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Author: Michele Maritato Artificial Intelligence (AI) is transforming industries, reshaping business models, and redefining customer expectations. For organizations seeking to harness AI, the role of the Business Analyst (BA) becomes crucial—not just in defining requirements, but in aligning AI solutions with strategic goals and operational realities. According to the BABOK Guide, Strategy Analysis comprises four key tasks: 1. Analyze Current State This article focuses on the first and foundational task: Analyze Current State. Understanding where the organization stands today is essential before envisioning where it wants to go. Purpose of Analyzing the Current State: To uncover the underlying drivers for change — whether it's solving a problem, seizing an opportunity, or responding to external pressures. It helps identify what aspects of the business will be affected and lays the groundwork for defining business needs and solution scope. Key Outputs: 1. Current State Description: A holistic view of the enterprise’s internal and external environment, including capabilities, resources, performance metrics, culture, infrastructure, and dependencies; 2. Business Requirements: Clear articulation of the problem, opportunity, or constraint that justifies the initiative. Analyze the Current State of an organization is an integration endeavour, aimed at studying, discovering and integrating the several aspects of an organization, as well as understanding the Needs and their root causes behind. Below is a structured framework (might not be complete) to guide Business Analysts in capturing the current state for AI-related initiatives:
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1.External Context
- Understanding the external environment helps identify forces that may influence the AI initiative:
- Industry Structure: Is the industry undergoing digital disruption? Are competitors leveraging AI? What are the major trends?
- Competitive Landscape: What AI capabilities do competitors offer? Are there emerging threats and opportunities?
- Customer Expectations: Are customers demanding more personalization, faster service, or predictive capabilities?
- Regulatory Environment: Are there laws governing AI use, data privacy, or algorithmic transparency?
- Technological Trends: What emerging technologies (e.g., generative AI, foundation models) are relevant?
- Macroeconomic Factors: How do inflation, labor shortages, or global supply chains affect feasibility?
2. Organizational Structure and Culture
- Structure: Is the organization hierarchical or agile? How does this impact decision-making and innovation?
- Culture: Is there openness to experimentation and data-driven decision-making? Are employees AI-literate? What is the mindset of the Management?
3. Capabilities and Processes
- Business Capabilities: What core competencies exist? Are there gaps in automation, analytics, or customer engagement?
- Processes: Are current workflows optimized for AI integration? Is data flowing efficiently across systems? Very often Needs are tightly connected with processes.
- Data Management: Is data clean, accessible, and governed? Are there silos or legacy systems?
4. AI Competencies
- Talent: Does the organization have data scientists, ML engineers, data engineers, data owners, or AI product managers?
- Experience: Has the organization deployed AI before? What lessons were learned?
- Readiness: Are teams equipped to manage AI lifecycle—from experimentation to operationalization?
5. Technology and Infrastructure
- Systems in Place: What platforms, tools, and environments are currently used?
- Limitations: Are there constraints in compute power, storage, or integration capabilities?
- Cloud vs On-Premise: What deployment models are feasible in the existing context given security and compliance needs?
6. Stakeholder Landscape
- Who Benefits: Is the solution aimed at customers, employees, partners, or executives?
- Stakeholder Clarity: Are expectations aligned? Is there a shared understanding of success?
7. Strategic Alignment
- Organizational Strategy: How does the AI initiative support broader goals—growth, efficiency, innovation?
- Initiative Fit: Is the AI project a strategic priority or a tactical experiment?
8. Business Needs and Outcomes
- Needs Identification: What problems or opportunities are being addressed? Business Needs may be identified at many different levels of the enterprise:
a. From the top-down (e.g. a strategic goal that needs to be achieved); b. From the bottom-up (e.g. a problem with the current state of a process,
function or system); c. From middle management (e.g. a manager needs additional information to make sound decisions or must perform additional functions to meet business objectives); d. From external drivers (e.g. customer demand or business competition in the marketplace)
- What is the root cause of these Needs? Can we prioritize these Needs?
- Time Horizon: What are the short-, medium-, and long-term goals?
- Desired Outcomes: Revenue growth, cost reduction, operational efficiency, customer satisfaction? Make them measurable.
- ROI Expectations: What financial returns are anticipated? Over what timeframe?
9. Solution Suitability
- Cognitive vs Non-Cognitive: Is AI truly needed, or can simpler automation suffice?
- Generative AI and Foundation Models: Can these technologies accelerate delivery or reduce cost?
10. Constraints
- Time: Are there deadlines driven by market or regulatory pressures?
- Budget: What financial resources are available?
- Resources: Are skilled personnel and infrastructure in place?
- Any other constraint?
11. Performance Expectations
- Business KPIs: Accuracy, precision, false positive/negative rates, thresholds
- Technology Metrics: Training time, inference speed, memory and compute requirements, integration needs
12. Ethical, Responsible, and Transparent AI. AI initiatives must be designed with trust and accountability in mind. This includes:
12.1 Ethical AI
- Bias and Fairness: How will bias be detected and mitigated?
- Positive Impact: Is the solution designed to benefit society and avoid harm?
12.2 Responsible AI
- Compliance: What laws and internal policies must be followed?
- Safety and Privacy: How will data be protected and misuse prevented?
- Human Accountability: Who is responsible for AI decisions?
- Cybersecurity: What are the requirements for building a secure cyber environment?
12.3 Transparent AI
- Consent and Disclosure: Are users informed about AI involvement?
- Visibility: Can stakeholders understand how data and models are used?
12.4 Governed AI
- Auditability: Can decisions be traced and reviewed?
- Monitoring and Versioning: Are models tracked across iterations?
- Contestability: Can users challenge AI decisions?
12.5 Explainable AI
- Interpretability: Can decisions be explained to non-technical stakeholders?
- Legal Constraints: Are certain algorithms restricted by law or industry standards?
13. AI Patterns and Acceleration. Having a first idea of what AI solutions will be requested helps requirements elicitation and shape design and delivery:
- AI Patterns: Recognition, prediction, conversation, goal-driven systems, anomaly detection, hyper-personalization
- Acceleration Tools: Can Generative AI or Foundation Models reduce development time or enhance capabilities?
Final Thoughts
Analyzing the current state is not just a diagnostic exercise — it’s a strategic imperative. For AI initiatives to succeed, Business Analysts must go beyond traditional requirements gathering and embrace a holistic, forward-thinking approach. By rigorously assessing the organization’s readiness, constraints, and strategic alignment, BAs can ensure that AI solutions are not only technically sound but also ethically responsible, operationally feasible, and strategically impactful. This is where professional Business Analysts make the difference.