C-Suite Perspectives On AI: Kylie Fuentes Of Zilliant On Where to Use AI and Where to Rely Only on Humans
An Interview With Kieran Powell
Learn from others. This is a rapidly changing moment in time, and we are at such an early point in this infection that nobody has all the answers. I think this is a time for technologists to really look around at what is happening, not just with their competitors and customers but also across industries and other technology vendors. There are so many opportunities to learn from each other that we need to be proactive about exchanging ideas.
As artificial intelligence (AI) continues to advance and integrate into various aspects of business, decision-makers at the highest levels face the complex task of determining where AI can be most effectively utilized and where the human touch remains irreplaceable. This series seeks to explore the nuanced decisions made by C-Suite executives regarding the implementation of AI in their operations. As part of this series, we had the pleasure of interviewing Kylie Fuentes, Chief Product and Marketing Officer at Zilliant.
Kylie Fuentes is an internationally experienced, commercially minded product executive with a specialization in Revenue, Monetization and Commerce technologies. Currently, Fuentes serves as Chief Product and Marketing Officer at Zilliant, which powers intelligent commerce for B2B companies by connecting their commercial strategies with effective execution. Zilliant’s industry-leading price optimization and management and sales guidance software enables profitable growth by transforming the way its customers use data to price and sell in traditional and digital channels.
Thank you so much for your time! I know that you are a very busy person. Our readers would love to “get to know you” a bit better. Can you tell us a bit about your ‘backstory’ and how you got started?
I’ve worked in quite a few industries over the course of my career. Early on, I was in telecommunications, where I worked on the launch of a subscription video streaming product designed for early smartphones. Digital and hybrid selling models were also just starting to take off. I then moved on to work at companies such as Westfield and Rodan + Fields, where I helped drive their consumer eCommerce and retail technology portfolio, with the goal of finding better ways to connect retailers to their customers using digital technologies. The experience I gained in commerce and revenue management is what ultimately led to my role at Salesforce as Senior Vice President of Product for Revenue Cloud. Now, I serve as Chief Product and Marketing Officer at Zilliant, the industry leader in pricing lifecycle management.
It has been said that our mistakes can be our greatest teachers. Can you share a story about the funniest mistake you made when you were first starting? Can you tell us what lesson you learned from that?
I wouldn’t have called this a mistake at the time, but the benefit of hindsight allows me to look back and see when I made the wrong choice. When I worked at Telstra earlier in my career, one of the first initiatives I worked on was around trying to figure out new ways to get people to use more data. Mobile phones were exploding in popularity at the time, but this was still pre-YouTube and all the other streaming platforms that came after. We came up with the idea of streaming cable TV through mobile phones, but at the time, we assumed that nobody would ever want to watch full episodes of content on their phones. We built our entire product strategy around that assumption and introduced these condensed three-to-four-minute highlight reels of full TV episodes that people could watch on their cell phones.
Looking back now, that clearly wasn’t the right assumption. I learned two lessons from that experience. First, when experimenting with any new technology, it’s important to be able to modify and adjust the product based on how it’s received. The second lesson is the importance of measuring. You can assume whatever you want, but the market will let you know pretty quickly whether you’ve solved the problem or you don’t have the right market fit yet.
Are you working on any exciting new projects now? How do you think that will help people?
We believe pricing is key to driving profitable growth because it impacts brand position, financials, buying, and selling. Pricing is a really strategic business function, but many companies lack the tools to identify the right pricing strategy and, more importantly, execute that throughout their business. So, at Zilliant, we’re out to solve that problem.
We recently added a CPQ solution to Zilliant’s product portfolio. A company can devise the best pricing strategy in the world, but if it can’t get it into the hands of the deal negotiators and in front of the end customer, it’s all for naught. So, we want to ensure that companies have the tools they need to make that happen.
We’re also working on some projects and product capabilities geared toward helping businesses automate internal processes around pricing. We see a lot of companies still using manual pricing processes and working out of Excel spreadsheets. We’re working on creating tools that enable companies to automate the pricing-related tasks that are repetitive and error-prone, so teams can focus on the strategic decision-making that more directly impacts their business goals.
Thank you for that. Let’s now shift to the central focus of our discussion. In your experience, what have been the most challenging aspects of integrating AI into your business operations, and how have you balanced these with the need to preserve human-centric roles?
We came from a world where AI adoption was challenging because people formed a view that said you needed these very clean, rich, and robust data sets to leverage it. Historically, that’s been a barrier because companies often didn’t have the tools needed to manage data efficiently. But with the latest advancements in AI, that’s less of a problem or hurdle because AI can work with a lot of unstructured data sets.
Now we’re in an age where many companies want to start using AI but don’t necessarily know where to start. It’s still somewhat of an uncharted technology, so we’re all still learning at the same time, we’re in the very early days. In my view, technology is a tool to solve a human problem. Ultimately, the biggest challenge in driving adoption is going to be finding the right use cases where AI can be embedded into the core flow of business processes, remove friction in end-user experiences and deliver an exponentially better outcome than what was possible before.
The other area that we’re starting to see emerge as a barrier is trust. In B2B technology in particular, customers are relying on technology to help them run their business operations. Trusting the outputs of AI-driven recommendations without understanding how they’re generated is a real concern. In the long run, as the technology and comfort levels improve, this will likely go away. But for now, I think B2B tech companies will also have to think about how they can build in features to allow end users to validate or override AI recommendations as a way of overcoming these barriers to adoption.
I’m an optimist at heart, and I believe that in the B2B space, AI is something that is going to change how we deliver solutions, but ultimately, it will be an up-leveling of human productivity and job satisfaction. On a macro level, with new technologies, there is new job creation and new industry creation. And on a micro level, individuals and businesses are looking for AI technology to help free up time from repetitive, task oriented work so they can spend more time doing strategic work that creates market advantage.
Can you share a specific instance where AI initially seemed like the optimal solution but ultimately proved less effective than human intervention? What did this experience teach you about the limitations of AI in your field?
Well, it’s not that AI isn’t the optimal solution, but I think it’s a recognition that it’s not the entire solution. As I mentioned above, one of the biggest concerns is around trusting the recommendations or outputs from something generated via AI. For example, when you’re talking about things like deal negotiation with a customer or prospect, businesses want to feel confident in what they’re offering. There aren’t too many businesses that are willing to go fully automatic on AI-generated proposals without some kind of human control. But it’s about doing that at the right point in the process so that the human touch is scalable, and catches the biggest outliers and risks, but that you don’t trade off on the efficiencies you achieve as a result of automation.
How do you navigate the ethical implications of implementing AI in your company, especially concerning potential job displacement and ensuring ethical AI usage?
I think in the long run, the outcome of AI acceleration will be a net positive for society and for the economy. History shows us that the introduction of any new disruptive technology brings about new industries and new jobs. And in the B2B space, I think that AI, in particular, has the ability to help workers do less of the stuff they generally don’t enjoy, and do more of what they do. Imagine a world where you don’t have to do data entry across six or seven applications, answer repetitive emails or messages, or build spreadsheet upon spreadsheet to analyze data… instead, imagine a world where you can spend more time brainstorming with colleagues, building better relationships with your customers, making better decisions or planning for the future. I think that’s where AI can take corporate life in the next 10 years, and I think that’s pretty exciting to be a part of.
Could you describe a successful instance in your company where AI and human skills were synergistically combined to achieve a result that neither could have accomplished alone?
Zilliant was a very early adopter of using machine learning for price optimization, which is a beautiful example of this. We leverage the expertise of our pricing data scientists, who truly understand the elements of what makes a great pricing strategy and combine that with technology to deliver that expertise in an automated, repeatable way. Price optimization software is extremely sophisticated, which is why every customer has access to our subject matter experts who can help them shape the software’s algorithms and models based on their unique business needs.
Based on your experience and success, what are the “5 Things To Keep in Mind When Deciding Where to Use AI and Where to Rely Only on Humans, and Why?” How have these 5 things impacted your work or your career?
- Understand the problem you’re trying to solve, then identify the right tool to solve it. As the late Harvard Business School marketing professor Theodore Levitt famously said, “People don’t want a quarter-inch drill. They want a quarter-inch hole!” This JTBD (Jobs to Be Done) foundation has been a critical career pillar for me, I want to make sure I am relentless on the problem, but flexible on the solution. If you know the problem well, you can try different ways of solving it and validate it with your customers as you go.
- Unpack the motivations of your customers. More often than not, if a problem is worth solving, there’s usually some kind of an alternative out there. Even if it’s pen and paper or an Excel spreadsheet. If using that Excel spreadsheet is easier or more reliable than the alternative you put out, human behavior will follow the path of least resistance. Knowing what motivates your customers, what they want and what they’re afraid of will help you find the right threshold of where to fully automate, where to partially and where potentially not to.
- Consider the impact and scale. If the problem you’re looking to solve would have critical consequences for the business if not executed properly, then I’d be really thinking about ensuring there are adequate human controls and redundancies. With any new technology, there’s some level of risk for early adopters — and you don’t want to apply that risk to something that could have colossal consequences.
- Identify where humans play a critical role in the process, then help them exponentially do it better. For example, setting strategy is as much art as it is science. There isn’t a single winning mathematical formula to win, and at the end of the day, it’s a lot of judgment calls. So then we might find ways to lean into the idea that humans will need to drive the ‘art’ part of the decision-making process, but find ways to help them do that job better by automating how they obtain insights, how they model scenarios and how they can assess risk etc.
- Learn from others. This is a rapidly changing moment in time, and we are at such an early point in this infection that nobody has all the answers. I think this is a time for technologists to really look around at what is happening, not just with their competitors and customers but also across industries and other technology vendors. There are so many opportunities to learn from each other that we need to be proactive about exchanging ideas.
Looking towards the future, in which areas of your business do you foresee AI making the most significant impact, and conversely, in which areas do you believe a human touch will remain indispensable?
My vision is to help customers unlock the full value of a well-developed pricing strategy. When it comes to AI, there are so many places where it can make a material impact.
For example, to build a great pricing strategy, you need to analyze and interpret a lot of different inputs, understand commercial implications, and of course, think about the potential consequences in the market. For most businesses, this is a fairly manual process that takes way too long and means businesses can’t be as agile as they’d like in terms of adjusting or pivoting as the needs of the business change. This is a huge area of opportunity for AI to add value to the process, but given the science versus art nature of this business challenge, I think it’s really going to be about upleveling the quality and speed of human decision making.
Other areas where businesses have to have valuable resources doing lots of repetitive tasks like data entry into disparate applications, building reports and dashboards, etc., represent opportunities for things like RPA and ML to create and then optimize automated processes. This allows teams to free up valuable human time to engage in the work they really want to be doing.
You are a person of great influence. If you could start a movement that would bring the most amount of good to the most amount of people, what would that be? You never know what your idea can trigger. 🙂
I often wonder how these new technologies, particularly AI and automation, can be used as a forceful, powerful lever for education. Everyone learns differently, yet curriculums are very standardized. There aren’t enough teachers to provide the personalized interactions and tutoring needed to meet every student’s individual needs. I believe there’s an incredible opportunity for technology to be applied here to rethink the way education is approached and help solve some of these gaps.
How can our readers further follow your work online?
LinkedIn: https://www.linkedin.com/in/kyliefuentes
This was very inspiring. Thank you so much for joining us!
About The Interviewer: Kieran Powell is the EVP of Channel V Media a New York City Public Relations agency with a global network of agency partners in over 30 countries. Kieran has advised more than 150 companies in the Technology, B2B, Retail and Financial sectors. Prior to taking over business operations at Channel V Media, Kieran held roles at Merrill Lynch, PwC and Ernst & Young. Get in touch with Kieran to discuss how marketing and public relations can be leveraged to achieve concrete business goals.
C-Suite Perspectives On AI: Kylie Fuentes Of Zilliant On Where to Use AI and Where to Rely Only on… was originally published in Authority Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.