Leveraging AI to inform business operations and strategy at board level

AI is transforming business operations and informing strategy. 

As the pace of technological advancement accelerates, artificial intelligence (AI) has moved from the realms of science fiction into practical, everyday use in business operations. Companies that understand and harness the power of AI are finding themselves ahead of the curve. 

The opportunity for AI in business operations. 

When applied to business operations, AI offers the potential to optimise processes, reduce costs, and enhance productivity 

Artificial intelligence, in its broadest sense, refers to the capability of machines to mimic human intelligence. It involves the ability of systems to process vast amounts of data, identify patterns, make decisions, and even learn from those decisions to improve future outcomes. Check out this jargon buster to help you decode the AI buzzwords.  

In industries where manual processes have traditionally dominated, for example, AI can streamline operations by automating repetitive tasks.  

• In sectors like finance, AI-driven systems can manage transactions, monitor for fraud, and provide real-time analytics.  

• In HR, AI can be  used to sift through large volumes of CVs to identify potential candidates, speeding up recruitment processes. 

AI use in practice

For Liquid Friday, AI plays a key role in improving operational efficiency. From managing contractor engagements to ensuring compliance with regulatory frameworks, AI systems help to process data more quickly, accurately, and at scale. This ultimately leads to better service delivery for clients. 

Informing strategy at the board level. 

One of the most transformative uses of AI is in decision-making and strategy formulation.  

Traditionally, strategic decisions at the board level have relied on a combination of experience, intuition, and data. AI is changing this. AI can enable dynamic data analysis, helping leadership teams to base decisions on real-time insights. Liquid Friday are using AI-driven analytics to better understand the needs of contractors and clients, ensuring that service offerings are aligned with market demand. Additionally, by leveraging AI for forecasting and scenario analysis, there is the opportunity to create more robust business strategies that are adaptive to future market changes.  

There are two phases to this process: 

Data analysis (inspecting, cleaning, and interpreting raw data) 

Consider the wealth of data available to businesses today, from customer behaviour to market trends and internal performance metrics. AI can: 

• Sift through these vast data sets 

• Identify patterns that may not be immediately obvious to the human eye 

• Predict future trends.  

This can empower boards to make more informed decisions and adjust strategies quickly in response to emerging opportunities or challenges. 

Data analytics (using data to make informed business decisions) 

AI-driven analytics can then help boards to: 

• Better understand customer preferences 

• Make more informed decisions about product development or market expansion 

• Identify inefficiencies within internal processes that might otherwise go unnoticed.  

The real-time nature of AI insights means boards can respond proactively, rather than reactively, to changes in their operating environment. 

Artificial Intelligence vs. Machine Learning: What’s the difference? 

Though AI is a broad term encompassing a range of technologies, machine learning is a specific subset of AI that deserves special attention.  

AI refers to systems or machines that perform tasks typically requiring human intelligence, such as problem-solving, understanding language, or recognising patterns. Machine learning (ML), on the other hand, is a technique within AI where machines learn from data. Instead of being explicitly programmed for every possible outcome, ML algorithms are trained on large datasets. They improve their performance over time by identifying patterns and making predictions based on the data they’ve processed. 

A machine learning model, for instance, used in sales forecasting might analyse past sales data to predict future sales trends. As the system is exposed to more data over time, its predictions become increasingly accurate. This capacity to “learn” from data without being manually programmed for every new situation makes ML an invaluable tool for businesses dealing with complex data sets. 

Though AI is often powered by machine learning, not all AI systems utilise machine learning. For example, a simple rule-based chatbot may use predefined responses without learning from new data, and therefore does not employ ML. By contrast, an ML-based chatbot can evolve and improve its interactions with customers over time as it is exposed to more conversational data. 

AI use in practice

Liquid Friday use machine learning algorithms within AI systems to improve the accuracy of compliance checks and to identify potential areas for operational improvement. By learning from past data, these systems mean potential challenges can be  anticipated and addressed before they arise, ensuring smoother business operations. 

Ethical considerations. 

Whilst AI offers considerable benefits, its integration into business operations also brings ethical challenges.  

Boards need to ensure that the use of AI aligns with the company's ethical standards and regulatory requirements. Issues such as data privacy, transparency, and accountability are paramount. Businesses must be careful to avoid bias in AI systems, particularly in areas such as recruitment or customer interactions, where biased algorithms could lead to discriminatory outcomes. 

In 2018, Amazon scrapped an AI recruiting tool after it was found to be biased against women. The system, which was trained on resumes submitted to the company over a 10-year period, had learned to favour male candidates over female ones because many of the resumes came from men. The AI downgraded resumes that included words like "women’s" (as in "women’s chess club captain") and preferred resumes with more traditionally male-dominated experiences. 

Conclusion.

The integration of AI into business operations and strategy is no longer a futuristic ideal—it is a present-day reality that offers immense potential. From improving efficiency in operations to providing data-driven insights at the board level, AI is reshaping how businesses function and compete.  

As businesses continue to embrace these technologies, understanding the nuances between AI and machine learning will be crucial for leveraging their full potential. The key to success lies in harnessing AI’s capabilities responsibly, ensuring that it drives not only efficiency and innovation but also ethical and sustainable growth. 

 

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