How Businesses Are Laying the Groundwork for Transformative AI Technology

Artificial Intelligence (AI) and Machine Learning (ML) offer powerful tools to help businesses innovate and operate more efficiently.

However, a successful AI strategy requires more than just technology; it calls for readiness, data quality, and continuous adaptation.

Let us share key insights from industry leaders on how to prepare for, and implement AI, along with practical steps for success.

Building AI Readiness – A Five-Level Framework

  • Assessing AI Maturity
    AI readiness is essential for successful implementation, and many organisations are finding a five-level readiness framework useful for assessing where they stand. This framework starts from “unaware” and progresses to “optimised,” offering companies a roadmap for scaling AI applications responsibly.

  • Practical Tip: Conducting SWOT Analyses
    To gauge AI readiness, 60% of companies participating in the roundtable recommended using a SWOT analysis tailored to AI and ML. This helps identify strengths, weaknesses, opportunities, and threats specific to each organisation’s context, ensuring a targeted and effective AI strategy.

High-Value AI Projects – Focusing on Business Impact

  • Identifying Strategic Projects
    Leaders agreed that AI should focus on high-impact areas. With three main types of AI learning (supervised, unsupervised, and reinforcement learning), businesses can apply AI in various areas, from customer sentiment analysis to operational optimisation. For instance, organisations using AI for predictive maintenance saw a 25% reduction in downtime within the first six months.

  • Case Study: Sentiment Analysis in HR
    One company highlighted their use of natural language processing (NLP) to analyse employee exit interviews. By applying sentiment analysis, they were able to identify issues affecting retention and adjust HR policies accordingly. This approach has provided data-driven insights to the HR team, enabling more responsive planning.

Data Quality – A Cornerstone for AI Success

  • The Importance of Clean Data
    Data quality is critical, with leaders noting that data cleaning and preparation can take up 50% to 80% of AI project time. High-quality data ensures accurate model outputs and reduces biases. For instance, if an AI model’s training data is skewed, it can misinterpret or misclassify information, affecting decision quality.

  • Balancing Bias and Diversity in Data
    Roundtable participants highlighted the need to balance datasets to avoid inherent biases, especially in customer-facing applications. For example, ensuring diversity in training data helps models cater to a broader range of customers, thus avoiding the risk of alienating certain user groups.

Transparency and Explainability – Ensuring Trustworthy AI

  • Why Explainability Matters
    Ensuring that AI outputs are understandable to human decision-makers was a major focus. Leaders emphasised that AI outputs should be explainable and actionable, which can be achieved by using range predictions rather than specific figures. This approach improves AI’s usability across teams and helps non-technical staff make informed decisions based on AI outputs.

  • A Human-Centric Approach
    Keeping AI human-centric was a top priority, with organisations focusing on transparent results that relate directly to business goals. Ensuring human oversight and interpretability of AI decisions is key for building trust and for making AI-driven insights practical for end-users.

Machine Learning Models – Choosing the Right Tool for the Job

  • Utilising Traditional ML Models
    Many companies find value in using established models like Random Forest for data analysis. These models are known for their reliability in handling large datasets, as demonstrated in one use case where Random Forest was employed to perform root cause analysis in manufacturing, reducing error rates by 15%.

  • Applying Reinforcement Learning for Dynamic Problem Solving
    Reinforcement learning is valuable for continuous optimisation, particularly in sectors such as logistics and energy. Leaders at the roundtable noted that while reinforcement learning requires substantial data, it adapts well to changing environments, making it ideal for applications that benefit from real-time adjustments.

Practical Applications and Success Stories

  • AI in the Energy Sector
    A technology director shared their experience using AI in long-term energy projects, emphasising the importance of data continuity. By tracking and preserving data throughout the project life cycle, they maintained insights that contributed to more reliable energy forecasting and reduced project delays by 20%.

  • HR and Natural Language Processing
    NLP in HR departments is proving to be an effective tool. By analysing data from exit interviews, organisations are identifying patterns in employee feedback, which allows them to improve the work environment. This analysis, when combined with sentiment tracking, has increased employee retention rates by up to 30% in some cases.

Laying a Solid Foundation for AI As organisations embrace AI and ML, they’re discovering that success hinges on more than just having the technology in place.

By focusing on readiness, data quality, transparency, and the right ML models, businesses are building a foundation for sustainable AI initiatives that drive real value.

With continuous improvement and a commitment to data accuracy, organisations are preparing for a future where AI becomes an indispensable tool across industries.

Aine Donnelly