The Future BA: Why AI Skills Will Be as Essential as Excel

The Future BA: Why AI Skills Will Be as Essential as Excel

By Harry Madusha

For decades, business analysts have relied on tools like Excel to gather, structure, and communicate data. It became the universal language of analysis, one every BA was expected to master. But the landscape is shifting.

Artificial intelligence is no longer an experimental trend. It is becoming embedded in everyday tools and workflows. From predictive models to intelligent automation, AI is transforming how decisions are made and how value is delivered. For business analysts, the implication is clear. Just as Excel became a default requirement, AI literacy is quickly becoming the new baseline.

The business analyst of the future will not just understand business needs. They will also understand how to collaborate with intelligent systems to solve complex problems faster and more accurately.

Why AI Is Becoming a BA Skill

AI enables faster processing of large data sets, uncovers patterns invisible to human eyes, and helps predict outcomes with increasing accuracy. The business world is already leveraging these capabilities to streamline operations, improve customer experience, and forecast performance.

But these systems do not run themselves. They need context, interpretation, and guidance. Business analysts sit in the perfect position to guide AI systems and translate their outputs into decisions that align with business goals.

Here are some key areas where AI skills are already proving essential for analysts:

1. Prompting and Querying AI Models

Business analysts are often asked to summarize large documents, generate customer insights, or create user stories. Tools powered by large language models can assist in all of these, but only when prompted effectively.

Knowing how to construct clear, structured prompts and evaluate the quality of the AI-generated response is now a valuable skill. It saves time and improves the quality of deliverables.

2. Understanding Predictive Models

Predictive analytics is no longer confined to data scientists. Many AI-powered tools now provide forecasting features that analysts must understand and explain. Whether the model is predicting customer churn or future sales trends, the business analyst is often the person who must interpret the results for stakeholders.

Understanding what drives a prediction, what data feeds into it, and what actions should follow separates strong analysts from technical spectators.

3. Data Cleaning and Automation

AI tools can automate repetitive tasks like data entry, document comparison, and even meeting transcription. But automations often need human review and configuration.

BAs who can design workflows using no-code or low-code platforms that incorporate AI features are already seeing faster project cycles and less manual work. AI does not replace the analyst, but it does make the role more strategic.

4. Evaluating AI-Driven Solutions

When vendors present AI-powered systems, BAs need to ask the right questions. What kind of data does the AI use? Is it explainable? Is it biased?

Understanding the basics of how AI works helps analysts make informed decisions about solution design and ensure recommendations are ethical, scalable, and aligned with business needs.

5. Storytelling with AI Outputs

AI can generate charts, trends, and summaries, but it cannot tell a compelling story. The business analyst’s role is to connect the dots between what the data says and what the business needs to know.

Being able to weave AI-generated insights into strategic narratives will become a core communication skill.

How to Prepare

You do not need to become a data scientist. But you do need to understand the basics of how AI works and how to work with it. Here are a few simple ways to begin:

• Experiment with popular AI tools to understand their capabilities and limitations
• Learn how to write effective prompts that get accurate results
• Explore basic data literacy concepts such as bias, model inputs, and accuracy
• Use AI for daily tasks to gain firsthand experience

Organizations are already looking for analysts who are AI-ready. Those who wait may find themselves left behind.

Excel was once optional, then expected. Now it is assumed. AI is on a similar trajectory. Within a few short years, business analysts who are not fluent in AI tools and techniques will be considered underprepared.

But the good news is this. Business analysts are already well-equipped to adapt. They are curious, analytical, and driven by outcomes. With just a few intentional steps, AI can become a powerful extension of the analyst’s toolkit.

The question is no longer if AI will affect your role. It is how soon you are ready to lead with it.