Navigating NLP Adoption Challenges: From Hype to ROI

Since the launch of ChatGPT in November of the previous year, organizations across industries have been grappling with the opportunity to incorporate this groundbreaking technology into their customer experiences. According to the latest 2023 State of Data + AI report by Databricks, AI workloads have witnessed an exponential surge, marking this Year as a pivotal turning point.

The problem of bridging the gap between human interaction and technological advancement is propelling IT leaders to recognize Natural Language Processing (NLP) as the cornerstone to unlock AI's true potential.

In this article, we delve into the challenges encountered by IT leaders during NLP adoption and propose insightful strategies to effectively tackle them.

The Build vs. Buy Conundrum

The excitement of mastering a novel technology can be irresistible to the avid IT enthusiast. A group of pioneers often believes in the "technology-first" ethos, driven by the ambition to independently conquer new horizons. While this approach may have its merits, the absence of pre-existing tools or expertise within the NLP landscape can lead these innovators down a challenging path of trial and error.

This approach might suit organizations equipped with a multitude of machine learning engineers and data scientists, but for the remaining 93% of enterprise companies, dedicating extensive R&D resources to decipher NLP intricacies is simply impractical. We've observed a similar trend around a decade ago when Hadoop emerged, resulting in IT pioneers grappling with tools like Pig, Flume, and Sqoop. Despite their efforts, tangible business outcomes remained elusive for the first couple of Years.

Fast-forward to 2023, and the landscape is far less forgiving. Crafting an NLP model and training it on proprietary data, coupled with GPU computational power, can easily escalate into a multimillion-dollar cloud expense. Expertise in-house is indispensable to determine the appropriate model and data scale for effective training. Moreover, anticipating scalability issues beyond the initial use case is a prerequisite that must be addressed before embarking on development.

Strategize with the End in Mind

A pragmatic approach is to begin with the end-goal in sight. Reflect on the business problem at hand, envision the return on investment (RoI), model development timeline, ongoing maintenance costs, and retraining prerequisites.

Exploring existing partnerships could be advantageous; a framework that's 60% to 70% complete could be tailored to align with your specific needs. This approach could potentially kickstart your project in a mere six weeks, as opposed to a year-long timeline.

Given the current pace of development, as observed in August 2023, there's a compelling argument to believe that the result of a year-long project could be obsolete in the face of rapidly evolving innovations.

Deciphering the Right Business Use Case and Measuring ROI

Any NLP-driven business use case in 2023 should inherently revolve around automation or cost reduction. Merely showcasing the technology's novelty won't secure funding. The focal point should be the concrete value that this technology brings to the table, a value that the existing tools fail to provide.

Consider a scenario: Inventory management emerges as a critical expense for manufacturers, exacerbated by Covid-related supply chain disruptions. Envision an NLP-powered platform offering insights into optimal inventory levels based on demand patterns. Such a solution could free up cash flow, a compelling RoI metric when pitching to the CFO.

You can extend this inventory concept to procurement, planning, finance, and other domains. By quantifying the RoI for each use case, the prospect of obtaining substantial funding becomes more feasible than initially envisioned.

In Summary

There's no one-size-fits-all approach to NLP adoption. It hinges on your organization's maturity, available resources, and a lucid comprehension of potential use cases. However, it's undeniable that the pioneering companies that crack the NLP code will secure a substantial edge, potentially shaping industry landscapes for years to come - much akin to the early days of Google's rise.

Feel free to share your insights and connect with me for further discussions on this journey.

I am an Enterprise Sale Executive with a leading Generative AI company , Conversight.

I am working with Business Leaders to help them solve business problems by providing business insights.

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