2023 was the year of AI enterprise adoption, with 55% of organizations adopting AI
into their workflows, according to a recent report from McKinsey &
Co. This adoption has been led by Large Language Models (LLMs) that
promised to fulfill numerous use cases across the digital workplace.
However, the failure of LLMs to live up to their hype will be the story
of 2024, as generic models become relegated to consumer-centric
applications and enterprise users turn to smaller, more targeted AI
models, purpose-built to meet their business needs.
Over the past year, companies have shown their willingness to experiment
with AI, but long-term success relies on the ability of AI to solve
specific business problems and achieve positive outcomes - and LLMs are
failing to meet those expectations. There are growing concerns around
the quality, accuracy, and security
of these models, to the extent that companies are already prohibiting
employees from using ChatGPT to shield their data, and the broader
market is filing lawsuits to prevent the use of their data for model training.
This calls into question the long-term sustainability and financial
viability of LLMs, which take billions of tokens to train. Without a
steady influx of good, clean and cheap data, it will become increasingly
difficult and expensive to build, deploy, and refresh models. Adding to
this pressure is the ongoing GPU shortage, impacting the computing
capabilities and running costs of AI models, with some companies having
to wait almost a year
to access these chips. Combined with challenges of hallucinations, data
privacy, ethics, data traceability, and responsible AI, you have a
perfect storm of headwinds facing LLMs going into 2024.
While some predict a slowdown in AI adoption as a result of these
challenges, at Aware we predict the opposite. Instead of giving up on
AI, businesses will look for more accurate, cost-effective and custom
models that solve real, complex business problems.
Trends to Watch in 2024
AI's purpose shifts from research to ROI
LLMs were created by research teams exploring the capabilities of AI
technology, rather than as models designed to solve specific business
problems. As a result, their capabilities are broad and shallow -
writing a fairly generic email or press releases, for example. For the
modern business, they have limited capabilities beyond that, requiring
more data to produce results with any depth.
While the AI landscape used to be dominated solely by OpenAI, major
names in the tech world are beginning to outperform ChatGPT with their
own LLMs, including Google's new Gemini model. However, due to the broad
capabilities of these new large language models, the text and
image-based benchmarks used to determine the model's prowess were just
as general. These benchmarks ranged from simple multi-step reasoning to basic arithmetic.
If an AI company's gauge for a successful Generative AI platform
is how correctly it can complete rudimentary math equations, that has
little to no relevance for the work of an enterprise organization.
Realizing this, companies will increasingly prioritize AI solutions
designed and built to solve real use cases and drive tangible ROI.
Companies truly recognize the value of their data
Data serves as fuel for LLMs - data traditionally sourced from end-user
prompts, books, articles, social media sites and more. This method of
training models provides the broad plane of knowledge LLMs are known
for, but raises data leakage and security concerns. Failure to manage
this data's usage creates blind spots that bad actors can attack and
jeopardize a company's place in the market. These blind spots can be
found in almost any internal data reservoir. Worst of all, this
accessible data could house troves of personal identifiable information, leading to serious future compliance issues. To address these issues, as many as 75% of businesses worldwide
are beginning to prohibit the use of LLM solutions like ChatGPT in
hopes of identifying solutions that can better protect their ingested
data.
This emerging drive to secure proprietary data has brought the sheer
volume of enterprise data to the forefront. This data exhaust,
originating from anything from collaboration data to support tickets,
holds deep insight into the risks and opportunities that sit within a
business. Recognizing the value of the data they hold, companies will
seek to secure it by taking a "hybrid cloud by design" approach, rather
than "hybrid cloud by default." Ultimately, data protection will emerge
as a key pillar in a successful AI strategy, and companies will move
towards prioritizing AI solutions that are trustworthy and responsible.
Beyond internal data troves, companies in 2024 will begin using AI
Models to proactively analyze external sources, like Reddit, in order to
gauge customer, employee and the general public sentiment. This data is
begging to be harnessed and companies will be looking for an
opportunity to extract these insights. By analyzing content on public
external platforms, companies could be made aware of issues months in
advance. The same is true for competitors, who will be able to harness
this publicly available data, giving them a leg up on the perceptions of
their biggest competitors.
Hybrid and targeted AI Models supplements - and maybe overtakes - LLMs
The push toward AI that drives value for enterprises will force
companies to pursue hybrid strategies - the coexistence of open-source,
closed-source and custom targeted language models trained on very specific internal data sets for very specific use cases.
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Targeted Language Models like Aware are like classic car restorations
built on a unique knowledge and through a set of unique experiences.
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Closed-source platforms such as Anthropic's offerings are like luxury
sedans, providing power and security alongside a concierge service.
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Open-source options (Hugging Face, for example) are like Formula One
race cars - fast and agile, but require technical expertise.
Each of these models house their own benefits, with closed-source
bringing security, industry models bringing specificity, and open-source
bringing agility for those that have the technical resources to use
them.
This will be especially important when the company's proprietary or
sensitive data requires stricter controls to meet compliance and legal
obligations. Targeted models can help teams develop intellectual
property around machine learning as a competitive advantage, training
them on closely curated datasets while reducing reliance on large
engineering teams or GPU instances that can add cost and complexity.
Combined with the judicious use of larger AI models when appropriate,
businesses can invest in solutions that fulfill their specific needs.
The rise of MLOps to manage new workflows
MLOps businesses offer managed infrastructure, running AI and ML models
that provide simpler management and reduced operating expenses. As the
nascent market matures, customers will elect their preferred deployment
option. Data Teams will become software teams. DevOps
created a movement within software development that empowers developers
to run the software they wrote. The same thing is happening in data.
Products have filled those needs by mapping each of the core functions
and responsibilities in the MLOps movement. The most sophisticated data
teams run like software engineering teams with product requirement
documents, ticketing systems and sprints so they can create efficient
and scalable models. And with the MLOps market size increasing by nearly 40% by the end of the decade, End-to End Data Science and ML Ops functions will emerge as a hot career path.
Human-Centered Intelligence. AI makes enterprise data actionable
Data is everywhere, and it's growing exponentially. As businesses get a
handle on managing their data and using it to train AI models, they must
also consider what they will do with the results AI provides.
Decisions, not dashboards, will be the yardstick by which AI is
measured. The ability to make decisions faster and with greater
certainty will fuel the future of AI as businesses continue to iterate
and refine models that drive progress with precision.
As a result, Humans & AI will emerge as partners within enterprise
work environments, not rivals. The outputs of AI should amplify, not
replace, workers' natural abilities. When organizations harness AI to
augment human capabilities, they unlock tremendous value by scaling
human-centered intelligence across the enterprise, driving greater
efficiency and dependability from a workforce.
Before companies can derive results from AI-driven platforms, it is most
important to determine if their choice of data platform is a) accurate
and b) cost-effective. This is where targeted AI models shine, with
performance metrics from these models providing over 85% fewer false negatives and false positives when tested against LLM competitors.
On top of greater accuracy, narrowly trained models prove more
cost-effective than large-scale LLMs; platforms like Llama-2-13b's
operating budget can reach a staggering $182,000 per month, whereas
targeted offerings cost below $1,000 per month.
ROI is everything to the enterprise. No matter how great the allure of
AI may be, it has to bring a clear benefit to a workforce, not just be
another line on a budget sheet. This can only come from internal data
troves lying in wait to be utilized effectively.