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The Boardroom Scramble of 2023
I have spent the first half of this year fielding the exact same question from nearly every CEO, CFO, and board member I advise: “What is our AI strategy?”
Since late 2022, the enterprise technology space has been dominated by a scramble to find generative AI enterprise use cases that actually deliver business value. Executives are watching the consumer market explode with new tools and naturally want to know how to apply that power to their own operations. They want increased margins, faster cycle times, and reduced operational overhead.
The problem is that enterprise architecture operates on an entirely different standard than consumer applications. A hallucination in a consumer app is a funny screenshot on social media; a hallucination in an enterprise financial report is an SEC violation, a lawsuit, or a fired executive.
Through my dual lens as an IT strategist and someone with a Master’s in Accounting, I evaluate technology based on risk mitigation and verifiable return on investment. The current market is noisy. Software vendors are hastily rebranding their existing features with “AI” labels to justify increased renewal fees. To navigate this, organizations must separate the theoretical capabilities of Large Language Models (LLMs) from practical, deployable applications.
A Framework for Evaluating AI Opportunities
Before writing a single line of code or signing a vendor contract, enterprise leaders need a filter to evaluate potential AI implementations. I use a straightforward two-by-two matrix based on Audience and Autonomy.
- Internal vs. External: Who is consuming the output? Internal employees or paying customers?
- Human-in-the-loop vs. Autonomous: Does a human review the output before it is executed, or does the system act independently?
In 2023, the only safe quadrant for 95% of businesses is Internal and Human-in-the-loop. You are utilizing AI to draft, summarize, and synthesize. The human employee is the editor and the ultimate owner of the work. This approach dramatically reduces operational risk while still capturing massive productivity gains.
High-Value Generative AI Enterprise Use Cases
When you apply the framework above, several highly practical applications emerge. These are the generative AI enterprise use cases I am currently helping organizations prototype and deploy.
1. IT Operations and Code Generation
The engineering and IT departments are naturally the first testing grounds for these technologies. AI copilots for software developers are perhaps the most proven use case currently available. By integrating LLMs directly into integrated development environments (IDEs), developers can prompt the system to write boilerplate code, generate unit tests, or explain undocumented legacy scripts.
This works exceptionally well because code is a precise language. The developer acts as the immediate reviewer. If the AI suggests incorrect syntax, the code fails to compile or fails the unit test. The feedback loop is instantaneous. I have seen organizations increase their sprint velocity by 15% to 25% simply by equipping their senior developers with secure, enterprise-licensed AI coding assistants.
Beyond development, IT operations benefit from incident management summarization. When a severity-one outage occurs, the resulting chat logs, ticketing updates, and system alerts can span thousands of lines. Generative AI can synthesize this unstructured data into a concise, readable post-mortem draft for the Chief Information Officer, saving hours of administrative work.
2. Financial Analysis and Reporting Drafts
With an accounting background, I look at financial systems through a lens of strict compliance and accuracy. Large Language Models are famously poor calculators. You do not want an LLM performing your depreciation math or calculating your tax liabilities. However, financial reporting is only 50% mathematics; the other 50% is narrative.
One of the highest-value applications I have evaluated involves feeding validated, structured month-end variance data into a secure AI model to generate the first draft of the Management Discussion and Analysis (MD&A). The AI translates raw data—such as “Revenue +12%, COGS +15%, Marketing Spend -5%”—into a readable narrative draft explaining margin compression.
The human controller then reviews, refines, and adds strategic context. This separates the mechanical task of drafting from the high-value task of strategic analysis. It saves the finance team days of manual reporting work at the end of every quarter without compromising mathematical integrity.
3. Enterprise Knowledge Management
Corporate intranets are notoriously difficult to navigate. Employees waste countless hours searching through SharePoint drives, internal wikis, and fragmented HR portals trying to find the current travel policy, the exact procedure for a hardware request, or the latest product specifications.
Traditional keyword search fails when documents are poorly tagged. Generative AI, specifically through an architecture called Retrieval-Augmented Generation (RAG), solves this. By pointing a private LLM at your internal, classified data repositories, employees can ask natural language questions: “What is the approved per diem for a business trip to London, and what form do I need to submit?”
The system retrieves the relevant documents, synthesizes the answer, and cites the source document. The productivity unlocked by reducing internal search time is immense. More importantly, because the system is restricted to internal documentation, the risk of external hallucination is heavily mitigated.
4. Customer Support Agent Assist
Customer service centers face high turnover and complex ticket escalations. When a Tier 1 agent escalates a problem to Tier 3, the senior agent often has to read through an email chain spanning 40 messages over three weeks just to understand the context.
Applying generative AI to triage and summarize these interactions is incredibly effective. Before the Tier 3 agent even opens the ticket, the AI provides a three-bullet summary of the customer’s issue, the troubleshooting steps already attempted, and the customer’s current sentiment. The agent resolves the issue faster, improving customer satisfaction metrics and reducing cost-per-resolution. Notice that the AI is not speaking directly to the customer; it is empowering the human agent to perform at a higher level.
Use Cases to Avoid (For Now)
Equally important to knowing what to build is knowing what to avoid. In 2023, there are specific areas where the technology is simply not mature enough for enterprise risk profiles.
Fully Autonomous Customer Chatbots: Do not let an LLM speak directly and unscripted to your customers. The models are prone to “jailbreaking” (where users manipulate the prompt to make the AI say inappropriate things) and hallucinations. If an AI promises a customer a refund that violates your policy, you will likely be held legally liable for that commitment.
Mission-Critical Automated Decisions: Any process that independently alters pricing, terminates an employee, or shifts financial assets based on generative AI analysis should be strictly prohibited. The reasoning capabilities of these models are predictive, not logical. They predict the next most likely word; they do not understand financial or operational consequence.
Implementation, Governance, and Vendor Strategy
Identifying the right generative AI enterprise use cases is only the first step. Execution requires rigorous governance.
By mid-2023, the biggest risk is not that you fail to adopt AI; it is that your employees adopt it without you. Shadow IT has evolved into Shadow AI. When a financial analyst uploads a confidential Q3 forecasting spreadsheet into a public ChatGPT prompt to “format this data,” they have just breached your data classification policy. That financial data may now become part of the training set for the public model.
IT strategy must immediately focus on providing safe, sanctioned alternatives. This usually means securing enterprise agreements with major cloud providers (such as Microsoft Azure OpenAI or AWS Bedrock). These agreements legally ensure that your corporate data is isolated, is not used to train foundational models, and remains within your security perimeter.
Furthermore, vendor management is critical. Every SaaS vendor in your technology stack is currently pitching an AI add-on. Do not buy AI for the sake of AI. Force vendors to prove how their new features directly impact your operational metrics. If the vendor cannot articulate the specific workflow improvement, refuse the price increase.
Frequently Asked Questions
How do we prevent employees from leaking sensitive data to public AI models?
You need a two-pronged approach: policy and provisioning. First, update your Acceptable Use Policy immediately to explicitly forbid entering confidential, proprietary, or customer data into public AI tools. Second, provide a sanctioned, private alternative. Employees use public tools because they are seeking efficiency. If you provide an enterprise-secured chat interface that connects to private models, you remove the incentive to use unauthorized shadow AI.
Should we build our own LLM or buy off-the-shelf?
For 99% of enterprises, the answer is to buy or rent. Training a foundational model from scratch requires tens of millions of dollars in compute power and specialized AI talent that most organizations do not possess. Your competitive advantage is not the model itself; it is your proprietary data. Focus your budget on organizing your internal data and using APIs to connect it to existing commercial or open-source models.
How do we measure the ROI of these generative AI initiatives?
Initially, measure capacity creation rather than direct cost reduction. Generative AI in its current state rarely replaces an entire job function; it replaces tasks. If an AI tool saves your financial analysts 10 hours a week on reporting, the ROI is not measured by firing an analyst. It is measured by redirecting those 10 hours into deep variance analysis, strategic forecasting, and work that directly impacts the bottom line. Track cycle times, time-to-resolution, and employee output volume.
A Look Ahead: Sustainable Competitive Advantage
The organizations that will win this transitional period are those that treat AI as a core operational capability, not a science experiment. They are applying strict IT governance, focusing on human-centric workflows, and demanding clear business alignment before investing.
The initial hype of 2023 will eventually settle. When it does, the gap between companies that effectively integrated these tools into their daily operations and those that merely bought software licenses will be stark. Focus on the foundational use cases now. Clean your data, secure your environments, and empower your workforce to iterate safely. That is how you turn a technological wave into a sustainable operational advantage.
