Sham Kashikar, Executive AI Advisor and Chief Data and Analytics Officer at The AIAG, speaks with Derek Strauss, Gavroshe Founder, in a video interview about cross-functional collaboration, breaking data silos, a case study, and the importance of strategic partnerships in AI.

AIAG (Automotive Industry Action Group) is the global collaborative hub for automotive excellence. For over 40 years, AIAG has been uniting automotive leaders to drive supply chain excellence, ensuring quality, sustainability, and compliance through collaboration.

The role of cross-functional collaboration in data and AI success

Kashikar reflects on the indispensable role of cross-functional collaboration and data sharing in unlocking the true value of a company’s data and AI program. According to him, these elements are not just technical necessities but foundational drivers of strategic growth and smart decision-making.

Kashikar illustrates this with a telling example from a high-growth B2B company that struggled due to a lack of foundational data practices.

• The company, operating in a fast-paced environment, had just emerged from an intense growth period.
• As a result, the organizational culture emphasized speed, often at the expense of building core infrastructure.
• There was no enterprise-grade data infrastructure, nor a defined concept of data governance or organizational governance as a whole.

This led to widespread decentralization of data management across departments: “Sales, marketing, customer success, product, and finance, product team to build their own data infrastructure because the centralized technology team or IT team, they were just providing data pipelines. That is just not enough to make it a strategic initiative.”

The consequences were significant:

• Fragmented data infrastructure and inconsistent interpretations of information
• Lost productivity due to duplicated efforts and misaligned priorities
• Lack of strategic alignment hindered business growth and opportunity capture

Next, Kashikar points out the limitations this caused across key functions:

• Sales teams lacked visibility into customer product usage, hampering their cross-sell and upsell efforts
• Customer success teams were unable to identify at-risk customers in a timely manner
• Marketing struggled to effectively target new logos due to incomplete customer insights

“There was no cohesive customer-360 or customer insight available where the sales team couldn’t find what the customer product usage was.” The consequences of poor data collaboration included suboptimal renewal rates and missed targets for new customer acquisition.

On breaking data silos

Speaking of breaking data silos, Kashikar says, “Breaking down data silos is very important, and the strategies can depend on the organization’s culture and maturity in a variety of different areas.”

He emphasizes elements such as having a clear vision and executive buy-in on the data program, that have shown success across industries. Further, Kashikar stresses investing in data literacy and fostering a sense of shared purpose across departments is essential.

Delving further, he states establishing cross-functional teams and having champions within the business is critical. These champions act as bridges between departments and help ensure consistent application of data principles.

“Having a standard governance function in place is also equally important. Have standard definitions, metrics, and data quality programs where trusting data is key,” says Kashikar. Without trust in the data, he warns, collaboration becomes extremely limited.

Moving forward, Kashikar suggests investing in modern, scalable data infrastructure and AI infrastructure that is not siloed. “It has to be a well-architected solution that serves the enterprise.”

A case study in transformation

Drawing from experience, Kashikar shares a detailed example of how these principles played out in practice. At a fast-growing organization where new customer acquisition and renewal rate improvements were top strategic goals, he and his team adopted a collaborative and cross-functional approach.

This involved bringing together the leadership from sales, marketing, finance, customer success, and product to create unified priorities and metrics.

“We established the basic data governance function with clear roles and responsibilities. Also, we defined what the core data infrastructure is going to look like.”

The team also focused on relentless communication, and change management was treated as a horizontal effort spanning across the entire initiative.

“Not only were the AI/ML models built, but they were integrated with the CRM system as well as the sales processes,” adds Kashikar. These models were made actionable, directly informing customer interactions and campaign strategies in real time.

Highlighting the outcomes of these efforts, Kashikar shares that there was a 20% improvement in renewal rates and over a 7% increase in new customer acquisition.

Strategic partnerships in AI: Driving innovation and impact

“Any successful partnership starts with identifying the business strategy and business goal,” says Kashikar.

He shares multiple examples to illustrate how collaboration, when rooted in business strategy, can create meaningful outcomes — both for organizations and society.

Kashikar points to one of his favorite examples — a collaborative effort in the energy sector focused on climate impact.

This initiative involves leading energy providers such as Duke Energy and PG&E teaming up with tech giants like Microsoft, Amazon, and NVIDIA to create open-source AI and GenAI models. The mission is to optimize energy grid performance, improve maintainability, and increase efficiency, fostering sustainability and economic value.

“That could have a huge impact not only on the energy sector, not only on the profitability or efficiencies but on our lives as well.”

Building partnerships with purpose

When asked how to approach partnerships, Kashikar emphasizes the need for clarity and alignment: “You cannot just focus on point solutions here and there. You need to understand your organization’s needs.”

Key steps he outlines include:

• Assessing organizational maturity across talent, infrastructure, domain expertise, and compliance.
• Defining clear business outcomes and measurable use cases.
• Selecting partners who align not only in technical capability but also in vision and values.

“You look for a partner who is not only a technical expert but also aligns with your values and vision.”

Practical innovation in digital products

Wrapping up, Kashikar also shares a practical business example involving Amplitude and Beam AI. Amplitude offers deep analytics into user behavior, while Beam AI specializes in automation-focused AI agents. Together, their collaboration empowers companies to optimize customer journeys across digital platforms.

This results in improved user experience and increased conversion rates, which are tangible business wins driven by integrated data and AI insights.

CDO Magazine appreciates Sham Kashikar for sharing his insights with our global community.