Scaling AI And Its Importance Within Organizations

According to a study by Accenture, organizations that are strategically scaling AI report nearly 3X the return from AI investments compared to companies pursuing siloed proof of concepts.

Until a few years ago, the discussion around Artificial Intelligence (AI) was limited to academic institutions and advanced research journals. Today AI is transforming many aspects of our personal and professional lives. Facebook, Google, Amazon, and Apple, along with many other big and small businesses, are showcasing the applications of AI.   The numbers below indicate some heavy hitters have made significant investments in the technology resulting in inspiring examples.  But short of asking “Siri, what can AI mean for my business?”, many executives are in search of the smartest, most cost-effective way to invest in AI solutions for our own purposes.

What does Scaling AI mean?

Many organizations have been successful in AI pilots or use cases. But are finding it hard to move towards scaling AI, which means deploying AI across the organization and realizing its full potential. While a POC or a Pilot project of an AI-based solution deals with only a small subset of data, a scalable AI solution has to work with data in real-time as it is being generated and sometimes to the tune of millions of records on a daily basis. This requires the transformation of the operating model of a business, a series of top-down and bottom-up actions, adopting a new culture, and commitment of a big budget.

There are some excellent examples of AI making real progress and helping organizations in automating and optimizing processes, driving productivity, boosting customer engagements, etc., but most companies that have deployed AI as pilot use cases are not making significant progress. A vast majority of companies are still at the stage of experimenting with pilot deployments. Companies struggle to move from pilot use cases to companywide programs and from a specific business problem, such as automating customer support, to big business problems, like enhancing the entire customer experience. AI at scale still remains a work in progress.

Why is it important?

Now, after understanding what exactly scaling AI means – the next obvious and logical question would be why is it important for organizations to implement and scale AI?

1. To put in simpler terms, organizations that successfully scale AI find diversified ways and sources to identify meaningful information. Cleaning, structuring, and managing this information will be easier as they can tune out the noisy data without any difficulty.

2. Apart from this, organizations with proper domain knowledge and with AI-powered applications will be enabled with the power of providing the right analysis and right predictions needed for business transformation.

Scaling AI is critical for enterprises that want to realize its full potential and use it as a business strategy. However, scaling AI is not simple and easy as it requires a significant investment of resources in aligning a company’s culture and organizational structure to make AI scalable and capable of delivering to its true potential.





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