How to identify your first data and AI use-case + some examples you might find in your organisation
In our previous blog post, Embracing Data and AI: A Practical Guide to Getting Started, we explored the foundational steps for adopting data and AI. Now that you’ve learned what steps are needed to create insights from data, you’re ready to begin with step one: identifying and understanding valuable use cases!
The key to success lies in starting with clearly scoped, high-value projects that can deliver quick wins. By focusing on projects with well-defined boundaries, businesses can experience the benefits of data and AI without diving into complex, organization-wide implementations too soon.
Why Start with Clearly Scoped Projects?
A common mistake in getting started with data and AI is to choose problems that are too large or complex for the organization’s current data maturity. While solving these problems may offer significant long-term value, tackling them too soon can lead to failure, discouraging future attempts and slowing progress toward becoming a more data-driven organization. You need to walk before you can run!
Clearly scoped use cases should check a few key boxes:
✓ They solve a pressing problem.
Solving a pressing problem will motivate teams who see the results to get enthusiastic about using data and AI in their own work. This is a great way to gain traction for your broader data and AI strategy.
✓ They are feasible with your current data and technology stack, or require minimal adjustments.
Adopting new technology takes time and can be costly. Unless you’re already running several experiments across different teams, you don’t need to build a big data platform right away. The quickest and most cost-effective solution is to leverage the tools you already have—almost everyone has access to a computer, so start there!
✓ They use a maximum of two data sources.
It can be tempting to integrate multiple data sources immediately. While this can enrich your data, even a simple dashboard using one or two data sources can deliver significant value. Start small, and as you build from there, avoid overcomplicating things by adding too many tables, databases, or files.
✓ They provide measurable value within a reasonable timeframe.
Measure the situation before implementing the solution and again after. For example, how much time does someone save by not having to manually combine two Excel files? That saved time becomes your measurable business value!
Steps to Identify Your First Use Case
So, how do you find these use cases? At Wolk, we typically follow four steps to discover them:
Map Out Key Pain Points
Identify areas in your business that feel “manual” or slow to deliver insights. This often includes operational processes, reporting bottlenecks, or manual forecasting. Teams where this type of work is common include finance, procurement, warehouse management, and sales and marketing. These repetitive tasks are prime candidates for automation or better decision-making using data.
Start with descriptive analytics, and move up gradually
Not every pain point is a good fit for AI right away. Focus on use cases where a clearly scoped solution can deliver quick, meaningful results. The smaller the project, the quicker the outcomes. Generally, if you are not experienced with building complex data products, a dashboard that describes something about historical data will already be of immense value (descriptive analytics). For example, an automated report on all goods bought and sold in the past year. If you are familiar with those types of data and models, you can move up the complexity scale.
Assess Your Data
The success of any data or AI project depends on the quality of your data. Do you have enough? Is it clean and structured? A good rule of thumb: if a person can manually produce the results you want with the data you have, you can likely automate that process to at least the same level of accuracy. Ensuring data quality now lays the foundation for scaling projects in the future.
Define Success
What will success look like? Set measurable goals from the start. For example, success might mean reducing operational costs by 15% or improving forecast accuracy by 20%. A popular goal for early data and AI projects is saving time on repetitive, tedious tasks. Freeing up time for higher-value work is a win for everyone. Measuring time spent producing reports or making decisions can be a simple but impactful metric.
Examples of common low-complexity, high-value use cases
Purchasing and inventory management: Most organisations that handle physical inventory struggle to get a grip on stock-levels, outstanding orders with suppliers and required items for production or sales. This can be a very complex problem, but often starts out by simply counting what is currently in stock and what is currently in order. In our experience, organisations tend to manage this information in multiple systems or Excel files. Create 1 report on for example outstanding orders, and automate the process. Just the current orders, no forecasting or other fancy stuff. When successful, add more data from ERP or sales.
Sales and accountmanagement: Make an automatic report that updates all account managers on sales figures every week, based on data in your CRM. This will save time for the sales team, and allow them to make better informed decisions.
Finances: A lot of finance departments still rely heavily on manually combining Excel reports or CSV exports from all kinds of systems. Help them out by automating that reliably, increasing trust in the numbers and saving them time!
Research and Development: Create a database or filesystem where R&D engineers can share and work on data. We find that a lot of engineers still rely on local files and visualisations, which is a cause for low data quality and multiple versions of “the truth”. Host a small instance of Grafana for them, and you’ll definitely help them out by enabling smooth collaboration and delivering high-quality data!
Get Started!
Starting with a clearly scoped, high-value use case that has low complexity is a smart way to introduce data and AI into your business. Whether it’s automating processes, enhancing reporting, or making better predictions, these projects are manageable and can deliver fast, measurable results. Once you’ve seen success with an initial project, you can gradually move on to larger, more complex initiatives.
Ready to get started with data and AI, but unsure of how to identify the right use case? Or maybe you have a clear vision but need technical help? Message me on LinkedIn, or reach us at hello@wolk.work and we’ll help you get on the path to smarter decisions and better outcomes.
