As digital transformation becomes paramount for businesses across the globe, companies like Publicis Sapient stand at the forefront of innovation, helping organizations navigate the complexities of a rapidly changing technological landscape. With a focus on customer-centric experiences and agile methodologies, Publicis Sapient is driving change by embedding AI, data science, and engineering excellence into the core of its operations. Leading this charge is Rakesh Ravuri, Chief Technology Officer and Senior Vice President of Engineering, who brings a wealth of expertise in transforming digital strategies for Fortune 500 companies.
In this interview, we explore Rakesh’s perspective on the transformative power of AI in the software development process, the evolution of engineering in the digital age, and the critical strategies Publicis Sapient is deploying to empower its engineering teams for a future defined by AI advancements.
Could you begin by sharing your current responsibilities as CTO & SVP Engineering at Publicis Sapient and your vision for the company’s technological advancements?
As CTO and SVP of Engineering at Publicis Sapient, my role is to lead our global engineering teams, guiding them to not just manage but empower them to innovate and deliver solutions that address our clients’ needs on a global scale. At Publicis Sapient, we follow the SPEED framework—Strategy, Product, Engineering, Experience, and Data & AI—which shapes our processes and decisions. This approach keeps us agile and forward-thinking as we drive digital business transformation.
Our vision is to sustain our current momentum while aggressively pursuing growth across all aspects of SPEED. We’re evolving to be more AI-driven, ensuring that AI powered solutions are not only leading our efforts but are seamlessly integrated into every facet of our work, both for clients and internally. Our ultimate goal is to enable outcomes that are not only advanced but also profoundly human, designed to make a real impact on the lives of those we serve.
How do you envision AI transforming the software development industry in the coming years?
AI is advancing at a rapid pace, and that presents a real challenge—keeping up. Right now, it sometimes feels like a technology searching for a problem, which is understandable given how generative AI can feel almost magical in its capabilities. This often leads to questions like, “Where else could this be applied?” But it’s important to refocus on the fundamentals: What are the actual pain points within the company? The right approach is to reevaluate the established assumptions first and then find the unlocks with technologies, which may include generative AI, that can address them effectively.
While AI is poised to revolutionize the software development industry, the bigger question is whether people can adapt and reskill quickly enough to keep pace. If we look at the adoption of cloud technology, despite its proven benefits, less than 15% of enterprise IT spending and fewer than 30% of workflows have moved to the cloud. This gap highlights how challenging it can be for industries to embrace new technologies, even when the advantages are evident.
At Publicis Sapient, we see AI, alongside other technological advancements, as critical to building a resilient future. We believe that agile engineering is key to staying ahead. This means being adaptable, flexible, and ready to evolve as AI and other technologies reshape the landscape. It’s not just about working faster; it’s about having the ability to pivot and grow in response to new developments.
As AI technology evolves rapidly, do you believe traditional software engineering practices still hold value? How is Publicis Sapient blending these traditional methods with innovative AI-driven approaches?
Traditional software engineering practices are still incredibly valuable—they are the bedrock of everything we do. What has evolved is how we apply these practices in today’s fast-paced tech landscape. At Publicis Sapient, we believe in the power of agility. This often prompts us to think, “Where else could we apply this?” But it’s crucial to return to the fundamentals—identifying the core pain points within the current process first and then finding the right technologies to address them. Sometimes that solution is generative AI, but it’s not always the starting point. The key is blending tried-and-true methods from the past with innovative approaches of the future.
Take agile engineering, for instance. It has been around for over two decades, and while the core principles remain unchanged, the way we implement them has evolved. We continually adapt these practices to align with new technologies like AI and cloud computing. It’s about being nimble and flexible, leveraging the knowledge we’ve accumulated over the years to tackle the challenges of today and tomorrow.
A great example of this approach is PS Bodhi, a solution we’ve developed that embodies our commitment to reuse and efficiency. PS Bodhi allows us to scale generative AI quickly and effectively, merging the solid foundation of traditional engineering with cutting-edge innovation. By integrating these elements, we’re able to stay ahead of the curve and deliver solutions that are not only relevant today but also future-ready.
What initiatives has Publicis Sapient implemented to enhance engineers’ skills in AI and machine learning?
At Publicis Sapient, we emphasize a continuous learning mindset that extends beyond acquiring new skills. That’s why we’ve introduced AI Assisted Software Development and AI engineering as a key initiative. This involves evolving the processes and solutions using AI to achieve the desired outcomes, empowering our teams to apply AI more effectively.
We encourage our people to become a 10x engineer, adopting reuse, creativity, consistency, and multi-dimensional in their work. By integrating this approach into our daily practices, we ensure that our people are always learning and evolving, blending human creativity with cutting-edge technology to drive consistent, transformative solutions.
What specific AI and machine learning skills do you consider essential for engineers to acquire today?
As leaders, it’s essential to create an environment for our people to be able to learn and grow. We should encourage our workforce across all levels to develop a mindset of consistent learning. We focus on creating an environment that nurtures this mindset. We encourage our teams at all levels to embrace the idea that learning doesn’t stop once you’ve mastered a skill—it’s an ongoing journey. Engineers need to be prompt in applying new knowledge, whether it’s understanding the latest AI algorithms, improving their data science capabilities, or mastering machine learning frameworks.
The skills that are most essential today include a strong foundation in data engineering and machine learning principles, proficiency in languages like Python, frameworks like TensorFlow, and PyTorch, and an understanding of data manipulation and model training. But beyond these technical skills, engineers must also be curious, collaborative, and proactive in constantly seeking out mastery in application of the emerging tools.
What are the key ethical challenges that organizations face when integrating AI technologies, and how should these be addressed?
One of the major challenges in AI-driven transformation is the need for a comprehensive and adaptive regulatory framework that both encourages innovation and addresses potential risks. To ensure AI systems are fair, secure, and unbiased, rigorous audits are essential for detecting bias and safeguarding data privacy. As AI is increasingly used for decision-making, robust audits help maintain public trust and regulatory compliance.
Effective AI audits encompass several key components:
• Algorithmic Transparency: Audits must thoroughly examine AI algorithms to uncover biases. This includes analysing algorithm logic, testing various inputs, and comparing outcomes to identify bias patterns. Transparent algorithms enable auditors to trace and explain decision-making processes.
• Privacy-Preserving Techniques: Integrating privacy-preserving techniques like differential privacy into audits protects individual data while enabling meaningful analysis. This balance between data utility and privacy is crucial for trustworthy AI systems.
• Implementing Bias Evaluation Systems and Red Teams: Bias evaluation systems and red teams systematically identify and measure biases, providing ongoing insights and stress-testing AI models. Their findings inform necessary adjustments, ensuring fair and reliable AI systems.
These AI audits are vital for detecting and addressing bias and data privacy issues, ensuring organizations develop fair, transparent, and secure AI systems. In addition, it’s also important to ensure that the client data and privacy is always maintained, and all AI based solutions are aligned to client’s needs and requirements. By establishing clear guidelines, we can harness AI’s full potential and maximize the advantages of this transformative technology across all sectors of society while upholding fairness, transparency, and accountability.
As AI tools become increasingly prevalent in engineering processes, what changes in mindset and behavior do you think are necessary for engineers to succeed?
Engineers must shift their mindset and behaviors to stay ahead as AI tools become more integrated into engineering processes. One of the most critical changes is the ability to redesign and rethink solutions using generative AI patterns. This transformation is essential for adapting to the increasingly AI-driven technological landscape.
To thrive in this evolving environment, engineers should embrace a learning mindset characterized by 10X behaviors. These include being Polyglots—proficient in multiple skills—and fostering mentorship. A diverse skill set and a commitment to continuous improvement are crucial.
Collaboration in cross-functional pods ensures that solutions are comprehensive, while a focus on resource efficiency and multidimensional expertise across SPEED capabilities drives value. These traits are essential for engineers to adapt to dynamic environments and optimize resource utilization.
Institutions play a pivotal role in addressing skill gaps by incorporating sustainable engineering practices and emphasizing mentorship. Developing the next generation of tech experts requires robust mentorship programs within both educational institutions and organizations. The focus should be on empowering young professionals, fostering a culture of continuous improvement, and building a strong sense of community within engineering circles.
Moreover, cultivating an entrepreneurial mindset is key to sparking creativity and innovation among young tech professionals. Organizations must encourage risk-taking and creative thinking, allowing individuals to contribute meaningfully to the organization’s innovation efforts. The 10X behaviors provide a framework for nurturing this mindset, driving efficiency, productivity, and higher-quality work.
To conclude, could you share your thoughts on the future trajectory of AI in software development and any advice for upcoming engineers who wish to excel in this evolving landscape?
AI, particularly generative AI (GenAI), is set to transform how businesses operate by significantly enhancing efficiency, driving rapid innovation and enabling the ability to build more complex solutions easily. Organizations are increasingly adopting GenAI to streamline processes, optimize workflows, and create unique value propositions with fewer resources. This shift goes beyond mere automation—it represents a fundamental change in how we approach problem-solving, with AI tools enabling continuous improvement and greater agility in business operations.
As AI technologies continue to advance, we’ll see a growing demand for specialized roles such as AI engineers, data scientists & engineers, and AI ethicists. Engineers will need to develop a strong understanding of AI models, libraries, and data governance, much like the current expectations around cloud computing knowledge. This trend signals that AI expertise will soon become a foundational skill for all engineers, regardless of their specific roles.
For upcoming engineers looking to excel in this evolving landscape, my advice is to cultivate a deep understanding of AI and its applications. Stay curious, embrace continuous learning, and develop a diverse skill set that includes both traditional engineering practices and emerging AI technologies. By doing so, you’ll be well-prepared to navigate and thrive in the future of software development.
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