6 Dos and Don’ts for CIOs to Implement AI Transformation at Scale in Large Enterprises

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Insights from discussions about AI Transformation with 10 enterprise CIOs at the REBUILD Virtual Roundtable

Introduction

The internet has come a long way since its early days as an unregulated wilderness. In the beginning,  the Internet was used primarily by scientists and researchers, and there were few rules or regulations governing its use. This led to a situation where the internet was essentially a wild west, where anything was possible.

Today, the internet is still a wild place, but it is much more regulated than it was in the past. Businesses use the Internet to communicate with customers, collaborate with partners, and sell products and services. Governments use the Internet to provide services to citizens, such as online tax filing and voter registration.

The internet has become an essential part of our lives, and it is only going to become more important in the future. As the internet continues to evolve, it is important that we continue to find ways to regulate it in a way that protects users and businesses.

But why are we still talking about the benefits of the Internet in 2024? It seems obvious that the Internet is an integral part of every business and every individual’s life, right?

Today, AI finds itself in a similar place to where the internet was in the 90s.

Most people acknowledge that adopting AI is the way forward but have no idea where it can be used.

The word “AI Transformation” has been thrown around so much it has almost lost all meaning.

AI Transformation means everything to everyone

– Andy Walter, ex-CIO, Proctor & Gamble

Almost every business claims to be using “AI” in some form or other. But how is it actually used?

We invited senior execs from some of the biggest enterprises across industries including Target, Comcast, Aditya Birla Group, Kaiser Permanente, Frasers Group, EW Scripps Company, Louis Dreyfus Company, Cholamandalam, and Stada Group to a virtual roundtable moderated by Andy Walter, ex-CIO, Proctor & Gamble. They spoke about Implementing AI Transformation at scale for Large Enterprises, the challenges they had faced, and the state of AI adoption in enterprises today. 

CIOs, here are 6 Do’s and Don’ts for Implementing AI Transformation at scale in a large organization.

AI is a broadsword. DON’T use it as a buzzword

The term AI is so broad! People use the term AI like they were using “digital transformation” 10 years ago. What is AI used for exactly? What does it help with?

AI sounds like a magic bullet that can help businesses save money, improve efficiency, and make better decisions. One of the biggest problems with using AI as a buzzword is that it can lead to unrealistic expectations. 

Another problem with using AI as a buzzword is that it can lead to misunderstandings. When businesses use AI in a superficial way, it can be difficult for employees to understand how it works and how it can benefit the business. This can lead to resistance to change and a lack of buy-in from employees.

DON’T: Glaze over the details – the devil is in them!

AI is a vast field that encompasses a wide range of technologies. Businesses can’t simply slap the “AI” label on the organizations today. They need to be more specific about how they are using AI. 

There is a huge difference between a few teams using AI-powered tools, for example,  the customer support team using an AI-powered chatbot and the entire organization undergoing a large-scale AI Transformation project.

AI has several use cases and there needs to be accepted sub-groupings like AI for content creation, AI for analytics, or AI for automation.

DON’T: Use isolated point solutions in individual teams

Over the last ten years, the market has been primarily building and iterating point solutions or AI built to solve specific problems. AI solutions built for businesses could accept a particular type of data, process it, and deliver output as long as the path was predetermined. Isolated, point solutions worked very well in the short term as it demonstrated the ability of AI to learn, grow and deliver results in controlled environments.

Business leaders should understand the role of continuous workflows compared to isolated point solutions because they can impact the efficiency and effectiveness of their operations.

Continuous workflows refer to the integration of multiple tasks, allowing data and information to flow seamlessly between them. This approach can lead to increased automation, reduced errors, and improved decision-making as it allows for more accurate and timely data analysis.

On the other hand, isolated point solutions refer to individual tools or processes that are not integrated with other systems. These solutions can lead to siloed data and information, resulting in inefficiencies and a lack of organizational visibility. This lack of context can make it difficult for businesses to realize the ROI they expected from their AI investments.

Additionally, if the model is not properly optimized based on the specific context in which it will be used, it may not be able to make accurate predictions, as multiple vendors are working on the same data. Working with multiple vendors also means multiple people just to manage them.

The “who,” “where,” “when,” and “why” provides the information that shapes human decisions and actions. Without context, AI models are also equally likely to fall short and not meet expectations.

One of the leaders echoed this thought while saying “Generalizable AI has a plethora of use cases. Corporate IT doesn’t understand how to use generalizable AI. They don’t use AI beyond isolated tools for specific use cases”.

So far, we have spoken about what not to do but let’s get on to the real meat. Here is how business leaders are driving value with AI.

Why do you need AI, DO you know?

AI adoption is no longer a problem. Almost every enterprise business uses AI today.

But how do leaders make decisions with respect to AI Transformation at large enterprises? How do they set goals?

One of the leaders at the REBUILD virtual Roundtable said “Tech adoption from an enterprise perspective is all about value creation. Nothing else matters.”

Daniel Gandara, VP, of IT Product Development at Mercado Libre said “The goal is not the technology – the goal is the business we’re trying to run.”

AI is a value generator. There are no more debates around this. But how is this value derived?

Successful AI Leaders use AI to solve real problems in their org. They don’t use AI for the sake of using AI. Leaders need to approach AI Transformation as a journey, and not the final destination. This is when AI drives real impact and value.

AI is truly a value generator, but to get real value with AI, you have to fix your foundation – data.

Your data needs some fixing. Just DO it.

“Fix your data” was one thought literally EVERY leader at the REBUILD virtual Roundtable echoed.

Getting the data right is probably the most important step in kicking off a successful AI Transformation journey. But this is easier said than done, especially in large organizations.

Data is siloed across thousands of applications used by teams across functions. It exists in several forms without standardization. Each team knows how to use their own data but rarely does this data flow to others in a usable format. This siloed data needs to be collected, cleaned up, organized, classified, and standardized to be used by the entire organization.

Another leader echoed these thoughts, saying “If you don’t have a unified data platform, people make decisions without understanding the underlying data”.

Ashwini, the founder and CEO of Mad Street Den said “Nobody wants to deal with bad data. It is always someone else’s problem. Nobody wants to step up and be the data janitor.”

This bad data or lack of data pipelines across the org affects almost every team and ultimately hits where it hurts the most, the bottom line.

This is where it is important to bring in a partner like Vue.ai on your AI Transformation journey who can deal with all your data issues, make sense of this data, and derive actionable insights which can be used to make better decisions across the organization. 

DO: Democratize Tech and AI

Around 20 years ago, tech was the back office of large enterprises. It then became the backbone of enterprises all over the world with the proliferation of the Internet in the business world. Today, with AI, it has become the brain of the organization that helps every employee, from an intern to the CEO make data-driven decisions that align with the strategic goals of the organization.

AI adoption itself is no longer a problem. Today, almost everyone uses AI in their own way. One of the CIOs at the REBUILD virtual Roundtable pointed out “We have talented super-users who use spin-off independent tools with AI which IT can’t keep up with. But is it scalable to have hundreds or thousands of disconnected tools used across the organization?”

This is a problem most CIOs face today. How do they integrate tools and data across teams to achieve the cumulative power of a single brain for the organization?

One of the leaders asked, “How do we make tech and data choices that make a positive impact on every employee in the organization?”

The answer to that is a framework to implement AI Transformation at scale in large organizations.

With a partner like Vue.ai to fix your data and lay the pipelines, you can create a unified data platform that can be used across the entire organization by every team in a meaningful way. With that foundation in place, the partner can build the first few applications to demonstrate how the underlying ML models can be used to drive impact and value. After this, each team can build their own applications that help make their jobs better. These applications are no2 not isolated point solutions, but connected systems that talk to each other with the same data platform. Enterprises can drive an order-of-magnitude impact once such a system is in place with a generalizable intelligence layer that can be democratically used by every team, function, and business unit to drive value.