The Promise and the Reality of AI

Every executive is talking about artificial intelligence. From predictive analytics to generative AI, the promise is huge. Yet reality often disappoints. Gartner reports that up to 80 percent of AI projects fail to reach production.

Why? It usually comes down to the basics. Poor data quality, inconsistent applications, and weak organisational knowledge prevent AI projects from delivering results. Companies rush to build models before their foundations are ready.

The Hidden Barriers to AI Adoption

Poor Data Quality

AI needs reliable, structured, and consistent data. Many organisations have fragmented systems, duplicates, and gaps that make the data unfit for purpose.

Inconsistent Applications

Legacy systems and siloed applications undermine AI projects. If your CRM, ERP, and marketing platforms are not aligned, the AI has no reliable foundation.

Knowledge Gaps

Staff often lack a clear picture of where data lives or how processes actually work. Without organisational knowledge, AI projects operate in the dark.

How Managed Services Bridge the Gap

Continuous Data Hygiene

Managed service teams work on your applications every day. They clean, standardise, and monitor data continuously. This creates a reliable source of truth for AI initiatives.

Example: A retailer preparing for AI-driven customer segmentation discovered their CRM data was inconsistent. Their managed service team cleaned and normalised the data across regions. This made segmentation accurate and allowed campaigns to target customers with precision.

Application Optimisation

By maintaining and optimising business applications, a managed service ensures they integrate correctly. This reduces silos and makes cross-platform AI initiatives possible.

Example: A manufacturing company wanted predictive maintenance using AI. Their ERP and asset management data were fragmented. The managed service team standardised the data feeds, allowing the AI to forecast maintenance accurately and cut downtime by 25 percent.

Knowledge Retention

Dedicated managed service teams retain institutional knowledge. They understand where data lives, how systems interact, and the history of decisions. This knowledge prevents mistakes and accelerates AI adoption.

Strategic Benefits of a Managed Service for AI

Faster Time to Value

With applications clean and data reliable, AI models can be built and deployed more quickly.

Lower Risk of Failure

Fixing hygiene issues reduces the chance of AI projects being abandoned mid-way.

Scalable Innovation

Once the foundation is strong, companies can scale AI initiatives across multiple departments with confidence.

Practical Steps for Companies

1. Audit Current Systems
   Identify where data is inconsistent and which applications need attention.
2. Engage a Dedicated Managed Service
   Ensure your team has the mandate to focus on hygiene, integration, and knowledge capture.
3. Align AI Goals with System Readiness
   Don’t start AI projects until the underlying applications and data are stable.
4. Measure Improvement Over Time
   Track improvements in data quality, system integration, and AI performance.

A Stronger Foundation for the Future

AI success isn’t just about choosing the right model or platform. It starts with the quality of your applications and data. Managed services provide the daily care and attention that AI needs to succeed.

By investing in hygiene now, you avoid wasted budgets, frustrated staff, and failed pilots. Instead, you create the conditions for AI to deliver measurable results.

For businesses serious about AI, a managed service isn’t optional. It’s the foundation that makes everything else possible.

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