by Jamie Champagne

What artificial intelligence means to business analysts

Aug 20, 2019
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Artificial intelligence is no longer a buzz word but a reality for the environments business analysts and many other analysts find themselves working with today. There’s a real need for analytical skills to learn these new capabilities and identify key problems and opportunities for the organizations to leverage. Tried and true techniques still work, but analysts need to adapt to underscore the continued value they can offer organizations.

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Are you ready for the robot invasion? Or are you just plain worried that your job will be automated away?

The age of artificial intelligence is not simply here but has permeated through our very existence already. From forecasting market trends to reordering your groceries that were going low to being delivered by drones, artificial intelligence is all around our lives and the way we do business. When we talk about artificial intelligence, its focus centers around adding and, more importantly, enhancing our current capabilities.

AI should be looked at as a way to increase what we currently do. To improve current performance, speed execution times and deliver greater value faster and more direct to our customers. So what would that mean for us as business analysts?

It begins with a great opportunity for us analysts! We get to take our tried and true skills and apply them in new contexts and environments and even emerging industries to uncover needs and see opportunities. Innovation is constantly happening, but the great successes are the ones that address critical problems. How do you know what’s a good problem that needs solving? You do some analysis work. Root cause analysis, process modeling and analysis, business rules analysis and data modeling can show where key aspects of organizations are under performing or even paralyzed by their own behavior.

Identifying these opportunities helps us open the doors to conversations with customers about what is possible. Then getting to expand our knowledge by learning new technologies, approaches and perspectives on how AI can be leveraged by organizations makes us great resources. We get to learn capabilities of new technologies to facilitate collaborative brainstorming sessions on how the new capacities can mean value for our organizations.

With this perspective, analysts need to be mindful of their time and, as always, focus on what key activities deliver the most value. What day-to-day activities could you automate so you spend more time facilitating collaborations, supporting decision making and understanding needs? This makes us then focus on what value we, as analysts, provide organizations. The automation of jobs is focused on removing the non-thinking aspects of work. Yes, there are amazing things that the AI and robotics and drones and other innovative technologies are doing, but first and foremost, having the power of computing to help daily activities is focused on what is repetitive.

What is redundant? What are the things we would often call monotonous? These are GREAT for automating!  And how do you know what these things are? That’s right – you have to do some business analysis! There is a great technique analysts use called decision modeling. The challenge many analysts have with this technique is that they find out that their stakeholders do not make decisions the same way all the time. When you can define rules that lead to patterns in behaviors and activities then you can start to leverage the AI technology. It still needs a foundation to start with and grow from.

Begin looking at your analysis activities: What work is repetitive? What decisions do you make daily? Again, start simple! Think about project management software that begins to notice patterns of status updates and deliverable timeframes so then starts creating reminders and building templates that make your work easier and faster. Then you can spend more time working with your stakeholders than working on your reports.

With the “easy” stuff being taken care of AI, we can then tackle the harder stuff, but that means we need to grow and improve our own skills. Our questions have to get better. Better questions on what the business wants to do. Better questions on what technology is now doing for us. Better questions on considering the possibilities.

As a business analyst, our focus is always on the right questions to determine through consensus, validation and verification activities what the right answers are for the organization at that point in time. Our questions need to have a focus beyond the current timeframe. Can we leverage what we’re doing today for tomorrow? For one year from now? For five years from now? Or can we constantly adapt our focus to meet the evolving landscape? Do you recognize the changes the consumer environments are going through and their perspective on getting their needs satisfied? Customers have gone from Uber to self-driving cars – do you really want to ask them to fill out a paper form?

If you haven’t already shifted your view to the consumer experience, now is the time. What questions can you think of to try to predict what your consumers and stakeholders want? What would they want a month from now? Six months?

Even more important, AI challenges us to not only ask questions of our customers, but ourselves. The biggest question people ask about with AI in many office jobs is what AI means to their livelihood. As business analysts, the same is true to us, we just need to use our own analysis skills. Consider asking yourself some serious questions. What are we doing to avoid becoming obsolete? Have you asked yourself today, what would prevent your role from being automated by a machine? Do you perform complex analysis work? What about your work and daily processes is programmable or repeatable? And more importantly what is not? Those things you could feed into a machine to do – get them off your plate.

Booking a flight, for example, may feel like a personal activity as you pick your timing, seat preference and favorite airlines. But all these preferences can still be elicited, captured and then fed into decision making models. Your actions, along with these models, can then be automated and even forecasted so that the work is now off your plate. But notice in here – requirements must still be elicited (preferences in this case), decisions need to be modeled and processes need to be captured.

Whether using technology to help you or not, the analysis work is still valuable but now must simply come from a new perspective and work in evolving context. Do not hold onto your techniques with such rigor you cannot apply them in new environments. Stay fluid in your approaches and grounded in the theory so that you can see possibilities while driving discussions forward.

We already know our analysis skills sets are valuable – we need to think though what they mean in this changing environment. This is where you want to shift your energies on growing your skills. Do you understand AI well enough that you can articulate its value to an organization? AI is still a ways away from telling businesses to buy new AI technology, but perhaps not that far off. WHY is AI important? What can it do for organizations?

Often the most valuable innovations are asking good questions about existing and often old problems rather than inventing new things. What challenges, inefficiencies and legacy components are holding your organization back? Perhaps these have to be addressed before AI technologies can be introduced? Or perhaps AI technologies can address these items themselves? How do we know? We have to do good analysis work.

In short, keep your skills sharp, continue to learn as much as you can and drive people to articulate what needs are not being satisfied today. Those needs are your opportunities for success!