Launching an AI/Data Science enterprise is a monumental task, not unlike the challenge of opening a recent restaurant. The analogy isn’t merely illustrative; it underscores the complexity and demands of such an undertaking. On this endeavor, making a compelling model (corresponding to crafting a delicious pizza) is only one aspect of the job. The more significant challenge lies within the effective and efficient delivery of this model (akin to serving it adeptly in a bustling restaurant). This process, if mismanaged, can result in project failure.
Recent data reinforces this sobering reality. As per a 2022 article by the International Institute of Business Evaluation™ (IIBA®), the failure rate for large data projects, analytics, and Artificial Intelligence stands alarmingly high at 85%, in accordance with Designing for Analytics. Moreover, VentureBeat reports that a staggering 87% of knowledge science projects never make it past the drafting board and into the production stage.
These figures shouldn’t discourage you but relatively function a catalyst. They underscore the urgency of thoroughly addressing certain key questions before you dive headfirst into your enterprise, to make sure you end up amongst the successful 15%. Only when these critical considerations have been satisfied are you able to begin to implement project management methodologies like Agile, and UX processes. We’ll delve more into these techniques in our upcoming blog post.
As with all enterprise, you could ‘begin with the tip in mind’ in your AI/Data Science project. Like a restaurant owner who needs a transparent vision before starting construction, you could define the objectives of your AI or Data Science project. Are you aiming to maximise accuracy, minimize processing time, or cut costs? The model’s goals must be articulated clearly and must be measurable.
After establishing a transparent goal, imagine the perfect scenario for delivering the model. How and where will the model be deployed? Who will use it, and what infrastructure and competencies are needed to support it? Do you may have the resources to execute a posh machine learning model? Do the end-users have the obligatory technical skills to operate it? These are crucial questions that may significantly impact the success of your project.
Similar to the success of a restaurant hinges on customer satisfaction, the triumph of your AI/Data Science project depends upon end-user satisfaction. Do the model and its outputs align with the users’ needs? Is it user-friendly and seamlessly integrated with their existing workflows and systems? Understanding your users’ preferences and constraints can aid you design a model that’s each helpful and user-friendly.
Excitement about an revolutionary concept often leads people to unexpectedly construct projects around it, only to appreciate later that the concept wasn’t as fruitful as they initially thought. So, before committing resources to a full-scale AI/Data Science project, it’s prudent to validate your concept on a smaller scale. Create a prototype of your model and test it with a small group of users, or conduct a pilot study to judge the feasibility and potential impact of your project.
In conclusion, embarking on an AI/Data Science project without addressing these key questions is like starting a restaurant with out a solid marketing strategy. It’s a dangerous strategy which may work, but you may substantially enhance your project’s possibilities of success by rigorously considering your objectives, the serving environment, user satisfaction, and validation.
To make sure that you’ve covered all of the essentials, we’ve compiled an inventory of pertinent questions as a takeaway:
- What’s the precise aim of the project? How will we define and measure success?
- Who’re the end-users of the model? What are their skills, requirements, and constraints?
- Can we envision the perfect scenario for delivering the model? What does successful implementation appear to be within the end-users’ environment?
- How will the model be used? Will it integrate seamlessly into the present workflow, or will it establish a recent one?
- What are the serving requirements? What infrastructure and resources are required for deploying the model?
6. Is our data sufficient and suitable for our objectives? Do we’d like to assemble more data or enhance the standard of our existing data?
- What potential challenges and obstacles might we encounter? How can we mitigate them?
- How can we validate our idea before investing heavily in it? Can we create a prototype or conduct a pilot study?
For an optimized user experience, consider these additional questions:
- How can we make the model as user-friendly as possible? Can we simplify the interface or the technique of using the model?
- How can we make sure that the users understand the model’s outputs? Can we offer explanations or visualizations to help interpretation?
- How can we make the model integrate easily into the users’ existing workflows? Can we customize it to suit their specific needs and constraints?
- How will we collect and incorporate user feedback? Can we establish channels for users to precise their needs, concerns, and suggestions?
- How can we make sure that the model continues to fulfill the users’ needs as they evolve over time? Can we make the model adaptable or update it recurrently?
Before you initiate the event phase, it’s crucial to share and discuss these questions along with your team. Remember, it’s a collaborative process: the more perspectives you concentrate on, the higher your possibilities of making a successful AI/Data Science enterprise. Once these considerations are addressed satisfactorily, you’ll be ready to herald Agile and UX processes to your project management approach, a subject we are going to explore in our next blog post.
