Recently, there has been significant interest in Generative Artificial Intelligence (GenAI) tools such as ChatGPT and Gemini. While many of these applications are aimed at consumers, businesses are increasingly exploring how generative AI for data analytics can support decision-making, improve operational efficiency, and uncover new insights from complex datasets.
James Gornall, Cloud Architect Lead, CTS, explains the vital difference between headline-grabbing consumer tools and proven enterprise-grade GenAI solutions.
Understanding AI
Given the recent hype, you’d be forgiven for thinking that AI is a new capability, but in actual fact, businesses have been using some form of AI for years – even if they don’t quite realise it.
Predictive Analytics and Forecasting
One of the many applications of AI in business today is in predictive analytics. By analysing datasets to identify patterns and predict future outcomes, businesses can forecast sales, optimise inventory, detect fraud and better allocate resources.
Generative AI can further enhance this process by helping users interrogate datasets using natural language, summarise findings, and identify trends more quickly than traditional reporting methods.
Improving Decision-Making
Using data visualisation tools to make complex data simpler to understand and more accessible, decision-makers can easily spot trends, correlations and outliers, leading them to make better-informed data-driven decisions faster.
Enhancing Customer Service
Another common application of AI is enhancing customer service through AI-powered chatbots and virtual assistants that meet customers’ digital expectations by providing instant support.
What’s New About Generative AI?
What distinguishes GenAI from traditional AI is its ability to generate entirely new content based on previously learned information. By drawing on vast amounts of data, GenAI can produce text, imagery, code, and other outputs at a scale that was previously impossible. This presents significant opportunities for organisations looking to accelerate content creation, experimentation, and innovation.
The technology can use data on customer behaviour to deliver quality personalised shopping experiences. For example, retailers can provide product catalogues tailored to an individual’s preferences, creating a more relevant shopping experience. GenAI can also generate recommendations based on previous purchases and support more natural customer interactions, helping to improve engagement and satisfaction.
Beyond content generation, GenAI can support data analytics teams by summarising large datasets, generating reports, highlighting anomalies, and presenting insights in formats that are easier for non-technical stakeholders to understand.
Furthermore, GenAI can improve operational efficiency by automating reporting, analysing customer feedback, summarising business performance data, and supporting inventory planning. This allows employees to spend less time gathering information and more time acting on insights.
The Risks of Using Generative AI for Data Analytics
The latest generation of consumer GenAI tools has transformed AI awareness at every level of business and society. In the process, they have also done a pretty good job of demonstrating the problems that quickly arise when these tools are misused.
IP Leakage
One of the most widely discussed risks is the accidental leakage of intellectual property (IP). Employees can input confidential code, business data, or proprietary information into consumer GenAI tools without realising that the information could be retained or used to improve future models.
Similarly, several high-profile cases have demonstrated the risks of relying on AI-generated outputs without proper verification, including legal professionals citing fictitious research generated by AI tools.
Employee Misuse
While this latest iteration of consumer GenAI tools is raising awareness of this technology’s capabilities, there is a lack of education on how it is best used. Companies need to consider how employees may be using GenAI, which could jeopardise corporate data resources and reputation.
Data Governance
With GenAI set to accelerate business transformation, AI and analytics are rightly dominating corporate debate, but as companies adopt GenAI to work alongside employees, it is imperative that they assess the risks and rewards of cloud-based AI technologies as quickly as possible.
Trusted Data Resources
Alongside governance and risk management, organisations must also ensure that the data and platforms underpinning GenAI initiatives are reliable, secure, and fit for purpose. Trusted data resources include:
Data Quality and Accuracy
One concern for businesses is the quality and accuracy of the data provided by GenAI tools. This is why it is so important to distinguish between the headline-grabbing consumer tools and enterprise-grade alternatives that have been in place for several years.
Business-specific language is key, especially in jargon-heavy markets, so it is essential that the GenAI tool being used is trained on industry-specific language models.
Security and Enterprise Controls
Security is also vital. Enterprise-grade GenAI platforms allow organisations to establish secure AI environments in which information is stored within a virtual safety perimeter. This environment can be tailored with a business’s documentation, knowledge bases and inventories, so the AI can deliver value specific to that organisation.
While these tools are hugely intuitive, it is also important that people understand how to use them effectively.
Using GenAI Effectively
Providing structured prompts and being specific in the way questions are asked is one thing, but users need to remember to think critically rather than simply accept the results at face value. A sceptical viewpoint is a prerequisite – at least initially. The quality of GenAI results will improve over time as the technology evolves and people learn how to feed valid data in, so they get valid data out. However, for the time being, people need to take the results with a pinch of salt.
Ethical and Responsible AI
Avoiding bias is a core component of any Environmental, Social and Governance (ESG) policy. Unfortunately, there is an inherent bias that exists in AI algorithms, so companies need to be careful, especially when using consumer-level GenAI tools.
For example, financial companies need to avoid algorithms that produce biased outcomes against customers seeking access to certain products, or that assign different interest rates based on discriminatory data.
Similarly, medical organisations need to ensure universal access to care across all demographics, especially when different ethnic groups face varying risk factors for certain diseases.
Conclusion
Generative AI for data analytics is helping organisations democratise access to insights, improve decision-making, and increase operational efficiency. From forecasting demand and analysing customer behaviour to automating reporting and supporting strategic planning, the technology offers significant business value.
However, successful adoption depends on strong governance, trusted data sources, and the use of secure, enterprise-grade AI platforms. Businesses that balance innovation with security, accuracy, and accountability will be best positioned to realise the full potential of GenAI.
