Large Language Models Disrupting the Enterprise

MC+A Insight Guest Article. Original Article can be found here.

Vivek Sriram

Vivek Sriram

Chief Product Officer @ Panape

Not since Alexander Graham Bell asked his butler, Mr. Watson, to “come here. I want to see you,” has human communication gone through so radical a change in so short a time as ChatGPT has caused in a few short weeks. It has truly captured the public consciousness.
Edison misheard “hullo” as “hello.” While no match to Bell’s exuberant “ahoy!” it is frequently the way we open conversations.
Perhaps Prompt Engineering is the new hello.
In the few short weeks since Kevin Roose caused Bing to fall in love with him, Silicon Valley Bank melted down, libertarian VCs rekindled their love of government, Open AI released GPT4 and marketing departments at prominent VCs cranked out new market landscapes. Perhaps it was the work of VCGPT — though one would never know. A lifetime of change in 3 weeks now heralds hundreds of new companies parading Large Language Models for every occasion. At this rate, Gartner’s Slope of Enlightenment is vertical.

There is a new vector search engine coming out every other week now. Myriad LLM-powered apps offer to <translate what jargon-laden landing pages actually mean>, to <interpreting loan applications> to <finally make it possible to search for that PowerPoint deck your CEO sent you 3 months ago>. There are LLM-powered apps for literally every occasion. There is no doubt that LLMs are transformational. But what’s every company not creating LLM-powered apps — or new Foundation Models — to do about it? Clearly there are many implications to IT, corporate governance, to marketing, to strategy and especially to the C-Suite.

IT has seen, stood up, and evolved through every evolution of enterprise tech over the past 20 years, from Cloud to Crypto, has continually adapted and has also continually stayed relevant. For the simple reason that corporate leadership likes to keep control over the stuff that generates money and likes to outsource the stuff that costs money, whatever this new evolution will be as a result of a proliferation of Large Language Models, the next generation of the enterprise will similarly adapt, adopt, evolve and set new standards for how the users of the future will consume and incorporate these technologies into their daily lives.
The same few considerations which cause IT to formulate strategy and CIOs to craft policies are likely to remain in the future, largely as they have in the past.
  1. Data ownership: companies like to own their data. If it’s important, or if sending it away makes their competition stronger, they are unlikely to let go of it, no matter how cool the usability might be.
  2. Security: CIOs and CISOs will pay exceedingly close attention to any data which has the possibility of creating exposure to risk. Generative AI using corporate data is likely to get extra scrutiny from these C-level execs.
  3. Productivity: developers across departments are likely to be radically more productive with a new generation of LLM-powered tools than before. The tools that deliver security and data stewardship have the greatest chance of winning the love of leadership.
There is great promise in LLMs. They truly are transformational. The companies of the future will take advantage of their myriad capabilities to forge new competitive advantages from them by using them to eliminate wasted effort and spurring new avenues of creativity. While the opportunities abound, the same constraints and concerns that companies have had through multiple evolutions of tech — security, data ownership, productivity — will remain. LLM-powered apps that want to sell into the enterprise must address these concerns — even more so than the tech of previous generations.

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