How Natural Language Processing will Improve Enterprise Technology Adoption
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Implementing new processes, technologies, and automation into large companies is a major challenge. Any significant project can easily take years to roll out and years to reach full potential. Is natural language processing the tool to speed up adoption and vastly improve enterprise project rollouts?

Most of us are familiar with how NLP is utilized in the personal and consumer spaces, with the growth of items like Alexa (Lex Technology) and Google Home (unstructured language). Our smartphones have become a particular hub for integrating NLP.

But how will these technologies revolutionize businesses and enterprises?

That’s what we’ll look at here.

Market projections for natural language processing

Like many other trending areas of technology, explosive growth is expected in NLP over the next few years. According to Tractica Research  (Tractica 2017), the Natural Language Processing Market is set to reach $22.3 Billion by 2025.

Natural Language Processing Market Growth-17-chart
Tractica Report 2017: https://www.tractica.com/newsroom/press-releases/natural-language-processing-market-to-reach-22-3-billion-by-2025/

The algorithms are largely considered to be effective and mature. The next steps are primarily in adoption and suiting to real-world use cases, particularly in the commercial and enterprise spaces.

Enterprise use cases for natural language processing

If you’ve participated in a major IT project, you know the massive challenges in gaining adoption and benefits across the company. But what if instead of manuals, training, enforcement, and hand holding, your users could just start interacting with new technology and processes in a natural way?

Here are a couple of examples to think about…

1. Big Data, Business Intelligence, and reporting automation

Delivering intelligent ways to understand an organization’s data is fantastic…especially when many companies still perform a shocking variety of data-driven tasks with Microsoft Excel.

While adoption of BI and Big Data in enterprises is a clear trend, we still see massive adoption difficulties thanks to complex implementations, inflexible user interfaces, and a slew of manual tasks in the background to deliver the reports people want.

Ultimately, you end up with a handful of power users and a large portion of users who default back to their Excel days because it’s quicker and easier to use.

Now imagine a natural-language implementation…

What if your users could instead type a question into a reporting chat channel on something like Slack?

“How many widgets did we ship to Arkansas this year compared to last year?”

Now your systems can go to work and deliver an answer.

Guess what happened?

  • Automation is actually being utilized…and your business case realized.
  • You don’t need to train people.
  • Adoption of the platform skyrocket.
  • Your organization can make intelligent decisions.

What if you had the power of NLP in your data?

2. DevOps and CI/CD (software-delivery lifecycle)

We are big on DevOps, but to do it effectively, you need to gain buy in and adoption from multiple organizations and many users. In fact, getting the adoption remains one of the biggest challenges. Silos continue to plague large companies and hold us back from achieving true continuous integration and delivery.

Many of the DevOps tools are very technical in nature, so bridging the gap can be particularly difficult when it comes to bringing in the non-technical side of the house, including:

  • Operations managers
  • Project managers and scrum masters
  • Business analysts
  • Reporting admins
  • Even quality assurance specialists
  • And more…

While there are plenty of technical solutions to automating software delivery, it’s no easy task to make the usage and adoption simple across the board.

But what if your infrastructure spoke a human language?

Wouldn’t it be nice to automate the interaction between everything here and actually have people use it?

Enter natural language processing. Imagine triggering steps in DevOps and gaining data out of the process with simple chats and questions? As soon as the automated components can interact with your teams naturally, the barriers to adoption disappear.

“Deploy new georedundant servers from the 1.08 template and notify the performance management team once ready.”

or…

“What were the results from the latest test suite?”

In this way, intelligent bots automate and augment workflows. They can initiate a complex sequence of tasks, make decisions, and deliver reports all while interacting in a very human-friendly manner.

This delivers automation your teams will actually use and that will deliver real-world results.

For more on natural language in the context of DevOps, check out our 7-minute LeTo video.

Conclusion

Humans have trouble adapting to change, especially when it comes to keeping up with the rapid advancements of technology. The answer is to expand the concept of “user interface” to include natural interaction. Language processing, unstructured text, and AI platforms pave a clear path to the future.

We expect interacting with complex technology to be massively easier in the near future.

What do you think? Let us know in the comments below…

 

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