Dr. Rich Sonnenblick, Planview’s Chief Data Scientist, holds years of experience working with some of the largest pharmaceutical and life sciences companies in the world. Through this in-depth study and application, he has successfully formulated insightful prioritization and portfolio review processes, scoring systems, and financial valuation and forecasting methods for enhancing both product forecasting and portfolio analysis. Dr. Sonnenblick holds a Ph.D. and MS from Carnegie Mellon University in Engineering and Public Policy and a BA in Physics from the University of California, Santa Cruz.
Planview’s Platform for Connected Work is designed to enhance time-to-market and predictability, improve efficiency to maximize capacity, and support the delivery of strategic initiatives aimed at achieving optimal business outcomes.
You’ve had an extensive career transitioning from management consulting to leading data science initiatives. What inspired you to make this shift, and how has your journey shaped your approach to leveraging AI in business today?
Management consulting provided me with a broad view of business inefficiencies and untapped opportunities, where there is a distinct gap between strategic advice and actionable insights. Data science bridges that gap, turning raw data into strategic assets that have the power to inform decision-making in real-time. My journey has taught me to view AI as an enhancer that can refine processes, accelerate decision-making, and unlock creativity in ways that amplify human expertise.
At Planview, you’ve spearheaded the integration of advanced AI solutions across various business functions. Could you share how your role as Chief Data Scientist has influenced the company’s AI strategy and the biggest challenges you've encountered along the way?
At Planview, AI is embedded in our platform as a tool to unlock insights and improve decision-making. I’ve focused on using AI to optimize resource management, project planning, and operational efficiency. Our Copilot AI assistant provides on-the-job training for users at all skill levels, automates frequent time-consuming tasks like report generation, and leverages best-practices to suggest productive courses of action, empowering teams to swiftly make informed decisions.
How can AI help companies identify inefficiencies within teams and improve resource allocation?
AI excels at identifying patterns in data that are too complex to be quickly recognized by humans. It can highlight underutilized resources, identify bottlenecks, and forecast workload imbalances. For example, by analyzing portfolio objectives, project timelines and team performance metrics, AI can suggest reassigning tasks or reallocating resources across portfolios to create maximum impact without adding additional resources.
What are some common inefficiencies in resource management that AI is particularly effective at addressing?
AI is particularly adept at highlighting off-strategy and low-performing initiatives, and we’ve built these critical skills into Planview Copilot. As Copilot evolves it is better able to highlight and suggest mitigation measures. It can also flag waste in processes, such as redundant tasks or excessive handoffs, and suggest optimizations.
Why is waste a significant challenge for software development teams, and in what ways can AI reduce it?
Waste in software development often stems from inefficiencies like poor prioritization, excessive debugging, or misaligned team efforts. AI can reduce waste by acting as a coding assistant, automating repetitive tasks, and offering predictive insights into project timelines and potential risks. For example, it can analyze past projects to identify patterns that lead to delays, helping teams avoid those pitfalls.
Are there specific AI models or tools that are particularly well-suited to optimizing the software development lifecycle?
To optimize the software development lifecycle, we’re looking for enhanced efficiency and alignment. Planview Copilot in Viz identifies bottlenecks and impediments to flow velocity, and provides actionable insights tailored to an organization’s data. Teams can use plain English to interpret flow metrics, identify systemic delivery slowdowns, and receive detailed recommendations. This optimization is the key to growing productivity, ultimately streamlining delivery.
How do underlying data relationships create additional value when deploying AI as a work assistant?
By mapping relationships between data points—whether in project timelines, resource utilization, or team communication—AI can surface insights that go beyond the obvious. For example, linking sentiment trends in status updates to project outcomes can help managers anticipate roadblocks before the team surfaces them to management, providing ample time to make proactive adjustments.
What steps should smaller organizations take to adopt AI affordably without compromising on impact?
Smaller organizations should start with accessible generative AI tools that work as gateways to more sophisticated solutions. Tools that summarize documents, assist with marketing content, or assist with code generation are cost-effective ways for these organizations to begin their AI adoption without extensive investment. Starting with a horizontal AI offering that is applicable to a broad range of use-cases will be a better value than investing in specialized applications that bend generative AI to very specific jobs-to-be-done. This enables the organization to identify highest-impact use-cases specific to their organization rather than over-investing in multiple offerings.
What role does predictive analytics play in improving project outcomes?
Predictive analytics helps teams foresee potential roadblocks and outcomes based on historical data and current trends. AI agents can predict the likelihood of project delays or resource shortfalls, enabling product managers to adjust plans proactively. This foresight minimizes risk and maximizes efficiency, ultimately enabling organizations to meet their strategic goals more swiftly.
Looking ahead, how do you envision AI transforming business operations over the next decade, and what emerging AI trends are you most excited about for their potential impact on industries?
AI will continue to transform business operations in the coming decade. It will foster new roles, enhance predictive capabilities, and streamline innovation.
LLM-native developers, experts in integrating AI collaboration, will become the norm and will replace developers that do not adopt AI into their day-to-day tasks. Generative AI will continue to blur the lines with predictive AI, enriching algorithms with synthetic scenarios for strategic decision-making based on external and internal factors. In biotech, genAI will create intricate patient profiles to uncover new treatments, while in cybersecurity, AI will simulate novel threats for predictive models to counteract. Emerging trends like adaptive inference and smaller, more efficient AI models, will address computational challengers in the coming years. They will ensure faster, more targeted solutions.
From strategic planning to proactive security, AI's integration will enable businesses to pivot with agility, uncovering resilient strategies and operational excellence in an increasingly dynamic world.
Thank you for the great interview, readers who wish to learn more should visit Planview.
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