As Generative AI (Gen AI) continues to make its mark in multiple industries, the demand for business analysts capable of steering AI initiatives in the right direction has never been greater.
Consider the case of an international shipping company that specializes in the door-to-door transportation of luggage and sporting equipment for traveling customers interested in avoiding the burden of dragging their heavy suitcases across the globe.
The company was hoping to leverage conversational AI to improve efficiency, accuracy, and personalization of services across the customer life cycle.
The leadership team initially set a specific goal: “One customer-facing conversational AI application running in production by the end of the year.” But after talking to a business partner that achieved disappointing results with a similar initiative, the CTO convinced the other executives that creating real business value would require more than “checking the box” of having a production-ready AI-powered application by a certain deadline. To make the initiative add up to something useful, they would have to understand the larger context of what the business was trying to accomplish.
A seasoned business analyst was assigned to work with internal stakeholders and produce a prioritized list of use cases based on the perceived opportunities for improvement. By asking the right questions and walking through a typical customer journey to see where the hiccups were, the BA identified a number of uses for an AI-powered chatbot with the potential to significantly improve customer service, including:
- Answer common pre-shipping questions in order to free up the time of support agents to better assist customers with lost, delayed, or damaged luggage.
- Fill out customs forms for customers by asking questions and automatically populating the appropriate fields to reduce customer annoyance and mitigate the risk of incorrect information causing shipping issues.
- Offer timely updates to customers experiencing shipping delays by instantaneously summarizing information from multiple sources.
Armed with a list of complementary conversational AI use cases across the service value chain, the company created a centralized set of reusable tools based on standardized blueprints and pre-configured AI components.
In contrast to the partner’s expensive ad hoc implementation, the company’s business-centric approach led to significantly lower costs per individual conversational AI agent deployed to elevate the customer experience.
Examples like this are typical in my line of work (consulting). Often, as an emerging technology goes mainstream, we see leading companies spend enormous sums of money on ambitious projects that fail to achieve the promised business transformation. But it doesn’t take long for smart leaders to realize what caused things to off the rails. The primary reason tends to be one or more of the following:
- a rush to adopt technology without a clear understanding of what it is supposed to accomplish;
- a shortfall in skills to build the right solution;
- the lack of the right organizational structure, processes, and people to use the new solution successfully.
This is why the companies that have experienced success in their AI endeavors are eager to tap their pool of business analysis expertise at an early stage of AI adoption. These companies capitalize on their BA capabilities to create an essential bridge across organizational boundaries that clarifies where the biggest opportunities and challenges lie.
How to succeed as a BA in an AI project
Great business analysts worry more about solving business problems than about deploying technology. As a BA working in an AI project, your primary job is to help your organization make informed choices about how AI can deliver the desired outcomes, including identifying the resources and competencies required to succeed.
You don’t have to comprehend all the details of how AI technologies work in order to succeed in this role. That being said, it’s crucial to develop a good understanding of the basic principles associated with the various AI subfields, as well as the possibilities for innovation that these technologies inspire.
This knowledge will be essential to help you discern and validate project assumptions and identify the most valuable and practical solutions to the business problem to be addressed with AI.
Especially when working with Gen AI, you should be aware of the substantial differences in approach and mindset required to succeed relative to projects involving traditional TI — and even traditional AI. A good starting point is to learn about the common mistakes organizations make with conversational AI.
Other than that, all you need is the same skill set that separates good from great BAs in any project. In particular:
- Critical thinking, essential for performing deep analysis, creating context, providing insight, and finding new ways of solving difficult problems.
- Strong communication and interpersonal skills to translate AI jargon into words and ideas that non-tech people can understand and successfully mediate the conversation between multiple business and technical stakeholders in pursuit of internal alignment.
How to find a fulfilling AI-related BA role
If you’re inspired by all the possibilities and eager to drive AI projects forward, here are a few guidelines to help you achieve your goals.
First, size up your employment opportunities on the basis of how much the organization understands the importance of establishing clear, end-to-end business outcomes for their AI initiatives. Avoid companies with a tech-first approach, which are more likely to offer disposable, document-centric BA roles that will prevent you from creating tangible value.
Second, don’t assume that your best opportunities lie with the big tech companies that are rushing to secure their dominance in the AI field. You may be able to find more exciting roles in smaller, less “AI savvy” businesses like the shipping company from the case study above, interested in exploiting the gigantic economic potential of AI to improve business results.
Different companies will be trying to optimize different aspects of their operations, from customer loyalty and service quality to supply chain, financial performance, and more. You’ll improve your chances of landing a fulfilling AI-related BA role if you apply to positions in organizations that are explicitly seeking to develop capabilities to leverage AI in a domain where you already have relevant knowledge.
Last but not least, look for employers with a culture that instills a companywide respect for the BA role and its potential to set the groundwork for project success. By doing so, you’ll be better positioned to help ensure that the benefits of AI can be realized with less effort, fewer resources, and reduced risks.
Author: Adriana Beal
Adriana Beal spent the past two decades helping innovation companies leverage AI/ML solutions and decision science to improve business outcomes. She recently left her job as a principal data scientist with a global AI consulting group to return to her roots as an independent consultant. You can find out more about her work visiting bealproject.com.