From Test Drive to Mainstream: Why Enterprises Should Consider a Platform Approach to Deploying NLP AI
By Subrata Chakrabarti, VP Marketing, Kyndi
We are seeing an exciting shift in the enterprise mindset related to NLP AI solutions. On an increasing basis, organizations have evolved from a ‘does it work?’ mentality to a focus on ‘how do we deploy?’ this valuable new technology enterprise-wide.
Over the past few years, enterprises were laser-focused on testing whether the technology could uncover the intelligence hidden in unstructured text. As the array of NLP and NLT technologies evolved rapidly, early adopters tracking their progress and kicked the tires on a range of tools — each solving a portion of the challenge. While some NLP tools focused on better search results (i.e. semantic search), others dealt with text classification or automatic discovery of key concepts. Enterprises found that many of their use cases required more than one of such tools or the addition of internally-developed applications. As a result, they were forced to cobble together technologies and tools and run PoCs to determine whether it was possible to convert unstructured text into strategic insights.
And the good news is: a number of these technologies worked! But the bad news is: internal IT organizations were left with integration and deployment hassles. Some came to the conclusion that they’d need to continue to depend on time-consuming and expensive consulting resources to bring the pieces together.
Asking the right technical questions
While proving technical feasibility was goal for the first phase, AI technologies that utilize natural language with semantic search and discovery capability are now poised to enter the next phase: the mainstream. But first, IT organizations must conquer the next set of challenges and it’s all about ‘the how’:
• How to build a reference architecture to connect all of the puzzle pieces together?
• How to deploy this connected technology architecture quickly and efficiently?
• How to extend use through the development of apps and functionalities that are rapidly adopted by business users?
• How to scale this new competitive advantage throughout the enterprise with security and industry compliance in mind?
As IT’s questions have changed, their requirements have evolved, too. And that’s because their success will not only depend on deploying NLP AI quickly, it will also hinge on their ability to deploy those solutions well.
Required: No hassle, self-service innovations designed for remote worker productivity
In 2020, IT’s top priority shifted to empowering the remote worker with digital-first strategies to sustain a permanent ‘work from anywhere’ workflow. In doing so, organizations learned that remote work was not only feasible, it was a preferred workstyle for many employees. Consequently, experts believe that the future of work will remain focused on empowering a distributed workforce—and will need IT’s ongoing innovations in this area more than ever.
Partial or hard-to-use tools, however, won’t be adopted by a distributed workforce. To optimize the effectiveness of powerful new AI technologies designed for unstructured text, IT teams must deliver turnkey, NLP-powered applications quickly that are designed — from the ground up — to dramatically improve remote worker productivity. And at the same time, all of these technologies should be guard-railed with proper security control, user access, ease of use and collaboration capabilities, that don’t need to be reimagined with every use case or application area.
Building the core first
This level of support, breadth of functionality, ease of use, and fast time-to-market can’t be achieved if IT organizations are attempting to develop AI NLP capabilities in application siloes and attempting to manage separate teams and different architectures. Employee satisfaction and engagement will be severely hampered as the awesome promise of such innovative technology will turn quickly to disappointment and under-utilization.
Alternatively, enterprises could leverage what worked well in the world of SaaS by embracing a unifying, platform-based methodology that would speed up the data-to-intelligence journey by:
• Doing the heavy lifting at the data source level to identify what data is needed and how to build the ingestion pipeline
• Performing key functions like decruft, data munging, wrangling, and cleansing, including removing non-language specific components – to get it enterprise-ready
• Yielding the higher quality catalogue of unstructured text data so that concepts can be identified more easily and deeper knowledge can be built more quickly
• Building the capability to extend to domain and organization-specific knowledge and information easily
• Pairing the data with a rich set of metadata in an intelligent index that can be leveraged in a number of natural language related application areas
Build your apps on a better foundation
When you have a powerful platform that excels in transforming unstructured text into intelligence, your developers are free to do what they do best: deliver powerful applications. Liberated from the ongoing rework of forcing disparate technologies to work together, they can focus instead on creating apps designed to unleash user productivity across your enterprise — from knowledge workers, to contact center teams, to support staff and across departments. And that means your remote workers will benefit from innovations sooner, and ultimately at a lower cost to your organization.
Contact Kyndi to discuss how to get started on your path to take NLP AI mainstream. We can provide guidance and discuss best practices to ensure you efficiently achieve the results you are looking for.