John Bruno, VP Strategy At PROS, On Engineering Excellence: Balancing Speed With Resilience In Digital Commerce

John Bruno:  The traditional “sales rep” will not disappear but rather evolve alongside digital selling itself. New AI-powered technologies are already transforming sales roles, making salespeople more efficient, data-driven, and strategic. As they use AI for initial revenue tasks such as pricing, designing offers, and segmenting customers, sales reps are shifting from performing basic, time-intensive responsibilities to analyzing insights, creating tailored solutions, and building human relationships. Instead of getting bogged down in tedious, repetitive work, sales teams can hand that off to AI and focus on what they're actually good at (coming up with thoughtful strategies and connecting with people). The workforce will not shrink, but instead get smarter, and sellers will work in the service of buyers from establishing the commercial relationship through enabling them to self-serve where appropriate.

John Bruno: Before a buyer has their first conversation with a sales rep, they have more than likely conducted significant research, sifting through the large volume of information readily available online. Enterprises must rethink value creation by focusing on building trust and providing insights that buyers cannot easily acquire on their own. Furthermore, the role of AI for the buyer is going to help the seller start from where the buyer currently is, rather than catching up. If the buyer has AI tools in their shopping experience (e.g., an agent on a commerce site), the brand providing that experience has more insight into the intent of the buyer. Now sellers can pick up on that intent before even talking to the buyer. AI will allow sellers to fast-track their understanding of buyer needs and help them live up to buyer expectations, even during the first interaction. Research on AI in revenue management shows that early-stage activities, such as pricing, offer design, and customer segmentation, are where AI can help shape deals before buyers engage. Rather than simply delivering information, sellers need to understand what buyers already know and use technology and data to provide tailored recommendations that guide them toward greater confidence in their decisions. In this landscape, sellers create value by helping buyers make better, more informed choices.

John Bruno:  To me, engineering excellence means the ability to build systems that are both fast and resilient, enabling organizations to act quickly without creating unintended consequences. In digital commerce, this means designing AI-powered pricing, contract, and subscription systems that work together across the revenue lifecycle. Focusing on one area in isolation, such as pricing, might deliver short-term results but can misalign outcomes with broader business objectives. True excellence comes from integrating multiple levers, anticipating how changes in one area affect the others, and ensuring systems are adaptable for the future. It is about achieving speed and efficiency while maintaining stability, reliability, and long-term business impact.

John Bruno: CTOs and engineering leaders are no longer there to simply manage systems and keep the lights on. They are increasingly expected to connect the dots between technical decisions and strategic business outcomes to boards and other executives in a way that a nontechnical audience can understand. Boards need to understand how technology investments drive efficiency, growth, and smarter decision-making. For example, instead of focusing on individual tools or processes, they need to paint a bigger picture of how integrated AI systems that work across pricing, subscriptions, and customer agreements are creating competitive advantages and sustainable growth. Ultimately, every technological decision made should be evaluated in terms of how it will position the company to win in the market.

John Bruno:  Short-term revenue optimization and long-term customer trust can sometimes seem at odds; however, they can be mutually reinforced. The emerging set of business processes known as Revenue Lifecycle Management (RLM) reconciles the two through a holistic view of the customer journey, aligning top-line revenue growth and bottom-line efficiency. By integrating marketing, sales, and transactional processes, companies can anticipate customer needs, create dynamic offers, and continuously refine strategies in real-time so they can maximize both revenue quality and customer satisfaction. AI-powered solutions play a key role in this balance, enabling dynamic pricing and cross-sell and upsell recommendations while delivering a consistent, fair, and customer-focused experience. With a dual focus on customer lifetime value and optimizing each transaction, businesses can deliver superior experiences and cultivate relationships.

John Bruno:  By 2030, the gap between digital laggards and leaders will come down to an integration of culture and leadership mindsets rather than just technology adoption. AI-powered pricing technology and holistic revenue lifecycle tools will continue to be increasingly accessible. The main difference will be the extent to which companies integrate these technologies into their workflows and how comfortable both leaders and individual contributors are in using them. Culture is key, as businesses must implement a design-experiment-scale mentality, which AI tools enable at breakneck speeds. This allows for a low-risk, high-reward environment, making rapid experimentation and learning accessible. Those who foster a data-driven, collaborative culture with adequate training are willing to challenge traditional silos and harness this mindset will be successful in leveraging AI and outperform others. Ultimately, the ability to integrate technology, strategy, and culture to consistently innovate and maintain long-term customer trust defines digital leaders.