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AI in business analysis is evolving. In 2024, it shattered the foundations on which many modern business functions were built. We are currently in an age of change, where everything is experimental. Business analysis lies at the core of this rebuilding effort. Every action a business takes today must be considerate of AI.
Business analysis is how we explore and discover the business’s performance, strengths and weaknesses, and use that information in decision-making. It happens for every part of the business. Whether it be the accounting department or the sales department, all business processes come under scrutiny in business analysis. Now that AI has become so dominant and present in our lives, all these functions need to be re-examined in this new light.
Let’s explore the new frontiers AI has opened up for business analysts and the new challenges it brings. The genie is out of the bottle at this point, so instead of rejecting it, we should rejoice and embrace it as another tool in our arsenal. With luck, we can help you find your footing in this unsteady business world and start making strides toward an AI-inclusive business.
The Role of AI in Business Analysis
The Significance of Data in Business Analysis
The first thing to consider in business analysis is that data is everything. Data is the foundation for every business decision. Without data, a business is effectively flying blind and can crash and burn at a moment’s notice.
AI as a Data Collection and Processing Resource
Given this dependency on data, one of the significant roles business analysts take up is data collection and processing. A lot of data is needed, and a business generates boatloads of data daily just from regular operations. This data pools in from the various business functions into the ERP system, where it needs to be sifted through.
There is good data and insufficient data. Faulty data that cannot be verified or is erroneous can negatively impact decision-making. The business analyst must separate the wheat from the chaff, after which the data can be used for analytical purposes.
AI can simply address this entire role by itself. Thanks to the sophisticated algorithms available today, data collection AI can pool together organizational data and all relevant publicly available data. Instead of an analyst laboring to extract spreadsheets full of data to sift through manually, AI can do it all on the fly. The data goes through various verification processes, and if it is usable, it gets categorized and output in the results.
All of this “busy work,” so to speak, does nothing but clog up a business analysis consultant’s schedule. Half their time is wasted just setting up for the “analysis” part of their job description. With AI taking up this responsibility, it offers two massive performance improvements in business analysis.
Firstly, business analysts and consultants can leverage their skill sets to the max and not burn out doing mundane data collection. Secondly, AI drastically speeds up the data collection process, which means faster analysis and faster reports for decision-making. Not to mention that with AI, data-driven reports can be generated more frequently. Urgent decisions can also be supplemented with a quick analysis of the latest data.
AI as a Preliminary Data Analysis Resource
While we highly advise against handing over data analysis to AI, there is no harm in using it as part of the process. A unique perspective can be obtained by running data through machine learning and pattern recognition algorithms. This preliminary perspective can then supplement a business analyst’s analysis.
Humans are not perfect, and there will always be information that slips under the radar. A pattern of consumer behavior, for example, that is too vague for a person to identify.
Conversely, AI can perform rigorous pattern detection and point out any possible points of interest in the data.
Using AI in this way greatly increases the quality of business advice an analyst can offer. Analysts are there to improve business processes, and being aware of even the smallest problems thanks to AI can be game-changing.
Business analysts can also use AI data analysis as a jumping-off point. They can rigorously examine the AI-generated analysis for problems and use it as a basis for a conclusive report.
AI as a Communication Tool
Another great way to leverage AI in business analysis is in communication. Many business processes are structured around team preferences and office politics, and understanding these interpersonal dynamics is crucial for process improvement.
AI can be a highly effective communication tool in this aspect. Business analysts can use AI chatbots and the like to regularly take the temperature of the workspace.
Automated surveys can be periodically distributed so employees can safely and anonymously communicate grievances and suggestions. Many employees are simply too afraid to talk out of turn, even if they spot issues, in fear of retaliation.
Making a safe space for them to air out any thoughts can go a long way to improving the workplace. Maybe one of the team members is not cooperating or bullying another. Opening communication channels where these things can be discussed effectively can curb bad behavior and encourage a healthy workplace.
In addition, business analysts can leverage AI to analyze email and other organizational communication channels. AI can quickly sort through all the messages and sift out the most relevant ones on a subject.
Business analysts can also use AI to analyze and look for patterns in team communications. These can show interpersonal conflicts or other team issues that must be addressed.
Of course, there is always room for such systems to be used for nefarious purposes. That is why business analysts should not simply act on all the feedback. Everything must be investigated and explored before action is taken. Just because the workforce has such a way of accessing upper management and contributing towards business process optimization is a huge step forward.
Challenges Presented by AI in Business Analysis
Data Hallucination and Regurgitation
One of the most significant drawbacks to AI data collection in 2024 is the lack of refinement. We have reached the point where a sizable portion of available data online is entirely generative and speculative.
AI and language learning models are unfortunately prone to including these generative datasets in their databases, which leads to the unfortunate results of data hallucination and regurgitation.
In essence, the idea is that because generative AI is so prolific while in its infancy, the data generated is likely to be faulty in some way. The problem comes when the AI reuses faulty data to develop or collect more data.
Imagine Google Gemini generating some data for you, and then that same generated data is collected and used by ChatGPT to create more data; AI feeds into itself and regurgitates useless data in this situation.
We have even seen cases where medical researchers have leveraged ChatGPT to create fake clinical trial datasets, as reported by Nature.com. Imagine data like this popping up all over the internet and entering databases. A lot of it gets caught, but an equal amount falls through the cracks. This means that AI-collected and generated data needs human oversight, which is why we have always advocated using AI to supplement rather than replace humans.
Ethical and Regulatory Compliance Issues
AI is viewed very warily by the general populace today. Although the ultra-rich elite have hopped on the bandwagon, the rest of society is skeptical. Privacy concerns are at the forefront, as people fear being micro-analyzed by businesses and forced into a consumer position.
AI is also under heavy scrutiny about fairness in the job market. Questions like, Will AI replace accountants? have come across our desk at EA frequently in the past year.
Many businesses are laying off personnel and replacing them with AI, leading to societal issues and harming livelihoods. This is why business analysts must be cautious when using AI. Especially in process optimization, AI may solve problems or save money, but it also unfairly harms employees and affects the workplace.
Business analysis consultants must also be very wary of using customer data with AI. There is no guarantee that sensitive customer data will be secure when inputting it into AI. That data can quickly end up online as part of the publicly available data collection. Such sensitive data needs manual processing and careful handling.
Outsourced Business Analysts as a Substitute for AI
It is no secret that business owners leverage AI wherever possible to save on personnel costs. However, as we have demonstrated, business analysis is one area where this won’t fly.
Unfortunately, many small businesses do not have the luxury of hiring a business analyst. AI has been a welcome tool for these businesses as it is a cheap yet decently effective alternative. But what if there was something even better?
Of course, we are talking about outsourced business analysts. EA, in particular, excels in business analytics and consultancy, linking businesses with affordable talent from the global talent pool. While the cost for an outsourced professional will be slightly higher than an AI program, it is far from the exorbitant in-house cost.
At the same time, small businesses dodge compliance issues and potential ethical problems by having an outsourced business analyst on the team.
In short, instead of jumping to AI for business analysis needs, small businesses should remember that outsourcing is an option. Regardless, whichever way you choose is for you to decide, and we hope we were able to help you make that decision. Cheers!