Artificial intelligence

Impact of Artificial Intelligence (AI) on Business Analysis

Artificial Intelligence

For quite a long-time, business analysts around the world have been thinking deeply about how the world of business analysis would change with the advent of artificial intelligence. AI enables significantly advanced capabilities to the enterprises which were in the exclusive domain of human intelligence till a few years back. So let’s try and explore what is business analysis and which aspects of business analysis are likely to undergo a significant change due to artificial intelligence.

There are multiple perspectives for business analysis. For this blog, we will focus on one perspective of business analysis, which is Software Solutions Requirements Analysis. 

There are 5 broad areas that we can consider for any IT or software business analysis:

  1. Planning for business analysis
  2. Eliciting requirements
  3. Analyzing requirements
  4. Managing requirements
  5. Suggesting solutions
  6. Improving solution performances
Business Analysis

                                                 Image credit: Medium

Area 1: Business Analysis Planning

That’s part of business requirements analysis with respect to planning. Planning defines activities we need to perform, wherefrom we will source requirements to what kind of risks we may face in our work. And we of course perform estimation for the effort that we would require.

Using AI for Business Analysis Planning:

There are AI-enabled tools available today for software requirements sizing which was always very contentious in the past. Stakeholders use to give some kind of personal opinion which can be a highly doubtful estimate. Tools like ScopeMaster provides requirements sizing in terms of cosmic function points, hence improving the reliability of the estimates.

AI-based tools also can predict the risks of the change and enable us to be better prepared to handle them.

Area 2: Requirements Elicitation

During elicitation, we elicit stakeholder requirements. We also utilize other sources of requirements such as existing documents and data to identify process issues to be solved. We get a detailed understanding of various stakeholder needs.

One of the things that the AI-enabled systems can do for us is to figure out requirements based on the project domain and context. Over a significant period of time, organizations have been capturing requirements in many ways, be it the Microsoft Word document, or Excel spreadsheets, homegrown tools or tools such as Jira, Microsoft TFS or HP ALM. A large number of requirements are already available in the organization. These requirements have been implemented, thus their design specifications, code constructs and test cases also are available to the organization.

                                                      Image credit: AdaptiveUS

So AI-enabled systems can suggest possible requirements for the project by understanding the domain and context. One such product, Adaptive RED from the Adaptive US enables organizations to build a requirements warehouse. The tool is envisioned to have AI techniques like knowledge extraction allowing the requirements to be generalized from a project requirement to a broader context. These are the likely requirements that one may see in one’s project. This can save a humongous amount of effort that we go through while eliciting requirements.

Many business analysts, especially ones new to the world of business analysis, may not understand all types of requirements. We’ve seen projects ignoring the implicit requirements and non-functional requirements. We are pretty confident in less than a decade, we could actually be having a virtual business analyst in the future which would actually take the stakeholders through the elicitation journey.

Again, as part of a business analyst’s work, we do hold a large number of discussions with our stakeholders. Getting this discussion transcribed was always a challenge. Often, we recorded them and painstakingly transcribed them. Fortunately today, there are many AI-enabled transcription software available to remove this drudgery. This blog’s draft was created by one such software. I used the voice to the text tool for this. Automatic transcription and knowledge extraction can be very powerful in terms of documenting and cleaning up our stakeholder discussions.

Area 3: Requirements Analysis

During analysis, the analysis we delve into individual requirements will look at their quality, will look at the comprehensive list of requirements, and develop different types of models to explain the requirements better to our stakeholders and to our developer community as well.

Requirement analysis is a very intense activity. There are so many attributes that we look for in a particular requirement, say for example the requirements must be clear, the requirements must not have any ambiguity, the requirements must be concise, and the requirements must be consistent. So many characteristics we look for in a good requirement. Conducting requirements analysis manually is a pretty time-consuming and costly activity. There are AI-enabled tools available today which will perform the requirements quality analysis. We can go back to a tool like ScopeMaster which does a fantastic job of assessing requirements quality.

ScopeMaster also identifies requirements dependencies which are pretty useful to business analysts. It can also help us to build concept models which can be very helpful to explain the domain to our stakeholders.

                                               Image credit: ScopeMaster

Area 4: Requirements management

As business analysts, we are custodians of business requirements, stakeholder requirements and solution requirements which is the primary focus of the requirements management area. Requirements management maintains requirements integrity during and post-project.

Then coming down to the requirements management, we are looking at maintaining requirements not only at the project level (which of course most of us perform fairly well) but at the enterprise level as well. When we look at Enterprise requirements management, generally organizations are not that good at it. 

With a requirement management AI-enabled tool, we can perform two amazing things automatically. One, it creates high-level generic requirements from project-specific requirements. It also aids in requirements reuse in a much more significant manner while suggesting what kind of requirements can be reused given the specific project context.

Area 5: Improve Solution Performance

And then of course we also look at solution performance and try to improve the solution performance over the period of time. AI-enabled systems can monitor system performances 24 by 7 and alert likely solution failures. This can help the organization maintain its systems much better.

Area 6: Suggest solutions

As business analysts, we suggest the best path forward, and the best solution for the organization to achieve its objectives.

Here again, AI plays a significant role as AI-enabled systems can perform tasks such as cleaning up messy data, alerting likely fraudulent transactions, suggesting product bundling etc. Business analysts should keep a close watch on products being introduced in the marketplace which can be part of their solution portfolio.


AI is a nascent technology, but it is evolving rapidly. Given the drastic reduction in computing and communication costs, there is no doubt AI will become even more pervasive in business and in our day to day lives. As we described in the article, AI can be pretty helpful in improving business analysis practices in any organization.

Got any questions or suggestions on the article? Do share it with me in the comments.

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