Artificial Intelligence (AI) projects are fundamentally different from traditional projects or IT initiatives. Typical IT/software projects are already very complex, and with new technology like AI, they have become more uncertain, experimental, and require flexibility. This means traditional project management methods that didn't work for IT/software projects stand no chance to deliver AI projects.
This blog explores why traditional project management methods fall short in AI initiatives and how adopting an Agile mindset can significantly enhance AI success.
The article was originally published on AgileWoW
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Why does traditional project management struggle with AI?
Traditional project management methods typically follow linear paths that clash with AI system development's iterative and incremental nature. This rigidity poses significant challenges for AI projects:
1. Uncertainty and complexity: AI development involves experimentation with uncertain outcomes that make fixed planning difficult, e.g.
a. Dependency on data quality and availability
AI systems heavily rely on large volumes of data. Data quality issues or availability constraints can significantly alter or halt a project's progress. Teams often discover data challenges only after starting the development, adding layers of uncertainty.
b) Exploratory nature of AI
Unlike traditional software systems, AI solutions are often experimental. Developers must explore various algorithms, techniques, and data combinations without guaranteed outcomes, making project timelines difficult to predict accurately.
c) Unpredictable results
AI models can deliver unexpected behaviors or results due to complex mathematical interactions within neural networks and machine learning algorithms. Small changes in data or parameters can dramatically affect outcomes, creating ongoing uncertainty.
2. Scope limitations: Traditional methods require upfront, detailed requirements, which makes it difficult for AI projects to evolve as new insights emerge.
a) Incremental and iterative learning and discovery
AI models often require multiple cycles of training, validation, and optimization. Teams discover insights, limitations, and opportunities through each cycle. Attempting to define everything upfront severely limits learning opportunities.
b) Changing requirements
Business and user requirements evolve rapidly in response to market changes or new insights from the data analysis itself. A fixed scope prevents adapting quickly to these insights and changes, potentially resulting in obsolete or irrelevant solutions.
c) Data-driven adaptation
The nature of AI is such that the data itself drives feature development and decision-making. As more data is collected and analyzed, original assumptions frequently change, requiring continuous scope adjustments.
In short, attempting to fix the scope upfront in an AI project severely reduces agility and the capacity to adapt, ultimately impacting the quality and relevance of the AI solution delivered.
3. Inflexibility: AI requires frequent adjustments based on continuous feedback, not accommodated by linear, waterfall methods. The complex and adaptive nature of AI solutions demands it for several reasons:
a) Responsive adjustments to model outcomes
AI development involves repeated cycles of model training and tuning. Flexibility enables teams to quickly pivot based on model performance and unexpected insights.
b) Faster innovation and experimentation
Flexible Agile methods encourage frequent experimentation. Teams can swiftly test multiple approaches, discard ineffective ones, and focus on those showing promising results.
c) Effective handling of ambiguity
Ambiguity is inherent to AI because of uncertain outcomes and exploratory processes. Flexibility in approach, processes, and even mindset allows teams to comfortably navigate ambiguity without becoming paralyzed or wasting resources.
Some of you may recall this graph from your Agile/Scrum training workshop, where do you see the AI systems fit in?
They are Complex. And what method or framework is best fit for the complex work?
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How does Agile support AI initiatives?
Agile is based on the values of adaptability, experimentation, and continuous learning—perfectly aligned with AI's inherent characteristics. Agile embraces incremental and iterative cycles, continuous feedback, adaptability, and flexibility, which are good fits for AI systems.
In Agile, the ability to adjust scope, adapt to data insights, respond quickly to model performance, and maintain a continuous loop of learning and improving becomes the core of success.
Adaptability: Agile embraces change and uncertainty, allowing teams to pivot when data reveals new insights.
Experimentation and learning: Agile encourages regular AI experimentation, incremental and iterative improvements, and continuous feedback loops, essential for successful AI model deployment.
Collaboration: Agile encourages close cooperation between AI teams and business stakeholders, ensuring AI models solve actual business needs.
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Practical Steps to Adopt Agile for AI
Here's how your organization can transition effectively:
Start with Training: Educate teams and leadership on Agile values and practices.
Pilot Small Projects: Begin with manageable initiatives to demonstrate Agile effectiveness in handling AI's complexity.
Continuous Learning: Regular retrospectives and reviews to iteratively improve AI processes.
Adopting Agile is not optional for AI—it's essential. Agile equips teams to manage uncertainty, innovate rapidly, and continually deliver business value from AI.
Are you ready to launch your AI initiative?
Explore our resources or schedule a consultation with AgileWoW today!
The article was originally published on AgileWoW