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Tuesday, May 28, 2024

How to Maximize the Value of Your AI Projects

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Grant Case
Grant Case
Grant Case is a data science leader with 20+ years' experience. As Head of Sales Engineering at Dataiku, he leads a team driving data-driven solutions for clients across APJ, empowering them to solve complex challenges and innovate with the AI platform.

How businesses maximize ROI, tackle economic challenges, and navigate AI maturity with strategic insights and practical approaches.

In recent years, the Asia Pacific region has significantly evolved to adopt Artificial Intelligence (AI) technologies within its business landscape. In 2021 and 2022, a modest 39% of businesses leveraged AI in their operations. Contrast that with today, where an astonishing 76% of enterprises in the region are driving growth, streamlining efficiency, and sparking innovation by utilizing AI. Southeast Asia’s projected AI platforms illustrate this shift in market spending size, growing by 40.8% from 2022 to 2026.

Therein lies the challenge: data leaders are tasked to transform their organization into a data-driven company, infuse AI within the entire organization, and maximize ROI across the board. At the same time, they need to keep AI costs under control. 

Amidst these technological developments, we can’t overlook the macroeconomic headwinds impacting APAC enterprises in 2023. 71% of APJ enterprises believe they have been in a recession since the beginning of this year. Despite the economic uncertainty, integration and AI projects have remained resilient to budget cuts. So, how can they demonstrate the value of leveraging AI in their operations? 

Let’s bring it back to the basics

As organizations reshuffle budgets and shift their spending priorities, APAC AI investment plans focus more on internal use cases, including employee productivity, cost reduction, and operational efficiency. The goal is clear: regional organizations want to make the most of their AI budgets, striving to achieve more with less. 

Scaling and integrating AI can appear overwhelming and sometimes even chaotic.  Finding a structured approach — effectively, some method to the madness — is essential. However, the mistake organizations often make is diving straight into complex use cases, which can prove costly in time and money. The better approach would be to prioritize the easiest use cases and tackle the low-hanging fruits that can showcase the success of your projects. This, in turn, makes it easier for teams to demonstrate the value of their AI projects. 

Organizations can iterate on this success by incorporating new projects into production, selecting and prioritizing them based on their potential value relative to complexity and time. The goal of increasing ROI while controlling costs and risks remains, though.

Indeed, as an organization grows, these use cases become more complex. The marginal value of these new use cases starts to decrease. At the same time, the cost of maintenance and the cost of executing each use case is also marginally and linearly increasing.  Now, let’s crunch the numbers: as the marginal ROI diminishes and operating costs continue to surge, there comes a critical point where these elements intersect and, unfortunately, are not in favor of profitability.

So, how can organizations sustain their momentum? How can we address that challenge and simultaneously maintain the AI strategy as a center of profit?

Three ways to address the AI maturity crisis

Data cleaning is one of the most costly, tedious, and time-consuming aspects of AI projects. However, if poorly executed, it can translate into poor-quality models and increased risk through the entire model lifecycle. Therefore, reusing and recycling AI components are great ways for people across organizations to become more efficient with their data. Reuse is the simple concept of avoiding rework in AI projects, from small details like code snippets to the macro-level to finding and using clean and trusted data. 

An example would be to provide a built-in, centralized, and structured catalog of data treatments (from data sources to data preparation, algorithms, and more) for easy consumption.

In addition, a profitable AI strategy requires massively increasing the number of use cases being addressed across the organization. To do this, organizations need to empower anyone (not just people on a data team) to leverage the work done on existing AI projects to spin up new ones, potentially uncovering previously untapped use cases that bring a lot more value than expected. 

Imagine that a large organization’s marketing and customer service departments have separately developed AI projects to engage customers better. However, they do not know each other’s initiatives. Large economies of scale can be achieved by democratizing AI and data across the organization. That’s where Everyday AI comes into play, where it’s not just data scientists bringing value from data but the business itself.

Finally, organizations need to consider MLOps early to remain in control. Why MLOps?

AI projects don’t end when they move to production; they are living tasks requiring constant monitoring and evaluation.

The real challenge kicks in after deployment of your model– you need to keep a close eye on the model, updating it regularly to ensure it still works well with changing data conditions. Organizations can not keep this ongoing maintenance out of the equation. Multiply this by 10, 100, or 1000 for the most advanced organizations. Furthermore, generally, a model may become less effective or, in the worst case, cause problems and become costly for the business. That’s where MLOps comes in handy. It’s a way to manage maintenance costs by shifting from a one-off job handled by different people to a more organized and centralized process.

Moreover, data scientists and machine learning engineers prefer to spend their time developing new models rather than maintaining ancient models. That makes them come to work; otherwise, what’s the point of doing a long, tedious Ph.D. in mathematics or data science? Ultimately, MLOps is, for companies and data leaders, a way to reduce the attrition rate within the team, and that’s another way to save money by keeping the talents.

As AI becomes more embedded into organizations’ ecosystems and more budgets are invested into embarking on new projects, the need to demonstrate its value is more important than ever. We don’t know what the future may look like, but we do know that AI will be a part of everyday life, and it’s warming up. It’s time for organizations to embrace the AI revolution and make room for its transformational impact. 

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