Despite big investments, many organizations get disappointing results from their Artificial Intelligence program (AI) and analytics efforts. But what exactly makes programs go off track?
Companies set themselves up to fail when:
1. They lack a clear understanding of advanced analytics, staffing up with data scientists, engineers, and other key players without realizing how advanced and traditional analytics differ.
2. They don’t assess feasibility, business value, and time horizons, and launch pilots without thinking through how to balance short-term wins in the first year with longer-term profits.
3. They have no strategy beyond a few use cases, tackling Artificial Intelligence in an ad-hoc way without considering the big-picture opportunities and threats AI presents in their industry.
4. They don’t clearly define key roles, because they don’t understand the tapestry of skill sets and tasks that a strong AI program requires.
5. 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.
6. They isolate analytics from the business, rigidly centralizing it or locking it in poorly coordinated silos, rather than organizing it in ways that allow analytics and business experts to work closely together.
7. They squander time and money on enterprise-wide data cleaning instead of aligning data consolidation and clean-up with their most valuable use cases.
8. They fully build out data 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.
9. They neglect to quantify analytics’ bottom-line impact, lacking a performance management framework with clear metrics for tracking each initiative.
10. They fail 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.
What's your ROI?
Many companies, caught up in the hype, have rushed headlong into initiatives that have cost vast amounts of money and time and returned very little. By taking a little time, thinking through what value means, and what success might look like, and then defining a program that takes into account accuracy, speed, regulations, and ethics, Artificial Intelligence programs will stop being frustrating investments full of promises that never deliver. Indeed, it will become a way for organizations to better understand themselves, their risks, and their customers.
By identifying and addressing the ten red flags presented here, such companies have a second chance to get on track. If you'd like to discuss any of these common pitfalls in more detail, get in touch or learn more about our data platform services.
