Bridging the Transformation Divide with Artificial Intelligence (AI) and Data

Winning with Data and Artificial Intelligence (AI)

Even with significant digital transformation efforts, many companies still struggle to realize the benefits and are often left with results they may or may not have expected. Why is this happening and what do successful companies do?

Across industries, there is an increasing “Transformation Divide”. The winners have sprinted ahead, while 2 out of 3 (67%) of CEOs feel that they’ve fallen behind*. And those who have sprinted ahead, the most profitable industries, are getting double the growth (i.e., pharmaceuticals, Fintech, Medtech, media) and have grown their collective economic value by more than +$300B while the bottom 20% have lost over $300B in market cap.

The winning organizations all agree that data is the life force that’s essential for activating next-gen operating and business models for their business. For some, this is about delivering a rich experience across billions of moments. For others, it’s about accelerating decision velocity to cut through organizational lethargy. These organizations are using data to see what’s next and to drive continuous innovation.

In that quest, AI has become the number one priority in the digital transformation, and there is nearly no conversation around modernization that doesn’t have AI or data as an agenda point. In this area, success comes from driving incremental business value. For some, this is about improving decision-making to reduce human error and move faster. For others, it’s uncovering deeper insights, improving quality, and faster time to market. Last, many are using AI to deliver cost efficiency, optimizing outcomes, and productivity.

But first things first, digital transformation is incomplete without data-first modernization. Data has become the heart of every digital transformation initiative across every industry. Data is everywhere, but accessing it without disrupting productivity is a key pain point. For most organizations, significant challenges remain as they aim to execute data-first modernization initiatives.

Changing the Game with the Cloud Operational Experience

When I speak to customers, one of their key topics is unlocking the value of their data. They express a need for a simplified approach to data that delivers a cloud-native experience without moving everything into the cloud and replacing one proprietary platform for another. They need to use their data to speed up time to insights down to the minute, accessing their data wherever it is, and powering their analytics pipeline. As they strive to gain better and faster insights to advance their company, they are often willing to spend now to gain an advantage later if they can identify a solution to help them navigate this complexity. With that, speed and trust are of the essence.

HPE has taken this customer feedback to develop an engagement model that considers the cloud operational experience as the real game-changer. Our engagement model, represented in the chart below, reflects enterprises just approaching their modernization journeys and those that are evolving and scaling AI towards the core business. I believe that no matter the journey phase, this method leads to an iterative and structured motion where data represents the starting point and gets enriched, iteration after iteration, empowering the business adoption of AI while continuous operations are crucial for defining a successful journey.

We have applied this model with our customer United Overseas Bank Limited (UOB), a leading bank in Asia with a global network of over 500 offices in 19 countries. Together with their team, we explored potential use cases and held an AI Transformation Workshop. During the experiment phase, we developed a proof of value. As we evolved the scenario, we developed a Compliance Platform as a Service with our Technology Partner Tookitaki that helps the United Overseas Bank to address Anti-Money Laundering and reconciliation issues.

AI has become a powerful tool to get more insights and value from data, and organizations face the careful responsibility to use it for good. At HPE, we strive to use and develop responsible AI with beneficial outcomes for people and businesses, and public services guided by ethical principles.

As a global market leader, we must ensure AI has a positive impact on people. I’m proud to see how our team members at HPE engage in empowering AI for Good by applying it to fight dengue fever spread and improve the demand planning for global blood management, an effort that can meet the unconstrained demand of blood donations by increasing forecast accuracy and thus reducing the global over and under-provisioning.

What’s next

As we evolve our portfolio, we are defining the next generation of technologies and frameworks. An example of this is High-Performance Computing and Artificial Intelligence workloads that are often converging, with huge loads of data sharing similar requirements or technology stacks. This is an area where I see lots of potential for HPE GreenLake.

Another area is HPE GreenLake for ML Ops. Another example is healthcare provider Carestream, for whom we enabled verticalization of specific needs and industry standards providing a scalable, future-proof AI as a Service platform to develop, deploy, monitor and support AI models enabled imaging systems at the edge.

I would like to invite you to learn more about our AI & Data capabilities and how we can help you improve your business outcomes.

For more information about HPE GreenLake, visit https://www.hpe.com/us/en/greenlake.html.