Generating a Business Analysis Revolution
The Accelerating AI Revolution
Eminent artificial intelligence breakthroughs will accelerate business analysis evolution to a productivity revolution. Well-trained artificial intelligence will assume rudimentary tasks, enabling business analysts to devote more energy to discovering insights and aligning stakeholders to achieve business goals.
Right now, natural language processing can accurately transcribe meeting notes and organize them for easy search and understandability. Semantic search will find transcripted knowledge by meaning rather than just matching words. By understanding the context and relationships between terms, semantic search can highlight patterns that surface new insights. While natural language processing continues to improve discovery, generative AI will create helpful content for business analysts.
Generative AI took the world by storm in 2023, led by ChatGPT. The breadth of content generated by GPT has amazed the world, with executives clamoring for AI, like stakeholders anxious to implement solution features. Along the same lines, generative AI must apply to business analysis in depth to deliver worthwhile benefits.
Generative AI models are only as good as their training, which currently requires significant time and effort. Model training will improve, making it easier for AI trainers to deepen a model’s knowledge with content such as:
The Business Analysis Body of Knowledge (BABOK) and other curated sources
Communication skills to effectively mentor and guide business analysts
Domain knowledge facilitating frictionless interaction with subject matter experts
The Business Analyst Copilot
Imagine a business analyst dealing with a difficult stakeholder - simulated by generative AI. The analyst develops critical interviewing skills without costing stakeholders time and energy. AI can play various stakeholder roles to sharpen business analysts' communication techniques. It’s one aspect of a business analyst copilot - a virtual BA assistant.
A business analyst could ask their BA copilot for information from the BABOK, and it would swiftly find the relevant knowledge and converse with the analyst to help them understand it. For example, a business analyst struggling with brainstorming asks their BA copilot for information from the BABOK. It presents the “Brainstorming“ section of the book and offers related approaches such as mind mapping and concept modeling.
When integrated with real-time transcription, the BA copilot can provide on-the-fly suggestions during live stakeholder meetings. For example, it could maintain a glossary of terms and alert the business analyst when a stakeholder introduces unknown jargon. The business analyst would follow up with the stakeholders for an agreed-upon definition. The BA copilot would then log the term in the glossary.
The investment in training a BA copilot will yield higher dividends as it continuously improves.
A Deeper Business AI Model
An AI model trained on an organization will provide valuable insights into business analysis processes. This training includes:
Company-specific values and goals for stakeholder alignment
An overview of business processes
Organization domains for meaningful dialogues with subject matter experts
A business analyst dealing with ambiguous requirements asks "why" until they clarify the business's needs. An AI model trained in domain processes can help business analysts understand the answers to their whys and offer follow-up questions to determine the problems to solve. When the answers become domain-specific, an AI model trained in the domain can provide follow-up questions using the domain’s vocabulary and translate the answers into standard business language.
The Revolution Will Need Savvy Business Analysts
Imagine a future discovery workshop where natural language processing takes notes and extracts requirements for a business analyst to curate. The business analyst should have enough domain knowledge to understand the needs. A deeply trained BA copilot will help them ask follow-up questions in the subject matter experts’ terms.
Tools with well-trained AI models will create artifacts such as glossaries, concept maps, process maps, and requirements. The business analyst will review the summary artifacts with stakeholders, drilling down on issues with their copilot’s help. A BA copilot trained on the enterprise’s values and goals will connect requirements to objectives and warn business analysts of requirements that don’t add value to the organization or its customers.
The coming business analysis revolution will not replace business analysts. It will only succeed with business analysts leveraging artificial intelligence to take over rudimentary tasks and offer advice based on deep training. Once in-depth training becomes more efficient, demand for expert business analysts to train AI models will surge.
As AI models generate and manage business analysis knowledge, companies will need savvy business analysts to capitalize on that knowledge. These analysts will advance toward a business architect role, collaborating with executives and technical architects to develop strategic solutions and produce higher value for enterprises and their customers.
The accelerating integration of AI with business analysis promises a transformative productivity revolution, empowering analysts to focus on strategic insights and decision-making by streamlining communication, enhancing knowledge discovery, and automating routine tasks.