AI driving business growth in 2024

AI driving growth in 2024

2024 has brought a sharp rise in AI applications across key business areas. Companies are using models to automate customer interactions, target campaigns with precision, make data-driven decisions and optimise the supply chain. The result is higher-quality experiences, faster decisions and tangible revenue growth.

Customer service: chatbots and assistants 24/7

AI has taken over the lion’s share of customer interactions. According to IBM, as many as 85% of contacts are now handled without human involvement. Chatbots based on solutions such as Google's Dialogflow provide instant support 24/7, efficiently resolving the most common issues and escalating more complex cases to consultants.

- A 32.7% increase in virtual assistant adoption confirms their growing role in customer service. - AI personalises responses based on customer history and context, reducing handling time. - Models predict user intent, proactively suggesting solutions. - Business impact: organisations using AI in customer service recorded 33% faster growth than competitors (Forrester Research).

The result is higher satisfaction and retention – a solid fuel source for business growth.

Marketing: predictive analytics that increase conversion

AI in marketing makes it possible to design campaigns based on real behavioural patterns. Already 68% of companies use predictive analytics to identify and interpret purchasing trends, which leads to better alignment between messaging and audience needs.

- Companies integrating predictive analytics recorded a 35% increase in conversion (Harvard Business Review). - A customer journey designed around touchpoints detected by AI creates consistent, personalised experiences. - Organisations implementing AI in campaigns report a 37.3% increase in customer retention and 25.9% overall business growth (PwC). - According to McKinsey Global Institute, the use of AI in marketing is associated with projected business growth of around 30%.

- Practical applications: - Predictive segmentation and lookalike modelling. - Real-time content and offer recommendations. - Dynamic pricing and media budget allocation. - Optimisation of media mix and impact attribution.

Data-driven decisions: AI/ML as an efficiency engine

In 2024, as many as 86% of companies are incorporating AI and Machine Learning into their strategy to make better decisions faster. The ability of models to process vast datasets and predict patterns is transforming analytics from descriptive to predictive and prescriptive.

- Organisations with well-integrated AI infrastructure recorded a 45% increase in revenue (IBM). - The use of AI for data analysis improves operational efficiency by 37%, and with the addition of ML – up to 50% (Gartner). - Marketing strategies supported by AI increase customer loyalty metrics by 30% (Bain & Company).

- Where you will gain first: - Forecasting demand and revenue in advance. - Real-time optimisation of pricing, inventory and margins. - Early detection of anomalies and operational risks. - Automation of insights and recommendations for teams.

Supply chain: logistics optimised by AI

AI and ML are changing operations planning from demand forecasting to delivery execution. The same mechanisms that improve analytical efficiency also translate into faster logistics decisions and lower costs.

- Key applications: - Demand forecasting at SKU and regional level. - Production planning and capacity allocation based on demand variability. - Route and time-window optimisation in transport. - Inventory management with dynamic replenishment thresholds. - Delay prediction, anomaly detection and loss reduction.

Companies using AI for data analysis report efficiency gains of 37–50% (Gartner, with ML support). These benefits materialise in the supply chain as shorter lead times, fewer stockouts and more predictable service.

What’s next: practical steps for 2024

- Map your processes and select 2–3 areas with the greatest impact on revenue or cost. - Define success metrics (e.g. CSAT, conversion, lead time, unit cost). - Launch fast PoCs: a chatbot in Dialogflow, a predictive analytics pilot in campaigns. - Ensure data quality and governance; prepare integrations and MLOps. - Iterate based on results and scale where ROI is highest.

AI is no longer an experiment – it is a competitive advantage. Companies that implement it deliberately and measurably grow faster, operate more efficiently and deliver better customer experiences.