Five Questions with an OG, Anurag Mathur: Cloud and AI Adoption

MissionOG is fortunate to be supported by a deep network of experienced operators and entrepreneurs. This entry is part of a blog series where we share perspectives from “OGs” – original innovators from specific market segments and/or business disciplines.

 

Anurag Mathur is the Global Head of Strategic Partnerships at Google Cloud, forging strategic alliances that leverage Google’s cloud computing and AI prowess to empower partners and clients in the financial technology sector.

 

PLEASE PROVIDE A BRIEF OVERVIEW OF YOUR BACKGROUND.

My background has been shaped by a global upbringing—moving to a new country every few years taught me how to adapt quickly and thrive in changing environments, which has helped me as I built a diverse career across product, technology, and go-to-market roles, working at both early-stage FinTechs as well as some of the world’s most innovative technology companies. Most recently at Google, my team was tasked with driving revenue by working with FinTech, enterprise, and data firms, enabling them to adopt cloud and cutting-edge AI to accelerate business growth.

 

WHICH PARTS OF CLOUD CONSUMPTION HAVE BECOME A COMMODITY AND WHAT AREAS WILL BE DIFFERENTIATED OVER THE NEXT THREE YEARS?

As with previous massive technology platform adoptions like the internet and mobile, the cloud’s foundational layers—compute, storage, and networking—are becoming more commoditized. With the emergence of new connectivity standards, multiple cloud ecosystems, emerging business models, and partner marketplaces, customers have been able to de-risk cloud selections.

The next wave of differentiation, again similar to previous platform adoptions, will be in advanced services: consolidated AI/ML platforms, more cost-effective and performant models, industry-specific solutions, edge computing, security, sovereignty, and the potential of quantum computing to unlock new use cases. We are seeing cloud companies racing to build out ecosystems, offering customers a comprehensive platform for innovation, or the option to stitch together ‘best-in-class’ capabilities that best suit each customer’s needs. This has provided opportunities for niche solution providers to emerge and grab market share.

 

CAN YOU DESCRIBE THE TRENDS YOU ARE SEEING IN ENTERPRISE ADOPTION OF ARTIFICIAL INTELLIGENCE?

The enterprise adoption of AI has been accelerating as companies are beginning to get past the experimentation phase and into production-level use cases. To be clear, there are many firms that are still struggling with AI adoption for a variety of reasons – immature or fragmented data management controls, talent gap, etc. The firms that have seen early success have clearly defined use cases with quantifiable ROI, a willingness to push solutions out to customers, and corporate-wide mandates for all internal teams to prioritize AI adoption. Many companies are finding value in starting with industry-specific and hyper-specialized solutions.

The rapid pace of new innovations and models, the rise of agents, ever-present regulations, competing ecosystems, and even ongoing geopolitics provide new challenges that firms must constantly monitor. As always, at times of massive inflection, incumbents are at risk and have to strongly consider new business and operating models, while disruptors without legacy tech or data debt, are racing to establish market credibility and win market share.

 

WHAT TRENDS ARE YOU SEEING IN ENTERPRISE DATA MANAGEMENT AND UTILIZATION?

In some ways, the trends in data management that have always been present are only accelerating in AI-first cloud-native environments that require a comprehensive enterprise-wide data strategy. The challenges of data availability, security, privacy, speed, etc. all still exist, and in larger firms, it is more likely that there are multiple technologies and strategies. In addition, operational data like emails and chat logs are now essential to drive some AI use cases. While this has driven incremental demands on the underlying data platform, in some cases it has actually unlocked additional budgets.

Many companies that are primarily data publishers, or have a lot of operational data, or generate monetizable ‘exhaust data,’ are beginning to treat data as a service, wherein all enterprise data is made discoverable, understandable, and reusable by a team. Some firms have created enterprise data organizations under a CDO to unlock value across the organization.

 

WHAT ARE THE TOP PRIORITIES AND NEEDS OF YOUR CUSTOMERS NOW, PARTICULARLY IN THE FINTECH SPACE?

FinTech has been having a bit of a moment in the sun with disruptors coming of age with significant customer growth in many global markets and segments, renewed M&A activity, the easing of some government regulations, and the acceleration of partnerships between the leading financial services firms and emerging FinTechs. In this environment, customers are focused on several pillars:

• Revenue & Growth: Allow financial services firms, and really any brand, to provide seamless, relevant, personalized experiences to customers, with the ability to engage them at every step of the customer journey; to be able to offer the widest set of products, be it crypto, trading, remittances, etc., and to offer it to them through whichever channel they prefer, enabling every brand to be a financial services firm
• Risk Management as a differentiator, not a cost: Achieve ambitious growth plans while minimizing fraud, being compliant in every market they operate in, adhering to the highest standards of data security and user privacy, in a manner that users can trust
• Operate efficiently and drive profitable growth, especially in this financial market, which often also means building out partnerships and ecosystems with other providers

 

WHICH DEVELOPMENTS IN THE INDUSTRY ARE YOU MOST EXCITED ABOUT?

While I am excited about all the innovations coming at a macro-level – new multi-model models, agentic workflows, etc., I am paying attention to the providers that are tackling problems in a few “un-sexy” areas including:

• Back-office innovation and the modernization of functions that are still sitting in legacy applications or that still rely on manual operations, and which have the opportunity to drive significant efficiencies when redesigned
• Updating and refactoring of systems of record that have historically run critical workloads and were therefore not touched during the initial wave of cloud adoption. These systems have the data that are core to offering new capabilities and experiences
• As the corpus of publicly available information is quickly being consumed by the leading models, the opportunity to extract private, protected, or un-indexed datasets that can drive new AI-based insights will provide unique monetization opportunities
• The adoption and acceptance of new technologies in both the private and public sectors to tackle some of the society’s biggest problems like fraud, terrorist financing, human trafficking, and climate risk

As a new generation of users and customers take charge, there is an opportunity to drive massive efficiency and unlock time and resources to fund innovation with the type of customer experience that historically have been associated with consumer products.