Interview with Fred Bliss (Global CTO, Data Insights)

In the era of disruptions and tech acceleration, how do you think the role of IT is evolving in terms of driving innovation and digital transformation within an organization?

In the era of disruptions and tech acceleration, IT leaders must not only be domain experts within their business but also be able to resist the 'old days' of centralized IT as a cost center. Instead, they should work to embed and enable technology within business units across the organization. This prompts the question: What about ‘shadow IT’ initiatives and too much de-centralization? We work closely with our clients to design and implement governance programs that balance autonomy with risk management. IT leaders who stay at the forefront of technology trends and identify when and where they are applicable to their business can drive innovation and digital transformation more effectively. In work with our clients, we often work with both business and IT leaders, jointly, to help them adopt this approach. As a final note - individual context and unique needs matter. What works for Google or Amazon may not work for a thousand different reasons at a different organization - be it culture, technology adoption, or complexity. We often find the best approach for most organizations is to keep solutions as simple, but as flexible, as possible - be prepared for disruption and for change, and embrace it.

In your opinion, what roles should IT and business leaders play in building the future of the digital economy?

We need to come together as leaders and envision what a better future could look like, removing all biases of the way we've 'always done it'. Disruption and innovation will continue, - which means all the leaders need to foster a collaborative spirit to identify what's working, what isn't, and build a cultural and organizational roadmap toward the future and vision we want as an organization. One great example: a project we led at a global insurance organization, running point on a data strategy initiative in collaboration with business leaders across the C-Suite, divisional leaders, and IT leaders worldwide. By addressing challenges like data residency laws, data privacy, and the need for data integration, we created a unified platform that balances divisional autonomy with strong governance and controls. With buy-in from both IT and a diverse group of divisional leaders, we were able to:

• Begin implementing on the phases in the roadmap;
• Bring together data across divisions that had largely never worked together before;
• All while balancing the centralized governance required for compliance and regulations;
• …without sacrificing the autonomy needed by the divisions to conduct operations and be able to drive more data-driven decisions

What measures can be taken to ensure that IT strategy is perceived as a crucial catalyst for business growth, fostering innovation and delivering strategic business value, rather than being viewed solely as an expense? Align IT strategy with business goals: Sounds simple, right? We see this as the fundamental disconnect more than 50% of the time. Collaborate with business leaders to understand their objectives and challenges, and design IT strategies that directly address these needs. By aligning IT initiatives with business goals, organizations can demonstrate the strategic value of their technology investments. Deliver early and often – and communicate wins along the way: Communicate the tangible benefits of IT initiatives to stakeholders, such as reducing waste and lost productivity, increased efficiency, and ultimately, a way to enhance the work of business leaders in the ultimate goal of enhanced customer satisfaction. By highlighting the positive impact of technology investments on the organization's bottom line, IT strategy can be seen as a value-driver rather than an expense. How to do this? small coalitions, work in small teams, and deliver outcomes early and often - gaining valuable allies along the way - and then rinsing and repeating again, and again, and again. Invest in continuous learning and development: Equip IT teams with the necessary skills and knowledge to stay at the forefront of technology trends, particularly in the areas of data, analytics, and AI. By investing in continuous learning and development, organizations can ensure that their IT strategy remains agile and adaptable to the ever-evolving technology landscape.

This year, there has been an increase in the number of organizations discussing the implementation of AI and LLM. What are some successful best practices you have observed in organizations that have effectively incorporated AI/LLM into their business operations?

2 in 3 of the conversations we have around AI turns into a data conversation. Organizations that have invested in their data ecosystem and data platform, such as building a centralized data warehouse, and integrating data sources across their organization together, have an upper hand here. LLMs enable some fantastic use cases, but without the contextual data required for a given task, they can often fall short. Where we see organizations most successful: 1. Continuously investing in their data: Traditional analytics is not going away, nor are the numerous other opportunities that allow organizations to consume data in novel ways. We see more and more enterprises building applications directly on their data platform, reducing or removing the need for data integration, and allowing for that application to have a holistic source of truth. By investing in this, and instilling best practices for building out data engineering pipelines, you're setting yourself up for success in any AI endeavor.

2. LLMs, combined with Document AI models, are quickly transforming what organizations can do with their data. With ~80% of the world's data in unstructured formats (such as PDFs, powerpoints, images, etc.), most of this data goes unused, sitting away in content management systems. What LLMs can now do is unlock that data, make it available for any number of use cases, and act as a copilot for answering complex questions that unify the bridge between structured data (in your data warehouse) and unstructured data (in an LLM-based embeddings data structure). 3. Build these capabilities in-house; don't outsource your AI capabilities to a third party. We thrive on helping you build and train these teams internally, allowing you to build an initial AI foundation on a small but tangible business use case, and equip you with the technology, people, and process to continue to deliver it again and again.

In your perspective, what do you believe will be the next major development in cloud technology?

Clouds within clouds, or micro-clouds. Companies like Modal and Runpod and even Snowflake have started building out capabilities to make deploying your code easier than ever, and deploying your applications in a way that makes it portable and cross-cloud compatible. Many of these companies already leverage public cloud infrastructure (along with their own specialized ones), and start to blur the lines a bit. Snowflake, for example, has announced Container Services, for both applications and AI models, allowing organizations to deploy their applications directly where their data lives. Since Snowflake runs on AWS, Azure, and GCP, this is all running on a public cloud, but since it's been abstracted within Snowflake's ecosystem, the line is blurred - clouds within clouds are the next 'big' development in that we'll see more 'smaller' and niche players abstract away a lot of the complexity.