An Interview With Chad Silverstein
AI isn’t about novelty. It’s about clarity. It’s about giving leaders confidence in real time. It’s about helping businesses operate with foresight instead of hindsight.
As a part of this series, we had the pleasure to interview Branden Jenkins.
A seasoned SaaS executive, Branden Jenkins brings extensive experience in finance, vertical SaaS, and AI-driven innovation. He previously served as Chief Operating Officer at Medius and spent nearly a decade at Oracle NetSuite, where he played a key role in scaling the business from $250 million to billions in revenue as Global General Manager of Retail and, subsequently, Vice President of Strategy. Earlier in his career, he was CEO of Retail Anywhere, which was acquired by NetSuite in 2012.
Thank you so much for joining us in this interview series. Before we dive into our discussion, our readers would love to get to know you a bit better. Can you share the backstory about what brought you to your specific career path in AI?
I didn’t set out to work in AI specifically. My career has been shaped by operating through multiple waves of technology change and learning which shifts actually change outcomes.
I graduated high school early and began working young, initially in technical roles before moving into sales and leadership. Early in my career, I ran a mission-critical point-of-sale software company serving high-volume retailers, where uptime and accuracy were non‑negotiable. That experience grounded how I think about trust, accountability, and operational rigor.
Over time, I led that business through major transitions from on‑premise software to the cloud, and later spent nearly a decade at Oracle NetSuite, where I saw how automation, integrated data, and scale fundamentally reshape how companies operate.
AI feels like the next and most profound shift yet.
What genuinely excites me is AI’s ability to transform finance and operations from historical reporting functions into predictive, strategic engines. For decades, finance teams have spent enormous energy reconciling data, explaining what already happened, and manually stitching together systems. AI changes that dynamic. It has the potential to continuously analyze complex signals, surface risk earlier, highlight opportunities faster, and remove the cognitive overload that slows decision-making.
In finance especially, where precision and trust are everything, AI isn’t about novelty. It’s about clarity. It’s about giving leaders confidence in real time. It’s about helping businesses operate with foresight instead of hindsight.
I’ve spent my career helping organizations navigate technology transitions that improved efficiency or scale. AI is different. It doesn’t just optimize workflows. It augments human judgment. It gives teams leverage. And when applied thoughtfully, it can unlock new business models, new pricing strategies, and new levels of operational discipline.
That’s what draws me to this space. Not hype, but the real, durable impact AI can have on how businesses grow, adapt, and compete.
Can you share a specific example of how you or your organization used AI to solve a major business challenge?
One concrete challenge I’ve spent significant time on is improving customer retention by identifying risk earlier, not just explaining churn after it happens, but building a repeatable way to intervene before a customer starts to disengage.
Historically, we had plenty of data across product usage, billing history, support tickets, contract structures, and customer communications. The problem was that each signal lived in a separate system and belonged to a different team. Risk only became clear once a customer was already disengaging, which limited what anyone could realistically do.
By introducing AI-driven analysis to unify those signals, we were able to see how they interacted. Declining usage combined with increased support friction and certain contract constructs created risk patterns that no single team could detect on its own. In a SaaS environment where companies are under pressure to defend retention and prove ROI, that early visibility shifts the organization from reacting after the fact to intervening while outcomes are still influenceable.
The real impact wasn’t the model itself, it was the coordination it enabled. Customer Success prioritized differently. Sales engaged with better context. Product could distinguish between true gaps and adoption friction. AI didn’t replace judgment; it aligned it, gave teams a shared operating picture, and moved conversations from opinion to evidence and action.
You are a successful leader in the AI space. Which three character traits were most instrumental to your success?
Curiosity. I’ve always been driven to understand how systems work and where they break. That curiosity pushed me to embrace major technology shifts early, whether cloud platforms or AI, rather than defend legacy approaches.
Pragmatism. Throughout my career, I’ve focused on outcomes over novelty. Innovation only matters if it reduces friction, improves accuracy, or enables better decisions.
Empathy. Early in my career, a CFO once stopped me mid‑conversation during a system outage and reminded me that before explaining solutions, I needed to acknowledge the impact on her and her business. That lesson shaped how I lead.
What are some common misconceptions about using AI in business?
One common misconception is that AI is primarily about replacing people. In practice, the most valuable applications of AI is to augment human judgment rather than remove it. AI excels at processing large volumes of data, identifying patterns, and surfacing signals early, but it still relies on humans to set strategy, define guardrails, and make decisions.
Another misconception is that simply adding AI features automatically creates growth. In reality, “AI-enhanced” positioning can dilute differentiation if it isn’t tied to a clear workflow advantage. In the latest B2B Growth Report by Maxio, companies with AI at the core of their product grew faster (21%) than companies that layered AI on as an add-on (17%). AI delivers an edge when it’s intentional, not when it’s bolted on for marketing.
A third misconception is that AI can compensate for weak foundations. AI does not fix broken processes or poor data quality. In fact, it amplifies whatever it sits on top of. If financial systems, operational workflows, or data integrity are weak, AI will scale those weaknesses faster.
I address these misconceptions by reframing AI conversations around workflows and outcomes instead of features. When leaders focus on where decisions slow down, where risk hides, or where complexity overwhelms teams, AI becomes a practical tool rather than a source of fear or hype.
In your opinion, what is the most significant way AI can make a positive impact on businesses today?
The most significant impact AI can make today is moving businesses from reactive to predictive.
Most organizations still operate with a rearview-mirror mindset, relying on reports that explain what already happened. AI enables a different operating model. By continuously analyzing real-time signals across finance, product, and operations, AI helps leaders anticipate risk, spot opportunity, and act earlier.
That shift changes not just decision speed, but decision quality. Teams spend less time reconciling data and more time thinking strategically. Over time, that ability to see around corners becomes a meaningful competitive advantage.
Based on your experience and research, can you share five ways AI can solve complex business problems?
1. Connecting fragmented systems into a single operational view
In many organizations, finance, product, and customer data live in separate systems. AI can analyze those sources together so leaders can see, for example, how changes in product usage, billing behavior, and support volume interact without manually reconciling reports.
2. Turning finance into a forward-looking function
Instead of waiting for month-end closes to understand performance, AI can continuously analyze transactions and usage data to highlight emerging risks or opportunities, allowing teams to adjust forecasts while outcomes are still influenceable. In an environment where investors are scrutinizing efficiency and profitability more closely, that forward visibility becomes a competitive advantage.
3. Automating routine complexity with proper controls
Processes like revenue recognition, compliance checks, and collections involve large volumes of rules-based work. AI can handle this complexity consistently while preserving audit trails and human oversight, reducing error and manual effort.
4. Enabling dynamic pricing and monetization
As companies experiment with hybrid pricing models, AI can help evaluate how different structures impact revenue, retention, and customer behavior without breaking billing or reporting workflows. This is especially important as AI-native products introduce credits, usage-based billing, and outcome-based models that add complexity to traditional subscription structures.
5. Improving accuracy and trust at scale
As businesses grow, manual processes introduce risk. AI reduces reliance on spreadsheets and one-off analyses, helping ensure financial and operational decisions are based on consistent, trusted data. When boards and investors are demanding clearer paths to profitability, clean, investor-grade metrics are no longer optional.
How can smaller businesses or startups integrate AI effectively with limited budgets?
AI doesn’t have to be expensive to be effective. The key isn’t budget. It’s focus.
First, start with one high-friction workflow like manual reporting, customer onboarding or invoice processing. Identify where time is being lost and apply AI to create leverage, not novelty. Small wins compound quickly.
Second, integrate before you automate. If your systems don’t talk to each other, AI will only amplify the noise. Even lightweight integrations across your CRM, finance, and ops stack can unlock real value.
Third, use what already exists. Most modern SaaS platforms now include embedded AI capabilities. Turn those on before investing in custom builds.
For startups especially, the advantage isn’t scale. It’s agility. Move fast, test quickly, measure impact.
AI doesn’t reward the biggest spenders. It rewards the most intentional operators.
What advice would you give leaders hesitant to adopt AI?
Start with the business problem, not the technology.
AI does not require blind trust or large bets. Leaders can pilot small use cases, measure outcomes, and expand what works. When AI initiatives are tied to concrete goals like faster closes, better forecasts, or improved retention, uncertainty gives way to confidence.
Hesitation often comes from abstraction. Once leaders see AI improving real decisions in their organization, adoption becomes a logical next step rather than a leap of faith.
How will AI shape the business world over the next 5–10 years? Are there any trends or emerging innovations you’re particularly excited about?
Over the next 5–10 years, AI will continue to move from generating insights to executing actions.
AI-driven agents will increasingly handle routine operational decisions such as forecasting updates, compliance checks, and collections. Humans will remain responsible for strategy, judgment, and accountability, but they will operate with far better information and timing.
Importantly, AI shouldn’t replace people. It should elevate them. When we use AI to turn complexity into capability, we give individuals leverage. We reduce cognitive overload, surface clearer signals, and free teams to focus on higher-order thinking. If applied thoughtfully, the ripple effects across jobs, business growth, and access to opportunity could be profound.
We’re also likely to see shifts in software business models. As AI agents automate workflows, traditional seat-based pricing will evolve toward usage-based and outcome-based structures. That will increase complexity in billing, reporting, and revenue management, not reduce it.
The trend I’m most excited about is this shift from static analysis to systems of action. When AI is embedded directly into financial and operational workflows, businesses become smarter, more scalable, and better able to adapt. The organizations that succeed will be those that measure AI by durable, real-world outcomes.
How does AI influence relationships with customers, employees, and the broader community?
When implemented responsibly, AI strengthens relationships rather than weakens them.
Customers benefit from faster, more accurate experiences and fewer breakdowns in service. Employees benefit when AI removes repetitive work and reduces ambiguity, allowing them to focus on higher-value problem solving. Communities benefit when companies operate more efficiently and sustainably.
But progress is still powered by people, and relationships are at the heart of everything we do at Maxio. AI and automation will continue to transform how we build products, lead our business, and support our customers, but it can’t replicate the human connection that drives meaningful outcomes.
That’s why direct customer engagement is essential. Meeting with customers, whether on calls or in-person, is how you build trust, understand real challenges, and ensure innovation stays grounded in what matters.
Our approach to AI mirrors how our customers think about it. We don’t innovate for the sake of innovation. We focus on enhancing how companies operate while staying true to the foundation of strong, lasting relationships.
If you could start a movement through AI to do the most good, what would it be?
I would focus on using AI to support mental health and well-being in high-performing environments.
AI can help organizations identify burnout patterns, uneven workloads, and systemic pressure points early, before they become personal crises. Used thoughtfully, it can support healthier expectations around productivity and performance.
Leaders have a responsibility to use AI to support people, not push them harder. Creating environments where individuals can do great work without sacrificing their well-being would have a meaningful and lasting impact.
How can readers follow you online?
You can find me on LinkedIn at linkedin.com/in/brandenjenkins, where I share perspectives on leadership, finance, and technology. You can also learn more about our work at maxio.com.
Thank you for sharing these insights!
About The Interviewer: Chad Silverstein is a seasoned entrepreneur with 25+ years of experience as a Founder and CEO. While attending Ohio State University, he launched his first company, Choice Recovery, Inc., a nationally recognized healthcare collection agency — twice ranked the #1 workplace in Ohio. In 2013, he founded [re]start, helping thousands of people find meaningful career opportunities. After selling both companies, Chad shifted his focus to his true passion — leadership. Today, he coaches founders and CEOs at Built to Lead, advises Authority Magazine’s Thought Leader Incubator.
Branden Jenkins Of Maxio On How Artificial Intelligence Can Solve Business Problems was originally published in Authority Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.