Choosing Your First AI Application: A Detailed Roadmap for Organizations

This article is based off the transcript of my video https://youtu.be/0Ly34nFESQk and edited using AI with some human love after.

Artificial intelligence has quickly shifted from being a futuristic idea to an essential capability for nearly every industry. Yet for many organizations that haven’t yet dipped their toes into AI, the question of where to begin looms large.

Choosing your first AI workload isn’t a simple task. The sheer number of options—especially with generative AI’s meteoric rise—can make the process overwhelming. Generative AI is powerful, creating content such as text, images, video, and audio through natural language prompts. It offers creative, conversational, and often interactive capabilities that can fundamentally reshape how organizations operate.

But this isn’t about adopting AI for AI’s sake. It’s about selecting a use case that delivers measurable business value while laying the groundwork for future success. Let’s dive into the steps, considerations, and common pitfalls when choosing your first generative AI workload.

Why Generative AI, and Why Now?

Generative AI is only one branch of AI. Others—like vision systems, speech-to-text models, classification engines, or predictive machine learning, to name just a few—might be better suited depending on your business needs. But generative AI stands out because it brings something new to the table: the ability to create. It can draft reports, generate code, summarize content, and even assist with research or brainstorming.

Think of it as a new tool in the organizational toolkit. When paired with your existing processes, it can unlock entirely new ways of working. The key question is: What kinds of problems does this new creative ability solve best for us AND which one to start with?

Step Zero: Organizational Readiness

Before even thinking about workloads, organizations must ensure they’re ready to handle AI responsibly and effectively. This readiness spans across skills, data, and governance.

1. Employee Skilling

Generative AI requires more than just technical developers. It involves governance, security, operations, legal teams, monitoring, end-users, leadership and a lot more! Your developers need the right tools and expertise to integrate AI models into workflows. Meanwhile, non-technical teams must understand how AI affects data handling, compliance, and risk management. Consider building internal training paths and upskilling plans early.

2. Data Readiness

Data is the fuel for AI, but not all data is ready for this kind of use. Generative AI introduces challenges because natural language interfaces make hidden data suddenly discoverable. Weak permissions, overly shared files, or unsecured data repositories become risks. Before applying AI, conduct a data hygiene exercise—clean up, properly permission, and secure your data.

3. Content Safety and Evaluation

Generative models are non-deterministic: the same input won’t always yield the same output. That unpredictability demands new approaches to content safety and quality checks. Organizations need:

• Content filters and safety systems to prevent harmful or irrelevant outputs.
• Evaluation frameworks to test for tone, compliance, and alignment with business goals.
• Responsible AI principles to prevent bias and ensure fairness.
• Governance and monitoring to meet both internal and external regulations.

Without these foundations, even a promising AI project can go off track.

Exploring Common Use Cases

Generative AI can be applied in countless ways. Popular starting points include:

• Summarization: Distilling long documents, transcripts, or meeting notes into concise summaries or slide decks.
• Chatbots: Conversational interfaces for example internal HR policies or external product catalogs.
• Coding Assistance: Tools that write or debug code, enabling both developers and non-technical staff to "talk" their way into creating functionality.
• Report Generation: Drafting reports or documentation from raw data sets.
• Research and Reasoning: Performing deeper multi-step analysis across large datasets.

These examples demonstrate potential. But the first use case must strike the right balance of business impact, feasibility, and safety.

Criteria for Selecting Your First Workload

1. Tangible Business Value

Your first AI project needs to move the needle in a way you can measure. Focus on solving a real business problem:

• Can it increase revenue?
• Reduce operational costs or improve efficiency?
• Improve employee or customer experience?

And most importantly, can you quantify the impact? Whether it’s reduced time-to-resolution, improved satisfaction scores, or increased conversions, measurable outcomes will help demonstrate real value and secure future AI investment.

2. Data Readiness

Assess the data needed for the workload:

• Is the data clean and unbiased?
• Are permissions properly set to avoid oversharing?
• Can the AI model efficiently access the necessary data?
• Where is the data? We will likely need new types of index and access from our AI applications.

Poor data quality or governance will amplify problems rather than solve them.

3. Technical Feasibility

Don’t start with the most complex project. Your first workload should be achievable:

• Use pre-built APIs or cloud-hosted models instead of building from scratch.
• Ensure the workload fits seamlessly with your current infrastructure and tools.
• Have the right team: developers, data engineers, prompt engineers, and DevOps.

4. Ethical Implications

Avoid ethically sensitive scenarios for your first deployment. Loan approvals, medical decisions, or anything involving life-impacting outcomes are not the place to start. Begin with low-risk, non-sensitive data and processes.

5. Scalability and ROI

If the project succeeds, can it scale it across infrastructure, teams and functions? Consider ongoing operational costs versus long-term value. Build in metrics to track ROI from the outset. We need success metrics that have been agreed up front. Think of measures like:

• x hours timed saved for this role
• y% conversion of online interactions to sales
• Handle z% of support calls by AI or reduce the time of a call by z% with AI assistance

We need to show cost-benefit. We commonly see an ROI of $3.70 for every $1 spent but that is not guaranteed and amplifies the need for careful planning and assessment.

For my first project I want something that is in the intersection of all 5 of these key criteria!

Real-World Examples of Ideal First Projects

1. RFP Drafting Assistant Organizations often waste countless hours responding to requests for proposals. By leveraging past RFP responses and success data, a generative AI assistant can draft initial responses, which humans can then refine. This saves time while maintaining quality.

2. Customer Support Copilot AI can transcribe support calls, retrieve knowledge base answers, and suggest responses to agents in real time. This reduces call times and can improve customer satisfaction.

3. HR Chat Assistant Upload your HR manuals and policy documents into a retrieval-augmented chatbot. Employees can then get answers instantly, reducing time spent navigating HR portals from 20 minutes to just a few.

In both my first examples you'll notice there is a human in the loop! In my last example it's for an internal audience but would show references to the source for validation. I'm minimizing my risk.

Identifying Good Use Cases

The best first workloads often share one or more of these traits:

• Repetitive and time-consuming tasks
• High frequency of requests or interactions
• Processes involving multiple systems or data sources
• Areas prone to human error
• Opportunities for human-in-the-loop oversight

From Pilot to Maturity

The creative nature of generative AI introduces huge new abilities but new concerns over the possibility of hallucinations, over sharing, inappropriate responses, not staying on task. We use capabilities like content safety and grounding detection, we use evaluations to validate the output, we will still follow normal testing and safe deployment processes BUT we still need to build our ability and confidence.

The maturity of AI adoption often follows three stages:

1. Assistant to a Human – AI augments human tasks but always requires review.
2. Member of the Team – AI operates with limited oversight, contributing independently but with checkpoints.
3. Autonomous Agent – AI executes tasks automatically, governed by rigorous monitoring and evaluation.

Start at stage 1, prove value, gain confidence and grow from there.

Final Thoughts

Launching your first AI workload isn’t about creating a flashy innovation. It’s about choosing a project that is safe, practical, measurable, and impactful. By focusing on business value, ensuring data and organizational readiness, and keeping humans in the loop, you set your organization up for sustainable success.

Your first AI project doesn’t need to be glamorous. It just needs to work—and show results.

Good luck as you take this first step into AI-driven transformation.