Does Agile Methodology work for AI projects? With many companies adopting Agile for software development, does trying to "make" Agile work for AI initiatives a mistake?
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I know that using agile methodologies such as SCRUM is a must for any Engineering project you could have.
Agile was created by Engineers for Engineers, instead of being a methodology based on processes, it is based on people.
If you are working for AI initiatives, it is the best way to accomplish your goals and be aware of everything that has to be done. You know that the product might change, and stakeholders might get a new idea that could deviate the project upside down without warning. Being agile allows the team to know what is required to be completed, to have all the information needed in the SCRUM cards, and to be productive without interruptions from third parties that won't provide value to the project.
I believe agile methodologies are very useful in AI projects. The continued evolution towards high experimentation will necessitate these methodologies in order for organisations to keep up with the pace of innovation required, albeit in a responsible and cost-effective manner.
Unfortunately, many have co-opted agile methodology and tried to turn it into something it isn't. The Agile methodology is successful because of one word - feedback. The tools and practices of agile all revolve around feedback. Things generally associated with agile - daily standups, test driven design, continuous integration, iteration reviews, retrospectives, code reviews, etc. - all are about ways to gain continuous feedback. Thus, in a world changing as fast as AI with the implications of poor design have wide reaching effects, early and often feedback is critical.
The implementation doesn't need to be complicated, but sticking with the core values: 1) individual snd interactions over processes and tools, 2) working software over comprehensive documentation, 3) customer collaboration over contract negotiation, 4) responding to change over following a plan, all are critical to successful AI projects with lots of diverse feedback.
AI & Automation Intentional Governance is a dynamic system of practices and intentional processes that focus on strategic goals and values while incorporating something that I call.
The ACT framework: Alignment, Clarity, and Transparency.
By integrating the needs of people with the needs of business, to drive organizational goals. We can ensure that the deployment of AI and automation systems supports both objectives.
Furthermore, by bringing AI and automation into business processes we may have the potential to bring significant benefits, but it also presents challenges related to ethics, bias, and transparency. To ensure that the use of AI and automation aligns with ethical principles and social values complies with legal and regulatory frameworks, and takes into account the needs of both the people and the business. It is crucial to have an intentional governance process in place.
As with any other project, the methodology is based on the project itself. you may find that Agile is appropriate or you may choose a hybrid waterfall/Agile approach. A lot depends on the level of defined scope, the level of skills of the development team in AI, etc...