Despite years of digitization, organizations often fail to capture nearly three-quarters of the value expected from digital investments, according to McKinsey research. This is because most companies begin with technological capabilities and then add applications, neglecting to prioritize customer needs from the outset.
Organizations that achieve outsized results from AI flip this approach on its head. They adopt a “customer-back engineering” mindset, developing products and services with the customer experience in mind-considering challenges, needs, and expectations.
Product development teams then work backward to find steps necessary for designing and building solutions that deliver the desired customer experience. For example, Ashish Agrawal, managing vice president of business cards and payments tech at Capital One, notes: “When engineers are closer to customers, they can devise more effective solutions from unique perspectives.”
The case for customer-centricity in engineering
Engineers are natural problem-solvers. By hearing about challenges and how customers use products, they can quickly identify ways to address those needs efficiently.
Agrawal explains: “Fostering a culture where engineers see their work as having a direct impact on customer lives motivates them.” To achieve this, Capital One has set goals for every engineer in the organization to establish multiple touchpoints with customers throughout the year, including digital empathy sessions, embedded customer support periods, engineering ride-alongs, and hackathon competitions.
The AI opportunities with customer-centricity
Engineers face challenges due to a lack of direct access to customers. However, AI has accelerated both the problems and solutions. The pace of product launches is faster, but engineers are closer to the data that powers AI.
Agrawal describes how recent innovations have enabled more rapid experimentation: “In a single conversation, we can summarize customer interactions and provide context for agents. Agentic AI can also ask follow-up questions, saving time.”
Using these tools, teams can move from incremental improvements to transformative changes in the speed of innovation.
The elements of an AI-first mindset
A recent MIT Technology Review Insights survey found that 70% of leaders use agentic AI to some degree. Half of executives expect improvements in fraud detection, security, and the customer experience.
To achieve these outcomes, companies must reimagine the core function of AI as solving meaningful customer problems. Start with a clean data layer as the foundation for unified information across systems. Build cross-functional teams involving diverse expertise to ensure trust and rigor in AI applications.
Key Takeaways
- Adopt a “customer-back engineering” mindset: prioritize customer needs over technological capabilities.
- Establish multiple touchpoints with customers through various methods like empathy sessions, embedded support, and hackathons.
- Use AI tools to enhance efficiency and innovation in problem-solving. Ensure data readiness and governance are foundational for trust and reliability.
- Create a cross-functional team that includes data science, engineering, product, design, and other partners to accelerate transformation.
To achieve end-to-end transformation, organizations must empower engineers and partner teams to start with customer needs and work backward to technology solutions. This approach ensures the customer is at the center from the outset of any project or initiative.
