Currently, more and more industries implement AI solutions to improve efficiency of their operations – e-commerce, healthcare, automotive, defense and aerospace, and the list goes on. The demand for executing AI projects is increasing, and this trend is here to stay. At the same time, these projects require significant investment and are sometimes risky, so the price for mistakes is rather high.
How to ensure that your project management efforts will be crowned with success when it comes to AI initiatives? Let’s take a closer look at their critical success factors.
Efficient management of resources and their competences
To ensure successful completion of any project (and AI initiatives are no exception), a project manager needs to be sure that they are staffed with the right resources – i.e., those who have capacity, necessary skills and experience. At the same time, AI projects as well as other initiatives dealing with digital technologies suffer from a lack of digital workforce – the competition for them is very high. In this case, effective utilization of available resources and managing their competences come into sharp focus, and here’s why.
- Competence management makes it possible to create a pool of employees whose knowledge, skills, and experience help organizations achieve their business objectives.
- Ensuring that people work at their full capacity can eliminate the need to hire extra resources.
- Proper resource management also involves managing employees’ workload – ensuring that they work at their full capacity but aren’t overwhelmed with their tasks. It increases productivity and contributes to better engagement, which in turn is a prerequisite for retaining talent in a company.
Management of project resources, their competences and workload becomes maximum efficient with a resource management tool like Epicflow. In addition to competence and workload management, it helps a project manager keep track of all projects in the pipeline, timely detect bottlenecks, and make the right decisions under conditions of uncertainty.
Clearly defined goals of AI implementation
In an attempt to get maximum benefits of AI deployment and increase their competitiveness, organizations can overstep themselves and try to implement as many innovations as possible. However, having multiple innovations in one project or implementing them in a company’s operations can be too complex and even risky. So, to succeed with any AI project (whether it’s deploying an AI-driven technology or creating an AI-powered model), the company’s management should decide on the highest priority goal that can be achieved with the power of artificial intelligence. For example, why does a company need this particular solution? What problem is going to be solved? What business need is going to be met? How will the final deliverable be integrated with other company’s products/services? Determining the precise and realistic goal for each project and prioritizing them will significantly increase the chances for successful delivery of AI initiatives.
Ensuring data preparation
Data collection and analysis are an integral part of any AI project’s lifecycle. Data is one of the drivers of AI initiatives’ smooth workflow and successful outcomes, so it’s critically important to ensure its thorough preparation. Raw data cannot be processed by algorithms – as a rule, it has various inconsistencies that will make it impossible to derive any meaningful insights from it.
Data preparation aims to get raw data ready for processing by algorithms. In particular, the steps of this process include but aren’t limited to:
- Deleting junk data;
- Correcting the datasets;
- Changing formats;
- Transforming the data.
The process of data preparation can be automated, which also increases the accuracy of the procedure. Therefore, the main idea to keep in mind when it comes to AI projects’ success is that accurate data is a prerequisite for valuable insights and further efficient workflow.
Participation of subject matter experts
In addition to data scientists, successful AI projects should involve the participation of subject matter experts (SME). Notably, their involvement is recommended throughout the whole project lifecycle – from data collection to models’ testing and assessing the project’s outcomes. Why is that important?
- Projects executed within one domain can differ significantly, and without subject matter experts’ assistance, the developed product may not deliver the expected value.
- SMEs help understand what is really expected from the developed model: e.g., what features will simplify their work? It’s a good idea to find it out before the project starts.
- SMEs can assist in determining definitions required for an AI project – their accuracy is the other prerequisite of a properly functioning model.
- They can provide the project team with valuable feedback on the model’s functioning and in such a way contribute to maximum efficient project outcomes.
Selecting the right PM methodology
The right approach to managing AI initiatives becomes critical for their success. AI projects are still rather complicated to implement and are accompanied with great uncertainty. So, Agile methodology and its numerous variations will be a good choice for managing AI initiatives, and here’s why.
First, Agile lifecycle consists of small and manageable increments (sprints) with a definite timeline (lasting usually for 2 weeks) and involves delivering a minimum usable product. Such an approach makes it possible to overcome the complexity of AI projects and deliver particular results regularly.
Second, Agile principles involve collaboration of customers with the project team – the product is developed based on their feedback. It makes it possible to find out whether the team has been on the right track regarding the model’s functionality, whether it delivers real value to the customers and solves their problem.
Responsiveness is one of the biggest benefits of Agile methodologies. Like other digital technologies, AI is developing quickly, so to ensure that the developed product isn’t outdated by the end of a long project lifecycle, Agile with its frequent iterations based on the customers’ needs becomes a good pick.
Conclusion
AI projects are those that involve implementation or development of an AI-driven technology that aims to increase the efficiency of a company’s operations, improve customer experience, and help organizations increase their competitiveness. These initiatives are rather complex and risky, so the project team should be aware of the critical success factors that will drive AI projects to successful completion. They are the following:
- Clearly defined goals – understanding the problems that will be addressed with AI;
- Effective resource management – it will ensure that the project is staffed with employees with required competences;
- Data preparation – accurate data is the prerequisite for getting valuable insights and efficient project workflow;
- Involvement of subject matter experts – they will help the final product become maximum useful for the domain specialists;
- Applying Agile methodology – it will reduce the complexity of AI initiatives and make it possible to create models that will meet customers’ needs to the full.
