Hesham Khalafallah
In my previous article, I wrote about the importance of turning AI from recommendations into practice, and of starting with a real problem and a small application. The natural question then came to me from more than one executive: “Fine, but where exactly do we start?”
Here are five practical starting points that I believe suit most organizations, from universities and training centers to companies and government bodies. For each one: the problem it solves, what it requires, and how to measure its results.
1. Answering repetitive inquiries
The problem: a large share of staff time goes into answering the same questions every day: schedules, procedures, fees and required documents.
The solution: a smart assistant trained on the organization’s own guides and FAQs, answering users or staff around the clock and routing complex cases to the right person.
How to measure it: the number of inquiries answered without human intervention, response time and user satisfaction.
2. Summarizing and reviewing documents
The problem: long reports, meeting minutes, contracts and correspondence take hours to read and extract what matters.
The solution: tools that summarize documents, extract key points, decisions and deadlines, and flag what needs review, with the final review remaining with the specialist.
How to measure it: the time a review took before and after implementation.
3. Preparing content and correspondence in more than one language
The problem: organizations that deal with a multilingual audience spend considerable time and effort preparing and translating letters, announcements and introductory materials.
The solution: using generative AI to prepare first drafts and translate them, then having the team review and approve them. I have used this approach myself to prepare content in Arabic, English and French.
How to measure it: the time needed to prepare each piece of content, and the number of pieces completed per month.
4. Analyzing data and preparing periodic reports
The problem: monthly and quarterly reports are compiled by hand from scattered spreadsheets, taking time, arriving late and sometimes containing errors.
The solution: tools that gather and analyze data, extract indicators and trends, and draft the report, freeing the team to read the results and make decisions instead of compiling numbers.
How to measure it: report preparation time, data accuracy, and how quickly indicators reach management.
5. Searching regulations, policies and internal knowledge
The problem: new employees, and sometimes experienced ones, spend a long time searching for a regulation, a procedure or a past decision, or rely on asking colleagues.
The solution: a smart research assistant built on the organization’s own documents, which answers the question and points to the source of the answer. This is the very idea on which we built the Judicia AI platform in the legal field: searching regulations accurately and quickly, with reference to the source.
How to measure it: the time needed to find information, and fewer errors caused by relying on outdated information.
Before you choose: three rules
• Pick just one use case to start, apply it in one department, then expand once the results are proven.
• Protect your data: clearly define what can and cannot be entered into AI tools, especially personal and confidential data.
• Keep a human in the loop: AI speeds up the work, but decisions and final review remain with the specialist, because these tools can be wrong with complete confidence.
Conclusion
An organization does not need a massive project to begin its AI journey. It needs one clear use case and a result that can be measured within weeks. Any of these five use cases can be that beginning.
If you would like to identify the most suitable use case for your organization together, and set a realistic implementation plan for it, I would be glad to hear from you.
