For growth-minded CEOs and business owners, AI is quickly shifting from curiosity to competitive necessity. But like any powerful tool, it requires clarity, guardrails, and leadership judgment.
Contributed by David Nelms, Director of Technology Management, Warren Whitney
The use of AI in small to medium-sized businesses is increasing and will soon become a necessity. If you are a business leader in the early stages of integrating AI into your organization, read on to explore several key concepts and examples of how to implement different tools.
As a note, and in the spirit of transparency, I used AI to help draft this article. The value was not in having the tool “write” the piece, but in using it as a thinking partner. I started by outlining the structure and key ideas, then worked through the draft conversationally by asking it questions, refining language, and pressure-testing my thinking along the way. For business leaders, this is where AI can be most effective: not as a replacement for judgment or experience, but as a tool that clarifies ideas, speeds up iteration, and improves the quality of the final output.
Practically speaking, let’s dive in.
How Is Artificial Intelligence Being Used in a Business Context Today?
Artificial intelligence (AI) has moved rapidly from experimentation to practical adoption across organizations of all sizes. For small businesses in particular, AI now offers tools that can improve efficiency, enhance decision-making, and strengthen customer engagement—provided it is implemented thoughtfully and responsibly.
What Is a Large Language Model (LLM)?
A Large Language Model (LLM) is a type of artificial intelligence designed to understand and generate human language. LLMs are trained on vast collections of text—such as books, articles, and websites—to learn patterns in language, including grammar, context, and meaning.
Rather than possessing knowledge in a human sense, an LLM predicts likely responses based on statistical relationships learned during training. When a user provides a prompt, the model analyzes the input and generates language that aligns with those learned patterns. Importantly, LLMs do not inherently have access to live systems or proprietary business data unless they are intentionally connected to those sources.
What Is the Difference Between Interactive AI and Agentic AI?
Most AI tools in use today are interactive AI systems. These tools respond directly to user prompts and perform discrete tasks such as answering questions, summarizing information, or drafting content. Control remains with the user, and the system acts only when prompted.
Agentic AI represents a more autonomous model. Instead of responding to single requests, agentic systems are given objectives and can determine the steps needed to achieve them. They may gather data, invoke tools, analyze results, and iterate with limited human intervention. While agentic AI offers meaningful efficiency gains, it also introduces greater operational, security, and governance considerations.
What Are Practical Ways Business Leaders Can Use AI Today?
For businesses with limited staff and resources, AI can function as a force multiplier. Common and high-impact use cases include:
Content creation and marketing
AI can generate first drafts of blog posts, email campaigns, social media content, advertisements, and product descriptions, accelerating production while allowing teams to focus on refinement and strategy.
Customer support and engagement
AI-powered assistants can handle routine customer inquiries, FAQs, appointment scheduling, and basic troubleshooting, improving responsiveness and freeing staff to address more complex needs.
Data analysis and business insights
AI can analyze sales, customer behavior, operational metrics, and financial data to identify trends, surface insights, and support forecasting, enabling more data-driven decisions.
Internal productivity and knowledge management
AI tools can summarize documents, extract key points from meetings, draft internal reports, and help employees locate information more efficiently.
Sales support and forecasting
AI can assist with lead prioritization, customer segmentation, and demand forecasting, helping sales teams focus on the highest-value opportunities.
Process automation
Repetitive administrative tasks such as data entry, invoice processing, and workflow routing can be automated or streamlined, reducing errors and improving efficiency.
Key Governance Considerations Before Implementing AI
To realize these benefits responsibly, businesses should address several critical considerations as part of their AI strategy.
Policies and guidelines
Organizations should establish clear internal policies that define acceptable AI use, prohibited activities, accountability, and escalation paths to ensure consistent, responsible adoption.
Data security and confidentiality
Businesses must understand how AI tools handle data, including storage, retention, and third-party access. Sensitive information—such as customer data, financial records, or proprietary business details—should only be used with tools that meet defined security and contractual standards.
Training and education
Employees should be trained not only on how to use AI tools, but also on their limitations. Training helps reinforce appropriate use, effective prompting, and sound judgment. Users can also ask the AI tools themselves for guidance on how to improve prompts or generate better outputs, supporting skill development over time.
Data quality and verification of results
The usefulness and reliability of AI outputs depend directly on the quality of the input data. Businesses should ensure that inputs are accurate, complete, and up-to-date. Regular review and verification of AI outputs is essential to detect errors, inconsistencies, or bias, especially for outputs that influence decisions, customer communications, financial analysis, or legal and regulatory matters.
Reputational risks
Inaccurate, biased, or inappropriate AI-generated content can erode trust with customers and partners. Safeguards should be in place to ensure AI use aligns with brand values.
Selecting the right tools for the right purpose
AI tools vary widely in capability and risk, making tool selection critical. Paid versions of tools often offer greater capabilities, integrations, and security features than free versions, which can be an important consideration for business use.
- General-purpose LLMs (e.g., ChatGPT, Claude, Gemini, Copilot) Best for content drafting, summarization, brainstorming, and general productivity. Typically unsuitable for highly sensitive data unless deployed in secure environments.
- Customer support chatbots (e.g., Zendesk AI, Intercom AI, Freshdesk AI) Best for routine inquiries and first-line support when tightly scoped and connected to approved knowledge bases.
- Data analysis and BI tools with AI features (e.g., Power BI with Copilot, Tableau, Looker) Best for analyzing structured business data and generating insights, with results subject to review.
- Marketing and creative AI tools (e.g., Jasper, Copy.ai, Canva AI) Best for generating marketing copy and visual assets, with outputs reviewed for accuracy and brand alignment.
- Workflow automation and agentic tools (e.g., Zapier with AI, Copilot Studio) Best for multi-step processes and cross-system automation, requiring stronger governance due to increased autonomy.
What Is the Best Way for Businesses to Start and Scale AI Adoption Responsibly?
AI is becoming a foundational capability for modern organizations, and its capabilities continue to evolve rapidly. Each organization will adopt AI in different ways based on its goals, constraints, and risk profile, making it important to develop a deliberate strategy rather than pursuing ad hoc use.
At the individual level, effective use of AI improves with practice. As employees become more comfortable framing requests, evaluating outputs, and applying results, overall effectiveness increases. At the same time, AI technologies themselves continue to advance, meaning results will improve over time.
The organizations that will benefit most from AI are those that pair technological capability with disciplined leadership, clear governance, and ongoing learning. There are a wide range of vendor partners are available to support training and education, development of internal policies and guidelines, and the secure implementation of AI tools, to help realize value while managing risk.
Navigating AI responsibly is ultimately a leadership challenge. The best CEOs don’t do it alone—they seek perspective, ask hard questions, and learn alongside other experienced leaders.
Thank you to David Nelms and the rest of the Warren Whitney team for contributing this article. You can connect with their team by contacting Kyle Ficker, kficker@warrenwhitney.com or by calling (804) 282-9566.
