Artificial Intelligence (AI) has the potential to massively improve business operations, but many organisations are questioning if they are ready to embrace this new technology. This blog explores some of the factors businesses need to consider before adopting AI.

Why Quality Data is Key to Making AI Adoption a Success

The true power of AI is not inherent in the algorithms themselves, but rather in the fuel that drives them: data. The maxim “garbage in, garbage out” rings particularly true in the context of AI. Without high-quality data, even the most sophisticated AI models will produce unreliable, and potentially harmful, results.

Quality data is the bedrock upon which successful AI adoption is built. It encompasses accuracy, consistency, completeness, and relevance. Accurate data ensures that AI models learn from factual information, leading to precise predictions and informed decisions. Consistent data eliminates discrepancies, preventing confusion and ensuring uniformity across analyses. Complete data provides a comprehensive picture, avoiding biased outcomes due to missing information.  Relevant data also ensures that the AI focuses on the most pertinent factors, maximising its efficiency and effectiveness.

The implications of poor data quality are far-reaching. Imagine an AI system designed to predict customer churn, but trained on incomplete and inaccurate customer records. The resulting predictions would be flawed, leading to wasted resources and missed opportunities.

The trust and acceptance of AI within organisations hinges on the reliability of its outputs. When employees witness the AI making accurate and insightful decisions, their confidence in the technology grows. Conversely, if the AI produces inconsistent or erroneous results, scepticism and resistance are likely to prevail.

Therefore, organisations seeking to harness the transformative power of AI must prioritise data quality. This involves investing in robust data management systems, implementing rigorous data validation processes, and fostering a culture of data integrity. Only by ensuring they have high-quality data can businesses unlock the full potential of this powerful technology and achieve sustainable success.

Integrating AI into Existing Systems

Another challenges faced is the initial integration when companies add AI to their current workflows.

Telic works with Tungsten Automation, which emphasises the importance of “in-context AI,” meaning embedding AI into existing software rather than disrupting systems entirely.

Tungsten advocates for an “evolution, not revolution” approach to AI adoption, suggesting that a gradual rollout helps avoid disruptions. However, even with a well-thought-out plan, integration can be complicated, especially if the original data systems aren’t optimised for AI.

AI works best when integrated into existing systems that are data-ready. Poor data systems can hinder the effectiveness.

Building Trust

While AI has become more common in the workplace in recent years, employees who are wary of new technologies ‘taking over’ may struggle to accept its uses.

Transparency about how AI decisions are made are imperative to get teams onboard with new processes. Once this is done, small with AI additions can help employees feel more comfortable with the tasks. However, the trust in AI systems depends on the quality of data. If it isn’t accurate, the AI’s decisions will be questionable and staff may wonder how realiable these new systems will be.

Choosing a Platform

Choosing the right AI platform can be daunting. Telic is a provider of Tungsten, which specialises in selecting the right AI tools. The company works with businesses to discuss their needs, all while  considering features, integration capabilities, and  of course, cost.

Each business has unique needs, and again, poor data can undermine even the most advanced AI system.

Without the right data management practices, AI platforms can struggle to deliver accurate insights.

For example. Tungsten’s “TotalAgility” platform highlights an essential aspect of AI adoption—data management. Effective data management is key to ensuring AI models are trained on reliable and accurate data. Poor data—whether it is inconsistent, incomplete, or inaccurate—can lead to poor AI performance and undermine its potential benefits.

Investing in data management systems is just as critical as choosing the right AI platform.

Great Data Equals Great AI

As AI continues to transform industries, businesses must focus on ensuring that the data used to power these systems is of the highest quality. Integrating AI into existing workflows, selecting the right platform, and building trust with employees all depend on effective data management. Without the right data, AI can’t live up to its potential.

Find Out If You’re Ready For AI in 3 Questions

Do You Know Which Business Problems You Want to Solve with AI?
Yes or No

Is Your Data Structured, Clean and Well Organised to Support AI Initiatives?
Yes or No

Do You Know Who Will be Responsible for AI Governance in the Organisation?
Yes or No

Do Your Employees Need AI-Related Training or Upskilling?
Yes or No

About the Author

Neetika

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