The role of business analysts is changing, and AI will accelerate that change. Business analysts are asked to do more with less, which is always challenging. It means missed deadlines, delayed value, and lower-quality solutions. Today’s business analysts need more support than ever.
The use of AI in requirements management is inevitable. The same holds true throughout the organization. The potential for increased productivity and accuracy cannot be ignored.
Leaders expect AI to support their strategic objectives. It’s at the top of everyone’s mind, having captured everyone’s imagination. They believe it can achieve their strategic objectives without necessarily recognizing the significant changes this would bring to the organization.
The first big challenge is how to begin. There are tools and vendors that offer significant productivity benefits, but it is unclear how they can be used to the organization’s advantage.
With things moving so quickly, organizations may throw a lot of time, money, and effort at AI to keep up with competitors. If they don’t begin by defining how they plan to use it or what needs they hope to address, such resources are ill-spent.
There is always the fear that AI will get rid of all jobs in any category that it moves into.
AI doesn’t mean automating everything. Business analysts remain vital, but they must determine how and when AI can help them reduce time and effort spent on low value tasks, freeing them to focus on irreplaceable analysis and insights.
AI is only as good as the data used to train it. Embed mature practices in the learning models to ensure a high value, quality result. Business analysis plays a big role in this regard.
Approach AI adoption the right way. Charging in without knowing what problem you are trying to solve or the benefits you are looking to achieve wastes time and valuable resources. Doing that is as effective as ignoring it altogether.
Requirements management will always need the human element. AI can allow business analysts to focus on the activities that matter most. If you learn how to leverage AI to automate low-value requirements management activities, you can free up capacity for higher-value work to be done.
Despite the growth in supporting AI technologies, organizations are still struggling to adopt them. Despite the many promises of AI, organizations are struggling to fully realize its potential, as they would with any other new technology. 43% of business are concerned that they will become too reliant on this new technology, with an additional 35% considering whether they have the technical skills and know-how to effectively leverage AI in the first place (Forbes Advisor, 2023).
The reasons often boil down to a lack of understanding of when these technologies should and shouldn’t be used, including a fear of the unknown. There are reports of AI replacing many jobs, and these fears are rational as we have yet to learn how this technology will impact the current job landscape. However, fears like this accompany all revolutionary technologies. A better understanding of how best to work with the changes will help everyone through this revolution.
The organization is ready for AI when the following business elements are in place in the following manner:
The team is educated on AI, the use cases, and the tools that enable it. They have the skills and capacity to implement, create, and manage AI in a live environment.
Processes are documented, repeatable, and optimized to use AI effectively. Delivery tools are configured to enable, leverage, and manage assets to improve their performance and efficiency.
Delivery artifacts (e.g., code, scripts, documents) that will be used to train and will be leveraged by AI tools are discoverable, accurate, complete, standardized, of sufficient quantity, optimized or use, and stored in an accessible shared central repository.
There are defined policies, role definitions, guidelines, and processes that guide the implementation, development, operations, and management of AI.
Clear alignment of AI direction, ambition and objectives with broader business and IT priorities. Stakeholders support the AI initiative and allocate human and financial resources for its implementation within the team.
The capabilities to manage AI tools in operating areas. AI supports the growing needs of the requirements management practice, including security management, infrastructure, risk and change management, and data and application integration.
Human/computer interface in the new way of interfacing with technology. Requirements can never be fully automated. AI creates an opportunity for business analysts to focus on the activities that matter. If you learn how to leverage AI to automate low-value requirements management activities, you can free up capacity for higher-value work to be done.
The challenges in requirements management are as follows:
3.Decide where to begin
Track metrics throughout the implementation of AI requirements to keep stakeholders informed. Some example metrics/indicators and their descriptions include:
Requirements management will always need the human element. AI can allow business analysts to focus on the activities that matter most. If you learn how to leverage Ai to automate low-value requirements management activities, you can free up capacity for higher-value work to be done. Business analysis teams can and should lead the way in helping to start the AI movement in the organization.
AI won’t replace the business analyst, but an analyst who knows how to leverage AI will replace one who doesn’t: We are still at an early stage of AI in requirements management. This time should be used as a learning opportunity, so the organization does not get left behind by colleagues and competition.
AI is becoming the new human/computer interface: AI’s progression as a human/computer interface promises to redefine our relationship with technology. This will further empower us, breaking down barriers and making our interactions with the digital world become seamless, effortless, and natural.
AI will not solve people and process problems. It will expose and amplify them: Don’t use AI simply because it’s available. Explore options to improve people and process before exploring technology, and how AI can enable improved outcomes.
Don’t fall victim to over-hype: It is easy to get swept up when where to start or what the value is remains unknown.
AI brings a shift in how decisions are made: When decisions are based on probabilities, a shift is required from deterministic and binary thinking (yes/no) to probabilistic and less certain. Leaders must be ready to operate with this new way of thinking and decision making.
Ensure policies, processes, and standards add value: When defining AI policies, processes, and standards, ensure they are relevant, serve a purpose, and/or support the risk management framework and use the AI in the organization. Commit to reviewing them periodically as the technology expands.
Don’t swing for the fences during the first at-bat: Keep the scope and timelines of the initial AI project manageable. As the focus is on quick qins to get started, avoid big, onerous projects that span years.
Don’t be fooled by the “sell”: Many vendors have jumped on generative AI (Gen AI) as the latest marketing buzzword. When vendors proclaim to offer gen AI functionality, pin down what exactly is generative about it. The solution must be able to induce new outputs from inputted data via self-supervision – not trained to produce certain outputs based on certain inputs.
Start small. Select strategic targets: Focus only where AI has the potential to deliver significant business value.
Source: InfoTech Research Group
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