In July–August 2019 Issue of Harvard Business Review, the authors of “Building the AI-Powered Organization” present a critical look at factors impacting the success for organizations that are trying to move to the next level of getting value out of their data using advanced techniques based on Artificial Intelligence and Machine Learning.
As TM Data ICT Solutions, we also found the reasons that lead to failure of AI-based projects illuminating. Among the top 10 reasons for failure the authors listed, we think the following are the most important and outline how we can help you avoid these strategic pitfalls:
- The organization lacks a clear understanding of advanced analytics, staffing up with data scientists, engineers, and other key players without realizing how advanced and traditional analytics differ:
- As TM Data, we’re ready to guide you in this respect so that you’ll be able to invest your resources in an optimal combination.
- The organization has no strategy beyond a few use cases, tackling AI in an ad hoc way without considering the big-picture opportunities and threats AI presents in their industry:
- Our team always takes a holistic and unifying approach, because we know that this is the best way to co-innovate with our customers.
- The organization doesn’t clearly define key roles, because they don’t understand the tapestry of skill sets and tasks that a strong AI program requires:
- TM Data can help you for setting up roles and organizing them. We know that training people, and finding correct people for the relevant roles is very critical especially in the beginning of the project.
- They lack “translators,” or experts who can bridge the business and analytics realms by identifying high-value use cases, communicating business needs to tech experts, and generating buy-in with business users:
- Our experienced team members know how important and strategic proper communication is, especially when it comes to complex technology and AI-driven projects. This is why we’re ready to go the extra mile, helping you to bridge the gap between technology and business.
- They squander time and money on enterprise-wide data cleaning instead of aligning data consolidation and cleanup with their most valuable use cases:
- We know all too well the countless resources spent in big bang enterprise-wide projects. This is why we like to focus on your projects with the most critical business value, while always keeping an eye on a unified and long-term approach.
- The organizations fully builds out analytics platforms before identifying business cases, setting up architectures like data lakes without knowing what they’ll be needed for and often integrating platforms with legacy systems unnecessarily:
- Based on our experience, we always strive for customer satisfaction, and this means we’re always on the lookout to prevent data swamps.
- The organization fails to focus on ethical, social, and regulatory implications, leaving themselves vulnerable to potential missteps when it comes to data acquisition and use, algorithmic bias, and other risks, and exposing themselves to social and legal consequences:
- TM Data will help your organization for ethical AI, because we don’t want your reputation to be hurt and your company / brand appear on the national media because of careless application of data and algorithms.