MD Akram Hossain is an accomplished business analysis and product management professional with more than nine years of experience leading enterprise-wide digital transformations across healthcare, finance, and technology. An MBA and Advanced Certified Scrum Product Owner, he has guided organizations through the adoption of EPM, ERP, and EHR systems, including Anaplan, Workday, SAP, and Epic. As a Senior Advisory Board member at the IA Forum, Akram has become a recognized voice in how AI is reshaping the business analysis profession.
In this interview, he shares how automation, predictive analytics, and machine learning are redefining the analyst’s role, from documenting processes to driving data-backed, strategic decisions, and what it takes to thrive as a “Business Analyst 2.0” in today’s fast-moving, data-first world.
How would you define the key differences between traditional business analysis practices and the modern, AI-driven approach that organizations are adopting today?
The traditional BA vs. the modern AI-driven BA approach varies quite significantly. We used to see traditional BAs focusing more on manual documentation, preparation, and data analysis, but AI BAs now help with automating those tasks so that the analysts can prioritize real-time, predictive, data-driven decision-making. We now see a modern BA approach considering model behavior and governance so that organizations can build ethical tools.
For instance, in supply chain settings, a traditional BA would spend a lot of time designing a certain procure to pay process, and how variation in supply and demand would affect that but an AI BA can now eliminate redundant processes, and on top of that add model features to be built in, validate the model data and inputs/outputs, as well as have governance dialed in to ensure transparency in that P2P workflow. It’s about modern BAs now backing up qualitative factors with quantitative evidence.
With data volumes exploding, what role should modern business analysts play in making sense of complex information and ensuring it leads to actionable decisions?
A modern BA can help with making sense of unstructured data from data scientists or ML engineers and turning that into business actions for the organization to achieve its goals. For example, in a healthcare setting, an AI BA can identify primary drivers of a certain patient outcome model’s output and then build a dashboard for leadership with actionable insights by assigning a process owner for that output. The process owners can then take certain action steps, measure those on KPI boards, and eventually help to enhance patient outcomes.
How is AI reshaping the toolkit of a business analyst, and what new skills are becoming essential for analysts who want to stay relevant in the age of Business Analysis 2.0?
BA professionals are now expected to have some level of AI/ML proficiency at most organizations. Although coding is not something that’s been traditionally expected of a BA, some workplaces now are asking for basic to intermediate compatibility with tools like Python and SQL, while also emphasizing Tableau and Power BI for data visualization. AI literacy with knowledge of how model evaluations work, drift detection to validate those models, prompt engineering, and being able to translate ML data outputs into business insights are crucial skills these days. We see new tools coming up almost daily, but more than the tools, it’s important to have the right mindset and be receptive to learning and adapting while navigating this age of AI Business Analysis.
Traditional business analysis often centered on gathering requirements and mapping processes. What does that look like now when predictive analytics, automation, and machine learning models are in the mix?
Requirement elicitation, process mapping, etc., are still relevant, but those have evolved into becoming more automated, faster, and dynamic with AI tools. If you have previous project data, you can plug those into a model for identifying patterns, milestones, feedback, and so on when preparing for a new release. Discovery and requirement elicitation sessions are more productive with AI tools helping with processing meeting transcripts and capturing finer details from emails or other project documentation, so the chance of missed requirements is drastically reduced. Process mapping using AI tools has also become more efficient because processes get updated in real-time as new data comes in, and forecasting for the future state becomes cleaner and less speculative. Also, we see predictive and intelligent workflow being more integrated now. For example, in procurement workflows, purchase orders requiring reviews can be decided based on AI-driven signals and checkpoints.
What strategies can organizations use to encourage continuous learning so that business analysts are equipped for evolving roles in data-driven environments?
Organizations may have their own learning platforms up to date with courses designed by their internal teams, or can partner with third-party EdTech platforms so that their business analysts and other team members can prioritize continuous learning and growth. Peer-to-peer learning, designated learning days on the calendars, or internal hackathons can also play an effective role in pushing the teams to skill up and grow professionally.
In many companies, there’s tension between technical experts and business stakeholders. How can modern business analysts act as a bridge, especially when AI technologies are involved?
Business analysts need to maintain an appropriate level of abstraction in their communication with the business, especially when communicating technical language. In AI teams, BAs need to set the right expectations with the business and leadership from the very beginning about what the model can and cannot do. BAs are now the stewards for AI adoption, so they need to maintain consensus and get the buy-in across both technical and business stakeholders and handle trade-offs effectively using emotional intelligence and strategic thinking. Being able to facilitate joint demo and value-mapping sessions and communicating clearly in squad calls will help in building trust, creating transparency, and speaking the same vocabulary by both technical and business stakeholders.
What are some of the risks of clinging to traditional analysis methods in a world where speed, agility, and data fluency are critical?
If BAs don’t adapt and evolve working with AI products, they risk falling behind and opportunities being missed, both from an organizational and professional perspective. AI teams in the majority of cases, can make faster decisions and get access to deeper insights from data, so BAs who cannot support these teams working with AI initiatives will put them at a big disadvantage. Businesses risk losing out to competitors who use AI efficiently. Today, we also see more business stakeholders and senior leaders relying on personalized, actionable, and faster insights from BAs, so those who cannot adapt will appear as having reduced credibility and obsolete skillsets that can, in turn, put their careers at risk.
Looking ahead, how do you see the role of business analysts evolving in the next five years, and what does success look like for a Business Analyst 2.0 in a data-first organization?
I think all BAs will have to skill up and become AI BAs eventually if they want to stay relevant in the market. On top of that, I see a lot of organizations these days merging the roles of a BA, PO, and PM under one bucket, so BAs should be ready to put those hats on as well. Having cross-domain expertise, AI/ML literacy, and being able to communicate effectively with ML/data engineers, developers, designers, governance, and business stakeholder,s and being that ‘AI-enabled’ change leader will be key for a Business Analyst 2.0.
