5 Reasons to Leverage AI Business Intelligence to Maximise the Impact of AI Automation

Back in 2018, the Harvard Business Review published an article titled “Why Companies That Wait to Adopt AI May Never Catch Up”. In the piece, the journalists made the case that while some companies are rapidly adopting AI, others are opting for the "fast follower" approach, waiting for the technology to mature before deploying AI automation.

And if this tactic has worked with other information technologies, it is likely not the case for AI adoption. In fact, developing and implementing AI systems is a lengthy process, and by the time late adopters are ready, early adopters will likely dominate the market with lower costs and superior performance, leaving little room for latecomers to catch up. Now, in 2024, Gartner reports that 70% of organisations are exploring generative AI solutions.

This means that developing an AI strategy is an ever pressing priority for business leaders, who risk letting their organisation fall behind and miss out on a critical competitive advantage. Hasty, hype-driven AI adoption decisions, on the other hand, can equally damage a brand’s reputation and result in significant monetary losses.

For this reason, companies need to select the right partner to help them pick the most impactful AI initiatives to implement, prioritise projects based on their projected ROI, and monitor the overall success of their AI transformation. Here are the top five benefits of choosing an AI Business Intelligence (BI) Platform to speed up, streamline, and maximise the efficiency of your AI adoption journey. 

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5 Benefits of Choosing an AI Business Intelligence Platform

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1. Enhanced, Faster Decision-Making

AI Business Intelligence platforms provide feedback based on data. They collect large datasets and identify the trends and patterns that indicate which AI automation initiative will have the greatest impact on the efficiency of a company’s operations, the largest return on investment, and the most convenient implementation process.

Importantly, these platforms have the capability to make recommendations in a matter of minutes, significantly speeding up the process of performing manual data analysis, conducting time-consuming market research, or using basic analytics tools that lack the capabilities of AI-driven platforms. 

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2. Increased Operational Efficiency 

AI Business Intelligence platforms can leverage data analysis to pinpoint inefficiencies and recommend actionable improvements. These tools can identify bottlenecks, redundancies, and areas where resources are underutilised or could be reallocated in a more cost efficient way. 

For example, a retail chain might use an AI BI platform to identify the most impactful AI initiatives for their business model. By analysing HR data, the platform might discover that significant resources are spent on time-consuming CV screenings, and recommend incorporating an AI CV scanning solution into their recruitment process to speed up the process.

This would not only cut recruitment costs, but also shortened the time-to-hire, improving overall operational efficiency.

Using traditional methods to decide which initiative to prioritise would be less efficient and more time-consuming, as these methods often involve extensive manual analysis and trial and error.

In contrast, an AI BI platform can deliver these assessments in minutes, help the retailer choose whether to start their AI journey by automating part of their recruitment process, and providing precise, actionable recommendations that streamline decision-making and accelerate the implementation.

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3. Risk Management and Predictive Analysis 

When starting their AI journey, organisations need to evaluate and mitigate the associated risks to avoid potential legal and ethical pitfalls. 

For example, in the United States, the Algorithmic Accountability Act of 2023 requires companies to assess the impacts of the AI systems they use and sell, in order to foster transparency about when and how such systems are used. In Europe, the General Data Protection Regulation (GDPR) requires organisations to secure personal data and maintain privacy rights.

Some AI BI platforms have a specific focus on compliance and can be extremely helpful in identifying and reducing the risks connected with AI automation initiatives.

In order to make sure AI systems comply with these legal obligations, organisations should opt for AI BI platforms that are built with compliance and data governance in mind and have the capability to audit and analyse AI systems, perhaps spotting compliance issues before they become serious ones. 

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4. Tool Selection Capabilities

It's anticipated that thousands of new AI solutions will hit the market in 2024 alone. Understandably, this has resulted in some business leaders and digital transformation managers developing “AI fatigue”, as their inbox fills up with information about the latest tools.

The best AI BI platforms on the market will have a database of tried and tested tools that, based on which projects a company needs to implement to maximise the impact of their AI programme, can be recommended to solve a specific goal.

By virtue of being compiled through large scale data analysis, the list of recommended tools will be objective and tailored to the specific requirements, risk tolerance, and objectives of each organisation. 

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5. Increased ROI 

AI BI tools play a key role in helping organisations quantify and maximise the return on investment of their AI automation initiatives. By identifying and prioritising the most impactful AI projects, AI BI platforms leverage company data to estimate the potential financial benefit of each initiative, empowering business leaders to make informed decisions.

In fact, AI BI platforms not only accelerate ROI on AI initiatives by speeding up project implementation, but also eliminate the risk of unnecessary spending on tools that turn out to be a bad fit.

Each year, in the UK alone, wrong software selection costs UK businesses over $124 billion - a staggering number that highlights the importance of making data-driven decisions to avoid costly errors.

An organisation could, for example, evaluate the implementation of various automation initiatives, such as chatbots for customer care, inventory management, marketing personalisation, and HR management. Traditionally, the assessment would require intricate and extensive financial studies, which can be time-consuming and error-prone.

By contrast, an AI BI solution has the capability to analyse the data around the historical performance of each operation and determine which project has the best chance to generate ROI, in what timeframe, and with what level of implementation difficulties. This significantly shortens the time required for decision making and speeds up the ROI of AI automation initiatives. 

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Conclusion 

To avoid the risks of the “fast follower” approach to AI adoption, which include jeopardising business capabilities and losing a critical competitive advantage, organisations can turn to AI Business Intelligence platforms to kickstart their AI automation journey. 

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AIManager360 offers a comprehensive solution designed to accelerate your AI transformation by providing enhanced decision-making, increased operational efficiency, robust risk management, and precise tool selection. It empowers organisations to quickly identify, evaluate, and monitor the ROI of their digital transformation initiatives, enabling them to stay competitive in the digital age. 

Find out more here → LINK

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