Stakeholder Buy-in Helps AI/Machine Learning Projects Succeed
- Posted by GM, Digital Solutions
- On January 7, 2021
- AI, AI Project, Business Goals, Machine Learning, Machine Learning Project
As machine learning and AI continue to transform the business world, companies are increasingly exploring the benefits of this emerging innovation. Unfortunately, not everyone is seeing the full potential of AI and ML.
As with any project, stakeholder buy-in plays a critical role in the initiative’s eventual success. Gaining early buy-in ensures your project has the personnel, budget, and resources to fully realize its goals.
Failing to involve a financial stakeholder could mean your project will not gain sufficient financial resources to build a proof of concept and eventually scale it into production. The lack of a compliance-related stakeholder could cause an ethical issue or shifting trends in legislation related to the project’s use of AI to hamper its implementation.
Ultimately, an AI project’s leadership must consider the importance of stakeholder support for the success of their initiative. Let’s look more closely at why this buy-in remains a critical piece in the success of any AI/machine learning undertaking.
Stakeholder Sponsorship Gives a Machine Learning Project the Personnel it Needs
Without technical professionals experienced in the technology, a machine learning project is likely doomed to fail. Software engineers, data analysts, and the data itself all matter. This concept also applies to project managers, whose experience in successfully completing AI-related projects is a must.
Additionally, an experienced project manager boasts the ability to properly scope the project timeline. Having enough personnel on the team matters little if the deadlines are too aggressive. A strong PM also ensures easy access to business subject matter experts to ensure questions are answered quickly; preventing the delays that are a major risk to any technical project.
A kick-off meeting including the stakeholders, PM, and project team ensures everyone understands their roles and responsibilities. Stakeholder involvement in this meeting helps inspire the team to give their best effort. Providing details on the project timeline with all attendees prevents surprises when it comes to deadlines.
Ensure the Machine Learning Project has the Necessary Budget for a Successful Outcome
Once again, any AI project requires the necessary resources to ensure success. In addition to the personnel considerations highlighted above, providing a sufficient budget is also critical. The involvement of a financial stakeholder is necessary to ensure the project receives the necessary funds. Additionally, they make certain the goals of the project align with the financial goals of the organization, and there is sufficient ROI upon completion.
Remember, the project’s budget needs enough funds for a proof of concept as well as a successful deployment into production. The stakeholders must understand that a successful proof of concept doesn’t mean the project is finished. Providing enough funding for production deployment remains a critical requirement, reinforcing the importance of a financial stakeholder working closely with the project team.
Finally, make sure the stakeholders and project managers agree on how the project’s budget relates to the technical goals of the initiative. This analysis must include project initiation, proof of concept, deployment, and post-project maintenance,. Obviously, the ultimate success of the effort becomes risky without the fiscal resources to fund the entire timeline.
Ethical AI and Compliance Both Influence Stakeholder Buy-In
As an emerging technology, AI has its own set of ethical and compliance considerations. For example, how a machine learning model uses personal data, especially PII, must be clearly documented. This documentation becomes more important in the event of new legislation restricting the use of personal information.
It is important to involve stakeholders who understand this evolving risk. Otherwise, you could learn in the middle of your project that the data you are using or the machine learning model you are developing does not meet compliance requirements.
Not having stakeholder buy-in from the start can create major complications for AI projects.Graham Holt, GM Digital Solutions: How to Use Risk Analysis When Evaluating an AI Consulting Partner
AI-related applications with a global footprint undoubtedly require extra analysis regarding the use of personal data. International standards and laws for using PII now come into play, and can potentially differ on a country by country basis. Note that these standards also apply when training a machine learning model for a proof of concept.
Data security is another important issue to project stakeholders. Managing internal security might require a new tool, which could mean additional costs and a delay to the project. Keep in mind the healthcare, financial, and telecommunications industries typically demand tighter data security. Ultimately, identifying compliance related issues early in an AI project – before the POC – is essential.
When your company considers an initial project using AI and machine learning, understanding the myriad of risks becomes vital. With many successful AI initiatives, Daitan offers valuable insights on how to complete projects – from initiation to deployment. We authored an eBook, How to Use Risk Analysis When Evaluating an AI Consulting Partner, to help your organization take its project from proof of concept to production. Download it at the following link.
If you are searching for a partner on your next AI project, look no further than our talented and experienced team.