Artificial intelligence is here to stay and every company is scrambling to ensure it doesn’t miss out. It is already reshaping industries, redefining workflows, and reimagining customer experiences in the digital world. Companies across the globe, driven by the promise of efficiency, innovation, and competitive advantage, are enthusiastically diving into AI initiatives.
Yet, despite the enthusiasm, many stumble early in their AI journey – not because of a lack of technology, but because they overlook one fundamental step: clearly defining their AI requirements.
What are requirements?
Requirements, in the simplest terms, represent the detailed, strategic criteria that guide an AI project (or any project, really) from concept to completion. Without thoroughly defined requirements, organisations risk wandering off-course, wasting resources, and ultimately failing to achieve the transformative outcomes AI can provide and leaders are expecting.
Requirements are often overlooked, but putting together a strategy for their definition is really important. And attempting to generate requirements with AI itself, will lead companies down the well-worn path of failure, as most of the time, the system does not have the full scope nor understand the ultimate goal.
They are crucial in any project, but unlike traditional software, AI systems learn and evolve based on data and interactions. This dynamic nature means the absence of well-defined requirements can quickly lead to unintended outcomes, ranging from minor operational inefficiencies to ethical dilemmas or even the complete waste of financial and human resources. Clearly articulated requirements serve as guardrails, ensuring the AI’s behaviour remains predictable, valuable, and ethically sound.
How to come up with good requirements
To illustrate this, consider the common scenario of deploying a customer support chatbot. Many companies might start by simply saying: “We need AI to handle customer inquiries.” But without precise requirements, this initiative can quickly go astray. Take the example from a Chevy dealership chatbot in California that sold a Chevrolet Tahoe for $1!
This example shows us that even if there is a solution ready to go, it might not be the best solution for a business, because only they know what the company will need. A well-defined requirement for this particular project would instead be articulated as: The AI-powered chatbot must autonomously resolve at least 85% of common customer queries related to order status, returns, refunds, shipping delays, product information, and basic troubleshooting steps, doing so within an average response time of 30 seconds or less. Additionally, the chatbot must seamlessly escalate complex or ambiguous cases immediately to human customer service agents, ensuring this transition occurs without any noticeable interruption, confusion, or friction for the customer, maintaining a high standard of customer satisfaction throughout the interaction.
Such clarity leaves no room for ambiguity, allowing developers and stakeholders to build, test, and optimise the chatbot to deliver exactly what users and the business need. Obviously, this is easier said than done, but there are ways to minimise the effort and ensure a company nails the requirements for their next big thing.
Tips for requirements – clarity and alignment
Businesses and leaders must approach AI requirements through structured frameworks and established best practices. Consider incorporating the following methodologies:
- User stories and personas: define clear user stories that describe exactly how different types of users will interact with the AI solution. For example: As a supply chain manager, I want the AI to provide real-time predictions of inventory shortages, so I can proactively manage stock levels and prevent disruptions. User stories humanise AI, ensuring it solves problems users might face rather than hypothetical ones.
- Prioritisation with the MoSCoW method: to manage the complexities inherent in AI development, organisations must ruthlessly prioritise their requirements. The MoSCoW method (Must-Have, Should-Have, Could-Have, Won’t-Have) categorises requirements based on urgency and impact. This method helps teams focus resources effectively, addressing critical functionality first and deferring less essential features without losing sight of them entirely.
- Ethical and transparent guidelines: clearly defining ethical boundaries and transparency measures is essential. For instance, setting a requirement like, ‘All AI-driven decision-making processes involving financial or medical data must be fully transparent, auditable, and easily explainable to non-technical stakeholders’ ensures that the AI system remains responsible, ethical, and compliant with regulatory standards.
- AI readiness assessment: a rigorous readiness assessment ensures organisations have adequate data quality, infrastructure capabilities, and skilled personnel to deploy AI effectively. Without confirming readiness, many AI projects falter due to insufficient or low-quality data, inadequate computational resources, or lack of expertise—issues that could have been addressed upfront with clearly defined preparation criteria.
False economy
Businesses often skip or superficially address the requirements stage, believing it saves time, resources, and energy. However, much like cutting corners in software development projects, neglecting thorough requirement definitions in AI initiatives, creates long-term complications and regularly results in project failure.
Yet, when organisations approach AI requirements strategically, they position themselves for sustainable success. Effective requirements act as guardrails, protecting projects from scope creep, ethical missteps, and wasted investments.
They streamline collaboration, foster transparency among stakeholders, and set clear expectations for deliverables, creating a pathway toward tangible business outcomes. In short, clearly defined requirements will be the reason why a company succeeds in AI – they are not just documentations gathering dust.
Requirements keep the excitement of AI grounded in practicality and realism, while ensuring ambitious visions are transformed into expected and measurable outcomes.
Before embarking further on an AI journey, pause and reflect: has your organisation truly defined its AI requirements, or are you just hoping for success without a clear plan?
