The 3 P’s of AI Success: Preparation, People & Perseverance

Commence Nkomo
Chief Data & Analytics Architect

When executing AI projects, you will at some point need to have technical resources. However, the contribution of technical resources should be a sliver in time compared to the entire project. AI projects’ success mainly hinges on these 3 P's: Preparation, People and Perseverance. Stay Digi-Data Connect!

Executives Doubt AI

In a 2023 poll conducted by Boston Consulting Group (BCG), 66% of 1400 executives said they were ambivalent about or outright dissatisfied with their organisation's progress on generative AI. They cited a shortage of talent, unclear roadmaps, and an absence of strategy around deploying generative AI responsibly. Yet, 89% said that generative AI still ranked as their “top three” technology initiatives for their companies in 2024.
Harvard Business Review and Investopedia reported that most executives have little faith in their organisation’s expertise in implementing AI, are concerned about data, and/or don't feel comfortable using the insights coming from their analytical systems.

See the grim findings.

AI implement doubt

71% do not believe their organisation has the expertise to implement AI.

Data & AI Concerns

57% have concerns about data security and the technology’s bias and accuracy.

Analytics Trust Issues

67% are not comfortable assessing or using data from advanced analytics systems.

Overcoming Inexperience: The Key to Successful AI Implementation

	Overcoming AI Adoption Hurdles Companies don’t know how to start, so they don’t start. They let lack of inertia get in their way.
	Breaking AI Decision Paralysis Companies start but can’t get consensus on where to go and how to start, so analysis paralysis stops them from landing on a deployable use case.
	AI Focus Challenges Companies have too many competing strategic priorities, so they can’t get the focus, funding, and necessary facilitation to get organisational alignment and buy-in.
	AI Projects Need Expertise Companies start a proof of concept but without the right resources, guidance, and expertise from experts. Something that should take 2 to 5 months takes 6 to 9 months. As a result, they lose the attention span of leadership and support dissipates.
	Scaling AI beyond POC Companies complete the POC and realise the business value, but they don't know how to scale and operationalise the capability because of the organisational hurdles that come with workforce change management and maintenance.