Getting Ready for the AI Bandwagon: How Business Users Can Actually Prepare Requirements for That “Magical” Technology

AI is the buzzword on everyone’s lips, from the boardroom to the breakroom. The promise is enticing: streamlined operations, pinpoint decision-making, and fresh insights that could launch your business ahead of the competition. But let’s be honest—simply shouting “We need AI!” during a strategy meeting won’t make it appear like a fairy godmother. To truly harness the power of Artificial Intelligence, you need something more down-to-earth: well-defined requirements.

These requirements serve as your blueprint, telling the AI what you actually need it to do. Without them, you’re just hoping the AI will guess your intentions, which is like expecting your smartphone to read your mind (spoiler alert: it can’t). Below, we’ll explore how business users—yes, that’s you—can create these all-important requirements. We’ll keep it human, add a touch of ironic realism, and leave you better prepared to avoid glazed-over stares and polite nods in your next AI-focused discussion.

1. Embrace Your Non-Expert Status

First things first: You’re not expected to be an AI genius. AI can sound intimidating and drenched in technical jargon, but as a business user, your value lies in knowing what the company needs, not the intricacies of neural networks. Accepting this fact frees you from the pressure to sound like a Silicon Valley whiz kid. Instead, focus on defining the real-world challenges AI could solve.

Best Practice:
Own your role as the problem-identifier. It’s better to say “We struggle to predict seasonal demand accurately” than to pretend you know how to “implement a recurrent neural network for time-series forecasting.”

2. Start with Real, Concrete Problems

Everyone wants AI—because who wouldn’t want a magical problem-solver? But you need to root that desire in reality. Identify specific business challenges:

Are you losing money because you can’t forecast inventory properly?
Are customer support teams drowning in repetitive questions?
Is your sales team wasting time on unqualified leads?

The clearer you define the problem, the easier it is to translate it into AI requirements.

Best Practice:
Write a clear, one-sentence statement of the problem you want AI to address. For example, “We want to reduce our average customer email response time from 24 hours to under 2 hours.”

3. Get Specific with Goals and Metrics

Vague aims like “We want better decisions” are about as helpful as “We want to be richer.” AI needs specifics to prove its value. Set quantifiable targets:

Reduce invoice processing time by 30%.
Improve lead conversion rates by 15%.
Achieve a 10% increase in inventory turnover accuracy.

These metrics let you measure success, making it easier to know if your AI project is delivering or just producing fancy dashboards.

Best Practice:
For every business goal, pair it with a metric and a baseline. If you currently respond to 80% of customer queries within 24 hours, aim for 90% within 12 hours post-AI implementation.

4. Understand Your Data Reality

AI is fueled by data. But if your data is scattered across 10 spreadsheets, half of them incomplete, and stored in formats from the Jurassic era, you’ve got homework to do. AI doesn’t magically fix bad data; it amplifies what it’s given. Figure out what data you have, assess its quality, and determine whether it’s sufficient and relevant.

Best Practice:
Conduct a quick “data audit.” Identify where relevant data resides (CRM, ERP, spreadsheets), its cleanliness (missing fields, outdated entries), and any sensitive information that needs protection. Prioritize improving data quality before finalizing your AI requirements.

5. Involve the Right People Early

Crafting AI requirements is a team sport. Bring together:

Business Users: You know the pain points and desired outcomes.
Data Specialists: They’ll tell you if that sales data from 2012 is actually usable or just clutter.
IT/AI Technologists: They translate your goals into technical specs and choose the right models or vendors.
Legal and Compliance Advisors: They ensure you don’t end up in a regulatory nightmare by using certain data or influencing decisions improperly.
Change Management/Training Teams: They plan for user adoption, training, and communication so people actually trust and use the AI tool.

Best Practice:
Hold a cross-functional kickoff meeting before locking down your requirements. Each stakeholder should confirm feasibility and raise any red flags right from the start.

6. Keep It Manageable

It’s easy to dream about AI that does everything—predicting trends, writing content, and maybe even making your morning coffee. But don’t bite off more than you can chew. Start with a single, achievable use case that’s both impactful and realistic. Win small, build credibility, then scale up.

Best Practice:
Pick a pilot project. For example, use AI to categorize and prioritize customer inquiries. If that project succeeds, move on to more complex applications like sentiment analysis or customer churn prediction.

7. Remember Ethics, Fairness, and Compliance

If your AI will influence who gets a promotion, who receives a loan, or which job applicant gets an interview, you must consider fairness from day one. Include requirements that mandate unbiased decision-making and transparency. The same goes for regulatory compliance—list any laws or industry guidelines your AI solution must adhere to.

Best Practice:
Add a “fairness and compliance” clause to your requirements, stating that the AI must avoid using protected attributes (like race, gender, religion) and must be explainable if challenged.

8. Document Everything

Yes, documentation might feel like a chore, but it’s crucial. Write down the business challenges, objectives, data sources, constraints, and what “success” looks like. This document becomes your north star, preventing misunderstandings and “That’s not what we agreed on!” moments later.

Best Practice:
Create a requirements document that includes:

Problem statement
Goals and metrics
Data sources and quality notes
Technical constraints and integration points
Ethical and compliance considerations
Expected outcomes and timeline

Share this document with all stakeholders for feedback before proceeding.

9. Stay Flexible and Iterative

Even the best-laid plans encounter surprises. Maybe the AI model needs more training data than you have, or maybe it suggests a more effective approach you didn’t consider. Be open to refining your requirements. AI is a journey, not a destination.

Best Practice:
Schedule regular check-ins with your project team and encourage feedback. If something isn’t working, adjust the requirements or scope. Rigidity won’t help if the landscape changes or new information emerges.

Additional Best Practices to Streamline the Process

User Acceptance Tests (UAT): Define scenarios where the AI solution’s output is compared against human judgment. Did it pick the right leads? Did it reduce your inventory error margin?
Explainability: Ensure your requirements include a demand for AI explainability. If the AI recommends increasing prices for a product, you should know why.
Vendor Vetting: If working with external vendors, include requirements for vendor transparency, support, and the ability to retrain or fine-tune models as your business evolves.
Continuous Improvement: Treat the initial implementation as phase one. Collect user feedback, performance metrics, and error logs. Use these insights to update your requirements and retrain models, continuously improving the solution.

The Bottom Line: Requirements as Your AI Roadmap

Think of your AI requirements like a roadmap. Without them, you’re hoping for magic. With them, you have a clear path to follow. They guide the AI team, ensure everyone’s aligned, and help you measure success once the system is live. Instead of guesswork and hype, you get a data-driven approach to integrating AI into your business operations.

So, before you unleash the buzzwords and sign off on an AI project, take a step back. Identify your real business needs, define your metrics, figure out your data situation, involve the right people, and be explicit about fairness and compliance. With solid requirements in hand, you’re not just hopping on the AI bandwagon—you’re steering it in the right direction.

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Posted

December 17, 2024

Business Strategy & Technology

chilitask

Tags:

AI, Business Process, Change Management, Compliance, Data Quality, Ethics, HR Technology, Requirements