Investing in AI can pay off significantly for those who are exploring ways to be more efficient and effective. But without defined objectives or success criteria, AI investments can also be costly. Further, unmanaged risks or failures can cost more than money - they can destroy your credibility and reputation.
What should you consider when teams tell you they’ve got AI “covered”? To increase your chances of success with AI, align your organization and projects around the following actions:
- Define clear goals: Articulate the objectives and expected outcomes of your AI projects. For example, include non-modeling approaches as benchmarks and establish performance metrics that will prove that projects are successful. This will help align investments with your strategic vision and make sure that AI initiatives are focused and value-driven.
- Foster collaboration: AI projects require cross-functional expertise. Assemble a team of experts in AI and machine learning who have the necessary skills and experience to drive successful projects. This team should include data scientists, engineers, risk, and business domain experts who can collaborate effectively.
- Drive best practices: Model teams may default to their favorite tools or want to try out the hottest new tech, which can lead to a lot of waste and increased risk exposure. Create clear expectations and repeatable structure around your entire modeling program with a holistic governance approach.
- Prioritize data: High-quality data is crucial both for training AI models and for optimal performance once they’re live. Invest in data collection, cleaning, and preprocessing upstream to make sure that your models have access to the best possible data. Once in production, validate both input and output data that models process against clear thresholds to identify problems early.
- Test and iterate: Encourage teams to test AI models in real-world scenarios that go all the way to production to understand the technical lift of the last mile. Apply governance and model validation workflows throughout the lifecycle to learn how to optimize modeling systems and deliver consistent governance.
- Uphold governance: AI technologies raise ethical concerns, such as unseen bias, privacy violations, and lack of transparency towards modeling outcomes. Challenge all stakeholders to identify the ethical edges early and adhere to governance steps continuously during the project lifecycle.
- Stay vigilant and adapt: AI risk is not a mystery. Look for opportunities to adapt your existing processes and practices for low-risk AI use cases, and invest your time in managing and mitigating the new risk vectors of your consequential use cases.
Chances are that someone in your organization has completed part of the steps above; or they’ve encountered roadblocks across the business while trying to get this information.
If this sounds like you or someone you know, work with an experienced partner like Monitaur for managed software and expedient alignment across these steps. You can increase the likelihood of success in AI investments and mitigate the risks often associated with AI project failures.
