights at G2. In my latest conversation with Tim, he reveals why many companies are getting AI transformation wrong, and how a simple shift in perspective could be worth more than millions in tech investments.
His insights reveal why the future of B2B success lies not in the technology itself but in how organizations adapt and evolve alongside it.
This interview is part of G2’s Q&A series. For more content like this, subscribe to G2 Tea, a newsletter with SaaS-y news and entertainment.
To watch the full interview, check out the video below:
We're seeing AI capabilities expand exponentially, but organizational readiness often seems to lag. What are the most critical yet overlooked components of building true AI readiness in an enterprise?
Leaders need to understand two critical aspects of AI implementation in their organizations.
First, and this essential: create a sense of urgency around developing an understanding of AI within your company culture — specifically, how AI works and connects to existing business challenges.
There's a book called “The Technology Fallacy” that was written several years ago, but it's true even today. It says that organizations that clearly understood how disruptive technology worked and could connect it to their business were significantly more likely to achieve digital transformation than those that didn't. The key insight?
“Success depends not on the technology itself, but on your people's understanding and readiness for change.”
Tim Sanders
VP of Research Insights at G2
Second, organizations must develop the ability to reframe business challenges as prediction problems. In the UK, about a decade ago, getting a London cab during rush hour in Piccadilly Square was extremely difficult. Transportation leaders viewed this as a logistics problem. They couldn't get enough qualified drivers because the certification process (known as the Knowledge) required years of training to learn London's complex street system.
And then came an AI application, which changed everything. So now, to be a driver, you didn't have to go to school for years; you just had to have a car and a good sense of judgment about how to drive a car.
They increased the number of drivers in the last decade by over 500% with the launch of Uber.
What did we learn from that? It was never a talent shortage; it was always a prediction problem. This insight applies whether you're using established machine learning (ML) solutions or cutting-edge large language models (LLM). The key is to examine your operating plan, identify real challenges, and ask: could prediction power — whether through ML or generative AI — help solve this problem?
Once you pick that up, you've started to achieve what Dr. Tsedal Neeley calls the 30% rule. She wrote a great book on this called “The Digital Mindset.” She says that business leaders don't need to understand 100% of the technology to leverage it effectively — they need about 30% understanding.
This 30% comprises knowing how the technology works and how to connect it to business challenges. The common mistake today is falling in love with technology solutions first and then searching for problems they might solve. Instead, start with the business challenge and then identify the appropriate technological solution.
There's a lot of discussion about AI replacing jobs but less about how it's creating new roles and transforming existing ones. How do you see AI reshaping the B2B workforce, particularly in areas like sales, marketing, and customer success?
AI doesn't really replace jobs. Instead, it replaces specific tasks within jobs. Currently, AI and automation agents have a narrow focus. While they can't manage complex processes like humans can, they excel at handling repetitive tasks. The key difference between traditional automation and AI agents is that agents can be more dynamic, handling unpredictable situations rather than following strict programming.
The first thing is that we've had automation for a long time. What we're seeing with AI is that a lot more tasks can be automated now. While this might eliminate some roles, it simultaneously creates higher-paying opportunities within the same companies — jobs focused on AI development, implementation, vendor selection, and system integration.
The second thing we're going to see is that AI is going to enable more people to start their own companies like we've never seen before. I was just reading an article the other day that we're going to see billion-dollar companies with two employees and many agents. That opportunity didn't exist before.
Earlier, you'd have to go to work for a big company for 40 years and watch the people in the C-suite make millions of dollars and sit on the sidelines because you didn't have the money to start a company. That game is going to change.
Consider what I call the Uber paradox. When Uber came out, a lot of people were thought taxi drivers are going to lose their jobs. When actually, in the long run, at least 500% more jobs were created by the Uber phenomenon. A lot of the people who drive Ubers today didn't have a job. Some of them were retired and scraping to get by, and technology came along and created jobs for them.
This pattern isn't new. Take automated teller machines (ATM), for example. When they were introduced, many feared bank tellers would become obsolete. Instead, tellers evolved from counting money to providing financial advice and earning higher salaries. Adjusting for population growth, there are now three times more tellers than before ATMs because they're performing higher-value tasks that generate more revenue for banks.
I understand the fear of all of this, but the reality is human beings are not happy doing the same thing 100 times a day that a machine can do. Human beings are happy when they're doing what you and I are doing right now: thinking, problem-solving, and working on something from a critical lens.
“I think it's a fear that's been around since the beginning of history when technology came along. But the paradox of it all is it creates more opportunity. ”
Tim Sanders
VP of Research Insights at G2
However, there's one important caveat. While technology ultimately creates more opportunities, there can be short-term disruptions. For instance, AI agents might significantly reduce customer service roles in the near term, and it could take three to five years or more for new opportunities to emerge.
Governments need to develop strategies to manage this transition period, supporting workers as they adapt to new roles. That is a valid concern, but we should still pursue it for the sake of humanity.
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Many organizations are struggling with “AI FOMO” while simultaneously dealing with AI skepticism among stakeholders. How can business leaders balance aggressive AI adoption with thoughtful implementation and risk management?
The best approach to leveraging AI opportunities is straightforward: start by examining your most significant business challenges and test AI solutions specifically designed to address them. Scale your investment based on proven results.
So I tell people, for example, if you've been spending a lot of money on Google AdWords, you might want to take a little bit of that money and start investing it to be effective with LLMs and scale that up as it begins to work. So start as slow as you can, but have a sense of urgency not to wait too long because AI has an exponential impact.
It’s like a popular Chinese proverb where they say, "The best time to plant a tree was 20 years ago. The next best time is today." This perfectly captures the current AI opportunity. While earlier adoption would have been ideal, starting now is better than waiting. It's a definite thing you have to balance.
“Remember: AI itself isn't coming for your job, but professionals who effectively utilize AI are.”
Tim Sanders
VP of Research Insights at G2
Looking ahead to three to five years, which AI applications or use cases do you believe will become absolutely essential for B2B companies to remain competitive?
Agentic AI will become the fundamental ingredient for successful businesses in the future. The reason is simple: it will dramatically expand your workforce's capacity to tackle critical business challenges. If you're not exploring AI agents for customer service, sales, marketing, and software development, you're essentially giving your market advantage to competitors who are.
These agents will continuously improve in reliability over time. Think of it as a compound advantage — the sooner you begin integrating AI agents into your operations, the more refined your understanding and implementation will become, creating an increasingly wider gap between you and late adopters. So, the time to get started is now!
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Edited by Supanna Das
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