Due to the lack of in-house capabilities, the high uncertainty of machine learning projects is becoming more harder and unmanageable. Unstructured data is useless to generate insights. In most of the organizations, everyone knows the implementation of artificial intelligence theoretically but practically, no one knows and this lingering question will always interrupt “Is machine learning model accurate enough?” and are we ready for production? A framework in agile project management can handle uncertain projects i.e. machine learning. What is agility? It is the state of high speed, adaptiveness while managing risks and high responsiveness. In software organizations, agile software development is common practice now. Main challenges in implementing machine learning are enlisted below:
1- Lack of in-house capability
2- Quest for a highly accurate model
3- High Uncertainty
We can use agile for prosperous machine learning. Here are 5 steps:
Start with minimum investment:
It seems very easy to apply artificial Intelligence in business but it is not. License for AI costs a huge amount. Even a single solution for AI can cost up to $200k depends upon the size of the enterprise. So start digitalizing an organization or implementing AI gradually with minimum investment.
Shift to agile contracting:
Fixed price and contract will not work. The agile project management process will deliver solution and value. Traditional approaches will not work for this after choosing AI that will cater to your business needs. The idea of agile driven contracting is you will only pay after the delivery of the value. In terms of business value, costs, implementation risks and expenses, supplier and the customer both will define common postulates in agile contracts. An indictive fixed price scope is agreed on the basis of these assumptions and both are yet not contractually bound.
Principles of agile contracting include: Align Incentives, Collaborate and be transparent and Built-in ability to inspect and adapt.
Design thinking process was started by Hasso Plattner Institute of Design that is also known as The Stanford Design School (d.school). According to them, this process includes three steps Understand, Improve and Apply. It emphasizes the significance of compassion in doing the project, this emanates value of agile project management. While gathering requirement and designing them keep in mind that identity verification for your business is crucial and an organization should not neglect it and by understanding current customer pain-point an organization can combat that challenge rather than direct zooming in machine learning functionality. This will help you reduce cost while implementing AI and improve customer experience.
Design thinking process includes: Emphasize, Design, Ideate, Prototype and test. Testing is the most important phase that helps in understanding impediments, what works and what is useless and quick iteration.
The contrast between traditional and predictive project management models is huge. Current predictive solutions are effective in handling big unknowns with streamlined processes of planning, development review and retrospective.
Build a machine learning model:
Build the machine learning model with the geeks with the assistance of subject matter experts instead of being reliant on the technical team. Machine learning and agile is a great match that can’t be neglected. A machine learning project will never work with a traditional approach.
With no clear business needs conducting research is a certain recipe of failure. Move further according to business need, without it conducting research is a stick in the wheels of agile. Without clear business needs, there is no way to commit on anything, no clear way to define iterations and this is the main reason for not proceeding. An organization does not have the privilege to wait for feedback in this fast-moving startup business. Validation approach is key to success without validation a business will not survive.