Whilst your organisation may be building momentum with dashboards, exception reporting, and real-time insights, one question remains – how do you move beyond reporting and start using analytics to predict, forecast, and automate?
A quick answer would be to look at areas where your organisation relies heavily on gut feel, where manual processes are slowing you down and where foresight will help the business act sooner. Not every business area is ready for AI, and not every team needs it immediately. The key is prioritising where AI and automation can make the biggest difference.
Start where there is a business impact
Focus on areas where delays, inefficiencies, or missed opportunities have real consequences.
Finance – AI can enhance financial decision-making by predicting future trends and identifying risks early. For example:
- Cash forecasting – AI can predict cash flow trends based on historical data, expected invoices, and payment schedules, helping to avoid cash shortfalls or plan for surpluses.
- Spend analysis – AI can analyse company spend patterns, recommending areas for cost reduction or helping optimise vendor contracts.
Sales and Marketing – AI can drastically improve customer targeting and retention strategies, moving beyond traditional methods.
- Lead scoring – AI can analyse historical data to predict which leads are most likely to convert, allowing sales teams to prioritise efforts on high-value opportunities.
- Personalised marketing – AI can enhance marketing efforts by segmenting audiences based on behaviour and preferences, allowing for personalised campaigns that increase engagement and sales.
Operations – AI can optimise processes and reduce costs by predicting potential disruptions and bottlenecks.
- Inventory forecasting – AI can predict stock shortages or overages by analysing past sales data, seasonal trends, and market conditions. This enables better inventory planning, reducing waste or stockouts.
- Predictive maintenance – AI can analyse equipment performance and predict when machines will need maintenance, helping to prevent costly downtime and improving operational efficiency.
Supply Chain optimisation – AI can help identify potential delays in the Supply Chain and suggest alternative suppliers or routes, reducing risks to production timelines.
HR – AI is becoming a critical tool in managing and optimising human resources.
- Skills forecasting – AI can assess workforce skill gaps and help HR teams identify future training needs or recruitment strategies, ensuring the organisation has the right talent to meet future challenges.
- Diversity and Inclusion – AI can help identify areas where diversity may be lacking or where unconscious bias might be affecting hiring or promotion decisions.
Focus on data quality and governance
AI readiness isn’t just about tools – it is about trust. Before applying AI, make sure the underlying data is clean, structured, and trustworthy. You can’t forecast accurately or automate decisions with poor-quality data.
Build trust by:
- Ensuring key fields are populated and standardised.
- Documenting data sources and logic used.
- Clarifying ownership and definitions (e.g. what exactly is a “repeat customer”?).
Take stock of where your biggest data pain points are. That’s likely where your AI journey should begin.
Look for quick wins
AI doesn’t have to be a major initiative to get started. Instead, choose a narrow problem, use your current analytics platform where possible and show how basic predictive insights can guide decisions. Once teams see AI in action – helping them solve a problem – they’re more likely to support further innovation.
Learn from success stories
Many companies begin their AI journey in small, focused areas. For example:
- A logistics team that used historical delays and weather patterns to predict late deliveries.
- A sales team that ranked leads by likelihood to convert based on past behaviour.
- An HR department that flagged teams at risk of burnout using absence and overtime data.
The common thread? Real business problems solved through real data and real insights.
