08.08.2022 Executive Data Bytes – 'Who's ON First?' - Prioritizing your AI projects for success

Executive Data Bytes

Tech analysis for the busy executive.

Welcome to another edition of Executive Data Bytes! This week, we are discussing why you should prioritize your AI projects. 

Focus piece: “How to prioritize Artificial Intelligence (AI) projects: 6 tips” 

Executive Summary

Cloud storage and computation have enabled the efficient processing of huge data sets to draw critical insights using Artificial Intelligence (AI). However, with so many options, deciding where to devote your resources is the most difficult part. Learn more about how you can prioritize your AI projects in this article from The Enterprisers Project.

Key Takeaways

• Prioritization helps businesses achieve long-term success by taking all factors into consideration and making thoughtful decisions, rather than executing AI projects on an ad hoc basis.
• AI initiatives should be conceived and implemented by a diverse mix of teams, skills, and perspectives. This includes consensus on deciding where to start.
• Organizations that are made up of silos will lead to inefficiency, delays, and failed implementations. By bringing groups together, companies can find success.
• The fast-paced nature of digital transformation demands flexibility across the board. Business leaders must communicate changes in scope, timeline, and steps involved with their teams.

Focus piece: “Understanding Your Business Needs Before Building AI”

Executive Summary

Many businesses are considering how Artificial Intelligence (AI) and machine learning (ML) fit into their strategy. This article from Excella explores why your organization must decide on a set of needs and goals, and why it’s imperative to decide what level of accuracy is required and how timely insights need to be.

Key Takeaways

• AI/ML projects are challenging because they contain significant unknowns, require rapid exploration and fast feedback to learn and adjust, and involve uncertainty with the data, modeling algorithms, and the interactions of key variables.
• The ideation phase is essential to making this process effective. It should be short and focused, and aim to collect and assess ideas so that the team can more rapidly converge on the most promising option(s).
• AI projects should be planned out carefully to avoid committing to a solution approach too quickly and without clearly understanding expected benefits or considering alternatives that may have higher ROI.

Focus piece: “Why you should prioritize governance of ML and AI”

Executive Summary

Machine learning is a truly momentous time for the enterprise, with investments soaring and a growing number of use cases that can create tangible business value. However, many organizations are still struggling with important phases of the AI/ML lifecycle, including governance. In this article, TechBeacon explains the importance of Ai/ML governance.

Key Takeaways

• AI/ML governance is the overall process for how an organization controls access, implements policy, and tracks activity for machine learning models. It includes regulatory compliance and audit risk, but also forms the bedrock for minimizing risk while maximizing ROI.
• Some 58% of organizations said they struggle with governance, security, and auditability issues. That makes governance by far the top challenge that organizations currently face as they scale up their AI/ML initiatives and put more ML models than ever into production.
• Many organizations struggle with governance because they lack expertise and prescriptive advice. It's also early days for the AI/ML regulatory landscape, which means companies must invest significant resources in compliance.
• To ensure strong governance, you must get up to speed on best practices, stay current on the regulatory landscape, enlist outside expertise as needed, and ensure that all stakeholders have a seat at the AI/ML table and are effectively communicating and collaborating.

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