The past two years of A.I. exploration have brought forward a new ‘language of information’ with A.I. capabilities and platforms making a broad push into strategic thinking and transformation efforts. Being conversant in the A.I concepts and principles is a strong first step toward making informed decisions on the potential value of A.I. in a company and how (and when) to leverage it. That all starts with “speaking the language.”
Become Fluent in AI
Let’s break down some of the commonly used terminology that business leaders need to understand to discuss AI. With a deeper appreciation for these concepts—and how they materially impact your business’ AI strategy—conversations centering on AI can be more informed, strategic, and fruitful.
AI Fundamentals
LM
Large Language Models (LLM) are AIs designed to process and generate language. Virtually all commercial AI models are LLMs. That’s because most users interface with AI using language-based requests.
Value Alignment: Understanding LLMs is critical as these models enable natural language processing, which underpins everything from automated reporting to client-facing chatbots, accelerating business communications and data-driven decision making.
Generative AI
Any AI that produces images, language, video, or code is a generative AI. This may be, in some cases, distinct from Agentic AI which is designed to automate tasks without necessarily processing or generating language.
Strategic Value: Generative AI supports innovation by enabling new ways to create marketing collateral, automate documentation, or generate code, giving businesses a competitive edge in content-driven markets.
Agentic AI
Agentic AI refers to programs that autonomously accomplish tasks. Today, agentic AI is most often used to recognize and respond to certain narrowly defined conditions within software development, customer service, cybersecurity, and business automation processes.
Value Alignment: Agentic AI can streamline operations, improve response times, and reduce costs by automating repetitive tasks, thus allowing your teams to focus on higher-value activities.
Prompt
Prompts are the user inputs, typically in the form of a text request, that an AI interprets and processes.
Strategic Value: By mastering prompt engineering, organizations can maximize the productivity and accuracy of their AI investments, ensuring outputs are aligned with corporate objectives and operational needs.
RAG
A RAG, or “retrieval-augmented generation” defines approved reservoirs of information for the AI program to draw from.
Value Alignment: By governing what data the AI can access, RAG frameworks increase the reliability and security of AI-generated insights, enabling compliance with data privacy and industry regulations.
Platform
An AI platform refers to the specific model (for example, version 1.0, version 2.0) and configuration (or aftermarket customization) of a program. ChatGPT and DALL-E are both platforms released by OpenAI. Copilot is Microsoft’s AI. Each AI platform has its own capabilities, quirks, and nuances which can change as the algorithms behind these AI models evolve from generation to generation.
Strategic Value: Selecting and deploying the right AI platform is a pivotal in aligning technology investments with business strategy and ensuring that AI adapts and scales alongside business needs.
AI Risks and Mitigation Strategies
Shadow AI
Shadow AI refers to the use of unsanctioned AI programs, a major risk accompanying AI’s rapid adoption. Any AI that is outside of the scope of an organization’s AI governance policy but accessed within its IT environment, or which uses its data, is a potential security risk. Learn more about Shadow AI, and the risks associated with it.
Risk Mitigation: Implementing rigorous AI governance is essential to prevent unauthorized usage, safeguard sensitive data, and uphold the organization’s compliance posture.
Hallucinations
For a variety of reasons, AIs sometimes output unreliable information or analysis. Inaccuracies in AI output are referred to as hallucinations, in part because AIs cannot always recognize their mistakes even when alerted to them. Hallucinations are one reason AI training data must be curated, and outputs must be checked and verified.
Strategic Risk Management: Building internal review processes and integrating human oversight is necessary to ensure that AI-generated insights can be relied upon for high-stakes business decisions.
Technical choices: Building in data analysis and content/response controls helps minimize the risks hallucinations can present. By using RAG (Retrieval-augmented Generation) – an advanced technique to configure A.I. systems – companies can minimize the impact of hallucinations.
Bias
Just like with people, AI can harbor unrecognized bias that is revealed in its responses. Typically, this is associated with an AI trained on data that is not carefully groomed to mitigate any potential biases within.
Governance Value: Addressing AI bias through careful training data selection and ongoing evaluation helps mitigate reputational risk and supports organizational commitments to fairness and inclusion.
Additional Strategic Considerations
As AI becomes a mainstay in business processes, leaders must recognize the importance of establishing robust frameworks for AI governance, ethical use, and continuous education. Investing in ongoing training for executive teams and employees will be vital as the landscape continues to shift. Executives should also cultivate close collaboration between IT, compliance, legal, and business units to ensure that AI initiatives align with both operational goals and regulatory requirements.
Conclusion
To understand where AI’s benefits suit your business, leaders and executives need to be fluent in the industry’s common terminology. As the technology evolves and its position within the corporate world changes, we will introduce and explain new terminology to keep you current. In the meantime, if you have any questions about your AI policies or strategy, contact CBIZ today.
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