10 Ways To Derail
An AI Program
OPINION
ProArch
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Despite big investments, many organisations get disappointing results from their AI and analytics efforts. What 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 realising 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 AI 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 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.

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 identifying and addressing the ten red flags presented here, such companies have a second chance to get on track.

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