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From Models to Infrastructure, Here's How to Navigate the AI Market

In the last decade, AI moved from science fiction to reality. The once-theoretical technology recently catapulted into the foreground of business and culture, largely thanks to revolutions in large language models, like ChatGPT. Companies are investing in AI at a remarkable rate, with private spend in the U.S. reaching nearly $50 billion in 2022, according to Stanford University’s 2023 AI annual report. Healthcare, fintech and data management are leading the way as they explore how this technology can help with everything from robotic process automation to virtual customer service agents.

AI INVESTMENTS IN 2022:

$6.1 billion

MEDICAL AND HEALTHCARE

$5.9 billion

DATA MANAGEMENT, PROCESSING, AND CLOUD

$5.5 billion

FINTECH 

Nearly any business can profit from thoughtful AI integration so long as they are prepared to create the data engineering and governance foundations necessary to use the technology effectively. With so many types and models out there, it can be difficult to know where to start. Careful consideration of data infrastructure, company priorities and governance can help you determine which AI solutions are right for your organization at this time. Companies considering investing in AI need to make two choices: what model to use and how best to support it. Answering key questions at the outset can help narrow your scope and make those decisions easier.

POPULAR USES FOR AI IN 2022:

0 %
ROBOTIC PROCESS AUTOMATION
0 %
COMPUTER VISION
0 %
NATURAL LANGUAGE TEXT UNDERSTANDING
0 %
VIRTUAL AGENTS

Choosing the Right Type of AI

Since OpenAI’s ChatGPT service released in November 2022, large language models (LLMs) have become the AI topic of conversation. Within six months, people visited ChatGPT 1.8 billion times. Together, they enter more than 10 million queries into the online portal every day. They use the tool to solve all sorts of personal, social and work-related problems, from writing sensitive emails to creating sales pitches and documenting code.

Large language models have the potential to revolutionize business, but that does not mean they are the most effective tools to meet your company’s needs. Adopting new technology is important, but it is even more important to use the right tool for the task at hand. Here’s a look at the strengths and weaknesses of some common AI solutions.

Large Language Models

EASY-TO-USE TOOLS FOR CONTENT GENERATION

ChatGPT and other LLMs can help marketers refine product descriptors and healthcare providers improve patient connections. These tools can boost engagement, increasing efficiencies and create a targeted experience.

MAIN TAKEAWAY

LLMs are quite powerful and can benefit almost anyone in a company. However, they are not the right solution for every problem. The purpose and use of an LLM should be carefully determined to maximize benefits and minimize risks.

Small Language Models

EXCELLENT SOLUTIONS FOR SPECIFIC PROBLEMS

Small language models are like large language models…but smaller. They are good at handling specific subjects or tasks, such as identifying parties in a legal contract, classifying documents or translating passages to another language or style of writing. For example, their narrowed scope can help retail companies reduce labor costs and human error while improving speed to customers with specific needs.

MAIN TAKEAWAY

Small language models can be excellent tools to create, edit or analyze writing and other content. However, they require specific training and expertise to maintain.

Other AI Tools

THE WORLD BEYOND LANGUAGE MODELS

AI can generate actionable insights from data beyond documents, from computer vision tools identifying supply chain problems to RPA hyperautomation of entire business streams. Industries like insurance can tap into these AI tools to generate accurate pricing and refine predictive models.

MAIN TAKEAWAY

AI is much broader than language models. Just because your organization does not need any help writing, managing or summarizing documents does not mean that AI cannot provide value.

 

Traditional Machine Learning and Statistical Solutions

SMALL-SCALE SOLUTIONS FOR DATA INFERENCE OR CLASSIFICATION

Sometimes simple solutions can solve problems just as effectively as more complex AI options. Random forest classifiers, clustering algorithms and even simple regressions can provide information to improve decision-making and increase productivity. Financial services companies can use statistical modeling to inform trading research.

MAIN TAKEAWAY

Not all problems require big, elaborate solutions. For a basic task, a simple tool may be just as effective as the cutting edge in technology.

 

Determining the Best Support Strategy

Once you decide what AI tools are appropriate for your organization, it is important to consider what infrastructure will best support them. First and foremost: will you use an internal or external support model? Companies can train and maintain AI in their own environment, or they can choose to use one of the many out-of-the-box toolsets available through cloud AI integration, such as AWS and Azure services.

Company-Maintained Models

INTERNAL OPTION PROVIDING GREATER CONTROL AND FLEXIBILITY

Maintaining AI does not have to mean creating something from scratch. Many open-source models can be adapted with proper data and training to meet your organization’s needs and protect against external threats.

MAIN TAKEAWAY

Internally maintained models are ideal for organizations that have very specific or technical needs from generative AI and are willing to invest in technical expertise.

 

Custom solutions to achieve your goals

No matter what AI use cases you find interesting, what models you want to use or how you want to support it, Kforce Consulting Solutions can help set your organization on the path to useful AI integration.

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