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AI, ChatGPT, and Rethinking Digital Transformation


AI, CHATGPT, AND RETHINKING DIGITAL TRANSFORMATION

Rakesh Malhotra




Rakesh Malhotra February 23, 2023

The technology industry often hypes new innovations only to see them fall flat
when in the hands of actual users. This can create hype “fatigue,” and rightly
so. Since the iPhone in 2007, you’d be hard-pressed to identify the introduction
of a genuinely transformative new technology. Even in the case of the iPhone,
many observers at the time dismissed its chances of market success, while its
proponents largely underestimated the wave of innovation it would unleash. 

Thanks in large part to the release of ChatGPT, the world is now captivated by
the potential of artificial intelligence (AI). While some may dismiss this as
another technology hype cycle, it is much more likely that we are actually
underestimating the profound impact this technology will have on life as we know
it. It’s both fascinating and impossible to wrap your head around what happens
to humankind in a world where technological and scientific advancement might be
sped up by orders of magnitude over a short period of time. You probably didn’t
come to this blog post for a philosophical view of AI, so I’ll focus on the
impact we see for our business and public sector clients at Nuvalence. 

At Nuvalence, we build mission-critical software for the world’s most ambitious
organizations. This is often done in the context of “digital transformation” as
we help our clients build new revenue streams and differentiate with software.
What we are telling all of our clients today is quite simple: “If your digital
transformation strategy does not deeply consider the impact of AI, it’s dead on
arrival.” It would be like building a software strategy in 1998 and not
considering the impact of the Internet. Traditionally, software companies would
be the first to adopt new technologies (cloud, SaaS, mobile, etc.), while other
businesses followed slowly behind. If you believe, as we do, that all companies
need to become software companies to compete, then traditional enterprises
cannot afford to wait. By the time the executive memo comes out, it will be too
late.

To help get the conversation started, I’ll outline three principles that
business and technology leaders need to internalize and incorporate as part of
any digital transformation strategy.


1. AI IS NOT A FEATURE

It’s tempting to look at AI as an enhancement to an existing product or use
case. Tactically speaking, this is probably true. For instance, at Nuvalence,
we’re helping our clients use state-of-the-art AI technologies to detect fraud,
run call centers, validate identity, or create chatbots. 

However, in the early days of the Internet, it would have been a mistake to
think of the Internet as a feature or extension of your existing business — for
example, brick-and-mortar delivery, a distribution mechanism for your newspaper,
or a way to buy DVDs online. The winning strategy was to completely re-imagine
how your business model and products worked in a world where internet access and
information delivery were nearly free and ubiquitous. The same will be true for
AI. What does it mean for your business in a world where access to cognitive
human labor is nearly free and ubiquitous?


2. AI REQUIRES A PLATFORM APPROACH

One thing that enables ChatGPT to do what it does so magically is that it has
been trained by large volumes of data at internet scale. Intellectual property
considerations aside, it’s clear that making it easy to access this training
data is essential to its effectiveness.

Compare this to a traditional enterprise, where knowledge workers can’t easily
locate the latest sales data from within their own line of business. This is
obviously not because search is an unsolved problem. Breaking down data barriers
between organizational silos is challenging, but AI success is highly dependent
on it. From a technical perspective, a platform approach is the only way to
solve this problem. Central to the effectiveness of Large Language Model (LLM)
based AI systems (on top of which ChatGPT is built) is the size and quality of
the dataset used to train it. Businesses and public sector organizations will
want to fine-tune models to adapt to domain-specific use cases important to
them. This might include health records, vehicle telemetry, or transaction
history at the DMV. None of this works if the data within an organization is not
easily accessible.


WHAT WE ARE TELLING ALL OF OUR CLIENTS TODAY IS QUITE SIMPLE: “IF YOUR DIGITAL
TRANSFORMATION STRATEGY DOES NOT DEEPLY CONSIDER THE IMPACT OF AI, IT’S DEAD ON
ARRIVAL.”

A platform approach prescribes that for all members in your LLM platform’s
multi-sided ecosystem they:

(A) Each can extend the platform and/or the underlying LLM (for example, by
injecting structured data sources as “knowledge” into the model or fine-tuning
the model itself)

(B) Have access to shareable, discrete unit-level LLM outputs via APIs.

As a practical matter, adhering to these two prescriptions guarantees that silos
can’t exist (with the assumption, of course, that the LLM turns highly
unstructured bits of contributed data into useful knowledge). Why can we make
such a guarantee? Because (A) requires members to contribute their data or
expertise to align the LLM with the knowledge that the LLM needs in order to
make the LLMs underlying knowledge architecture useful to themselves (and as a
consequence, to others) and (B) makes that knowledge accessible to themselves,
and as a consequence, democratizes access equally to all members. 


3. PLAY OFFENSE AND PLAY IT QUICKLY

When a new and disruptive technology emerges, organizations usually start by
considering how it might negatively impact their current business model and work
to defend it. This likely played some role in Google playing catchup in this
space despite originating much of the technology, though it would be foolish to
count them out. There are many other great examples of the innovator’s dilemma
in recent history, so this is not new.

What’s new with AI is the speed with which change and adoption are happening and
will continue to happen. Unless you are Google, a “fast follower” strategy is
likely a losing one. ChatGPT reached 100 million users within its first two
months of release. Notably, this was done without marketing, a wonky name, and
some significant limitations. Compare that to TikTok and Instagram, which took
nine months and 2.5 years, respectively, to reach this milestone. We must also
remember that ChatGPT is only the first platform, and it’s unclear if or how big
of a moat it really has. Much more innovation is coming (and coming quickly).
Organizations that mobilize immediately, even to simply experiment, will be in a
much better position to compete. 


WHAT LIES AHEAD?

As with most new and transformational technologies, it is nearly impossible to
predict where this wave of AI innovation will lead. This is no reason for
organizations to postpone considering the possibilities and getting to work now.

In 2003, Ray Kurzweil wrote:

“We’re entering an age of acceleration. The models underlying society at every
level, which are largely based on a linear model of change, are going to have to
be redefined. Because of the explosive power of exponential growth, the 21st
century will be equivalent to 20,000 years of progress at today’s rate of
progress; organizations have to be able to redefine themselves at a faster and
faster pace.”

Comparing the coming AI era with those that came before it will be tempting.
Even I have done so in this post to help define precedent. However, the AI era
will be, in retrospect, unprecedented. Fear of disruption can be a powerful
motivator, but truly ambitious organizations will view this as a generational
opportunity to redefine their industries.


INTERESTED IN LEARNING HOW WE HELPED A LARGE GOVERNMENT AGENCY IMPROVE THE
CITIZEN EXPERIENCE, REDUCE MANUAL LABOR, AND TRANSFORM ITS SERVICES WITH AI?

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