How AI is Used in Business: Benefits and Best Examples

More companies are using Artificial Intelligence (AI) to solve everyday problems and improve results across departments. From answering customer queries to helping plan budgets, AI is becoming part of everyday work. Today, 77% of businesses are exploring how to incorporate it into their operations.

Even with this interest, AI can still seem confusing or too technical for many teams. The key is to focus on where it can support real work, rather than thinking of it as an advanced tool that’s difficult to apply.

In this article, we break down how businesses are applying AI in practical ways. You'll see clear examples, understand the value AI is delivering, and learn how your organisation can take the first steps without overcomplicating the process.

TL;DR:

• Artificial intelligence is transforming business operations by improving efficiency, personalisation, and decision-making.
• More businesses are now using AI to optimise areas such as customer support, hiring, and risk management.
• Common AI applications include predictive analytics, chatbots, recommendation systems, and process automation.
• Strategic use of AI helps organisations cut costs, boost productivity, and stay competitive in a fast-changing market.
• Starting with clear use cases and focusing on measurable impact enables scalable and responsible AI adoption.

How Businesses Are Using AI?

Businesses today are leveraging AI to solve problems, automate manual tasks, and enhance the decision-making process. It plays a growing role in operations, from managing supply chains to handling customer requests. 

When used correctly, AI not only speeds up processes but also helps teams focus on work that requires human judgment and creativity. Here are five practical benefits that most organisations see when AI is used effectively:

• Improved Efficiency: AI systems handle routine and repetitive tasks, such as data entry, scheduling, and reporting. This reduces time spent on low-value activities and allows employees to focus on tasks that require problem-solving and creativity.
• Better Decision-Making: AI tools process large volumes of data to identify patterns and trends. This helps organisations make timely, data-backed decisions that are more accurate and aligned with business goals.
• Enhanced Customer Experience: AI enables quicker response times, 24/7 support, and personalised interactions. Chatbots, recommendation engines, and automated follow-ups enhance customer service by making it more responsive and consistent.
• Cost Savings: By automating standard processes, AI helps reduce labour costs, lowers the risk of errors, and streamlines workflows, all of which contribute to improved cost control.
• Competitive Advantage: Organisations that implement AI early can move faster, adapt quickly to market shifts, and raise the standard of service, often outpacing competitors who rely on traditional methods.

These outcomes are not just theoretical. In the next section, we’ll look at real-world examples of how AI is making a difference in everyday business operations.

Also Read: AI for Executives: Top AI Training Programs (2025).

10 Examples of How Artificial Intelligence Is Used in Business

AI is now part of everyday business operations. From automating support to improving decision-making, it offers clear advantages. The examples below show how businesses are using AI to achieve practical outcomes.

1. Customer Support Automation

AI chatbots and virtual agents are now an essential part of modern customer service strategies. They are deployed across websites, messaging apps, and social platforms to manage routine queries. 

These systems draw from pre-fed knowledge bases to resolve common issues instantly and escalate complex matters only when necessary. Their 24/7 availability reduces wait times and ensures customers receive help whenever they need it.

Why it Matters:

• Cuts first-response time from minutes to seconds, improving satisfaction.
• Reduces contact-centre costs by deflecting repetitive queries.
• Frees up support teams to focus on higher-value, more complex interactions that improve customer relationships.

Real‑World Example of AI in Customer Support:

Commonwealth Bank of Australia has integrated AI-powered messaging and live chat to manage around 50,000 daily customer inquiries. Using AI, the bank delivers context-aware responses instantly, significantly reducing wait times. This automation streamlines routine support and detects possible fraud across transactions. 

As a result, CBA efficiently handles high volumes, empowers staff to focus on complex issues, and maintains 24/7 service reliability—all without adding large numbers of new call-center agents.

Actionable Tips:

• Start with simple use cases: Deploy a chatbot that handles common queries, such as return policies, shipping info, or login issues.
• Expand functionality gradually: Once stable, extend its function to include transactional tasks like order tracking, payment confirmation, or account updates.
• Track performance consistently: Regularly monitor key metrics, such as resolution time, escalation rates, and customer satisfaction, to assess effectiveness.
• Use insights to improve: Analyse interaction data to refine responses, reduce friction, and introduce features based on recurring customer needs.

To support leadership in managing these kinds of technology-driven shifts, Corpoladder offers the Executive and Board Leadership in the AI Age course. This five-day programme helps senior leaders navigate change confidently and responsibly. Through practical scenarios, expert-led sessions, and a focus on strategic governance, it ensures your organisation’s leadership is equipped to guide AI adoption that delivers real operational value.

2. Predictive Analytics in Sales

Predictive analytics helps organisations optimise sales efforts by using machine learning to assess which leads are most likely to convert. These models analyse patterns in historical deal data, online behaviour, and purchase intent signals to prioritise outreach. 

This enables sales teams to work more efficiently and increase their chances of closing deals more quickly.

Why it Matters:

• Helps sales teams focus on leads that are more likely to convert.
• Enhances revenue forecasting accuracy by identifying valuable trends.
• Speeds up the sales process with quicker, better-informed follow-ups.

Real‑World Example of Predictive Analytics in Sales

HubSpot, a well-known CRM platform, uses predictive lead scoring to support its global sales operations. By analysing behaviour across email campaigns, content interactions, and deal history, HubSpot’s AI engine scores leads automatically. This allows sales teams to focus on high-priority contacts who are more likely to convert. As a result, HubSpot has improved sales efficiency and shortened the sales cycle for many of its customer-facing teams.

Used effectively, predictive analytics helps businesses grow sales and work more efficiently by focusing efforts where they matter most.

Actionable Tips:

• Add predictive lead scoring: Integrate predictive lead scoring into your CRM so sales reps have a clear list of leads to follow each day.
• Use the same data across sales and marketing: Keep the scoring system reliable by ensuring sales and marketing use the same data.
• Check and update the scoring system regularly: Adapt the system based on new customer behaviour or market shifts.

Used effectively, predictive analytics helps businesses grow sales and work more efficiently by focusing efforts where they matter most.

Also Read: Team Leadership: Essential Skills for High-Performing Teams.

3. Personalised Marketing Campaigns

AI enables organisations to deliver more relevant and timely marketing by analysing customer behaviour, preferences, and past purchases. This enables businesses to segment their audience with greater accuracy and tailor their messaging to individual needs. 

Instead of generic outreach, each customer receives content that matches their interests, delivered at the right time through the most effective channels.

Why it Matters:

• More people read and click on your emails when the content is relevant to them.
• You save money by not showing ads to people who are unlikely to be interested.
• Customers feel more connected to your brand when they get messages that match their interests.

Real‑World Example of Personalised Marketing Campaigns:

Sephora, the global beauty retailer, uses AI to personalise customer experiences across its marketing channels. Through its Beauty Insider programme, Sephora collects data on purchase history, browsing patterns, and preferences. This data informs personalised emails, product recommendations, and in-app messaging tailored to each customer. As a result, Sephora achieves higher engagement, repeat purchases, and loyalty by ensuring each message is timely and relevant.

Actionable Tips:

• Try different subject lines and messages: Use AI-based marketing tools to experiment with different subject lines and messaging strategies.
• Let AI determine the optimal send time: Use AI to choose the best time to send messages for higher engagement.
• Keep your customer lists updated: Ensure customer lists are updated based on their latest behaviour and preferences.

Corpoladder’s Business Analytics for Beginners course is an ideal choice for organisations looking to improve data-driven decision-making across teams. The course introduces essential analytics tools and covers key concepts like data visualisation, basic statistics, and predictive modelling. 

Through hands-on learning and practical case studies, it helps your workforce apply insights directly to real-world tasks, making business outcomes more efficient and measurable.

4. Inventory and Supply-Chain Optimisation

Organisations need stable supply chain operations to run efficiently and meet customer expectations. AI-based demand forecasting supports this by examining past sales data, seasonal patterns, promotions, and external factors such as weather or local events. This helps businesses plan inventory more accurately and avoid sudden shortfalls or overstock.

Why it Matters

• Reduces the risk of stockouts, keeping shelves full and customers satisfied.
• Reduces excess stock, saving storage costs and minimising waste.
• Helps suppliers respond more quickly by sharing accurate demand predictions.

Real‑World Example of AI in Supply Chain:

Walmart uses AI to manage its massive supply chain. It looks at data such as current sales, weather updates, and local events to better predict what products will be needed and when. This helps Walmart keep popular items in stock, avoid over-ordering, and reduce storage costs, while making sure customers can easily find what they want in stores or online.

A well-optimised supply chain supported by AI can improve service levels, reduce operational costs, and strengthen supplier relationships.

Actionable Tips

• Use AI forecasting for products with frequent demand changes: Start with products that see frequent demand changes or seasonal peaks.
• Monitor key performance metrics: Keep track of order accuracy, delivery timelines, and customer feedback.
• Adjust forecasting models: Use early results to refine forecasting models before applying them across all categories.

A well-optimised supply chain supported by AI can improve service levels, reduce operational costs, and strengthen supplier relationships.

Also Read: How to Improve Creative Thinking Skills in 5 Steps.

5. AI-Powered Recruitment

AI is changing how organisations hire people by making the process faster and more accurate. Tools powered by AI can scan CVs, match applicants to job roles, verify qualifications, and even rank candidates based on how well they align with the job requirements. This reduces the need for manual screening and helps ensure fair evaluations from the start.

AI can also reduce hiring costs by up to 30% per hire, making it particularly useful for companies operating on tight budgets. With routine tasks handled by smart systems, hiring teams can spend more time on genuine conversations with candidates and make more informed decisions about who to move forward with.

Why it Matters:

• Reduces hiring time by quickly sorting through large numbers of applications.
• Helps ensure fairer evaluations by using the same standards for every candidate.
• Offers data to help managers choose the most suitable applicants.

Real-World Example of AI-Powered Recruitment:

IKEA uses AI to simplify how it hires customer service and retail staff. The company uses a system to screen video interviews and assess traits like communication and problem-solving. This helps narrow down candidates who align with IKEA’s values. Their recruitment team then focuses on final interviews and ensuring the person fits well with the company culture.

Actionable Tips:

• Explore AI tools for high-volume hiring: Start by using AI tools designed for entry-level or high-volume hiring.
• Refine job descriptions: Use insights from AI tools to make job descriptions clearer and more appealing to qualified candidates.
• Blend AI with structured interviews: Ensure that human judgement remains a key part of the final hiring decisions.

Corpoladder’s Prompt Engineering for ChatGPT course enables organisations to unlock greater value from their AI tools by improving how teams interact with them. Tailored for diverse workplace functions, this programme offers a clear, hands-on approach to designing prompts that produce valid and accurate responses. 

The course equips participants to apply these skills across real-world scenarios such as customer support, internal training, and operational communication. It also helps organisations standardise prompt quality across departments and improve overall efficiency in adopting AI.

6. Financial Forecasting and Risk Assessment

AI-powered models, particularly those based on neural networks, are now being used to enhance financial forecasting and identify potential risks with greater accuracy. These systems can analyse large volumes of financial transactions in real-time, helping organisations proactively manage risks and predict future trends.

Why it Matters:

• Detects fraud at an early stage, lowering the likelihood of financial loss.
• Improves liquidity planning by providing more accurate cash flow forecasts.
• Simplifies compliance by generating consistent and transparent risk reports.
• Speeds up decision-making by offering real-time financial insights.

Real-World Example of AI in Financial Forecasting and Risk Assessment:

American Express uses AI tools to spot unusual activity in customer transactions. This helps catch fraud early and builds trust with customers. The company also uses AI to predict how likely someone is to repay credit and to understand future spending patterns. These tools help the company make better financial decisions and reduce risks.

Actionable Tips:

• Train AI models using at least two years of data: Use a minimum of two years of financial data to ensure reliable forecasting.
• Compare predicted and actual results: Regularly compare predictions with actual outcomes to improve model accuracy.
• Document AI-generated financial outputs: Keep detailed records of AI-generated outputs for transparency and compliance.

These capabilities enhance financial planning and support overall business resilience by identifying and addressing risks early. Incorporating AI into financial strategy gives organisations a competitive advantage in managing uncertainty.

Also Read: Why Interpersonal Skills Are Important & How to Strengthen Them.

7. Process Automation in Back-Office Operations

AI-powered Robotic Process Automation (RPA) simplifies repetitive tasks in HR, finance, and operations. Activities such as invoice matching, payroll checks, and monthly reporting are now completed faster and more accurately with minimal manual input. This helps teams redirect their efforts toward tasks that require decision-making and insight.

Why it Matters:

• Reduces the likelihood of manual entry errors by automating data-intensive tasks, resulting in more consistent and reliable reporting.
• Accelerates repetitive processes such as payroll, invoicing, and compliance checks, allowing quicker turnaround times.
• Enables staff to focus on areas requiring human judgement, such as strategic planning, forecasting, and stakeholder communication.

Real-World Example of AI in Process Automation:

Coca-Cola Bottling Company United uses RPA to handle repetitive administrative work in its finance department. The company automated the invoice matching process, which used to take hours of manual effort. By using software bots, they now complete this task more quickly and with fewer errors. This change has allowed finance staff to spend more time on analysis and planning instead of routine paperwork.

Actionable Tips:

• Automate repetitive HR or finance tasks: Start by automating a regular HR or finance task, like payroll checks or invoice matching.
• Write down each step: Document every step of the task to ensure accurate automation of the process.
• Use simple RPA tools to start: Begin with simple RPA tools and test them on small tasks before expanding to more complex workflows.
• Evaluate before and after automation: Compare the time spent and error rates before and after automation to measure improvements.

8. Product Recommendations in E-commerce

AI-powered recommendation engines analyse browsing behaviour, past purchases, and preferences of similar users to display relevant products in real-time. This data-driven approach enables e-commerce businesses to provide a more customised shopping experience and inform buying decisions with greater accuracy.

In fact, 35% of Amazon’s annual sales are driven by its AI-powered recommendation engine, highlighting its critical role in generating revenue.

Why it Matters:

• Drives higher average order value by identifying and promoting complementary or premium products at key decision points.
• Simplifies product discovery by showing relevant suggestions, saving customers time and effort.
• Boosts customer retention by delivering personalised experiences that encourage repeat visits.

Real-World Example of AI in Product Recommendation:

Netflix uses AI to suggest shows and movies that each viewer is likely to enjoy. It looks at what someone has watched before, what they searched for, and what other viewers with similar tastes prefer. For example, if someone watches a lot of crime dramas, Netflix will recommend more content from that category. This helps viewers find content quickly and keeps them watching longer, which is why it's become an important part of how Netflix keeps users engaged.

Actionable Tips:

• Place recommendation widgets strategically: Position recommendation widgets on high-impact areas, such as product details, checkout, and homepage banners.
• Start with basic models: Begin with AI algorithms based on past purchase data and gradually scale to more complex recommendation systems.
• Use A/B testing: Run A/B tests to compare different recommendation strategies and enhance conversion rates based on data insights.

To maximise impact, ensure your recommendation engine evolves with your users. By continuously refining algorithms through real-time data and behavioural trends, organisations can strengthen customer satisfaction and drive long-term revenue growth.

Also Read: Team Leadership: Essential Skills for High-Performing Teams.

9. AI-Driven Cybersecurity Monitoring

AI tools monitor user activity and network behaviour in real-time. They help detect unusual patterns that older systems might miss, allowing faster response to security issues. These systems learn from past actions, making it easier to spot problems early and reduce risks. This supports organisations in strengthening their cybersecurity and maintaining stable operations.

Why it Matters:

• Helps detect breaches more rapidly, reducing the scope of the damage.
• Minimises false positives, allowing security teams to focus on real risks.
• Continuously learns from evolving attack strategies to stay current.

Real-World Example of Cybersecurity Monitoring:

IBM uses AI-powered cybersecurity tools through its QRadar platform to monitor and respond to threats across large enterprise networks. The system analyses vast volumes of data to detect unusual activity in real-time. For example, when suspicious login attempts occur across different regions, QRadar flags the activity, prioritises the risk, and alerts security teams. This has enabled IBM and its clients to act quickly, contain potential breaches, and maintain business continuity with minimal disruption.

Actionable Tips:

• Integrate AI-driven monitoring into existing security systems: Add AI-powered cybersecurity tools to your existing SIEM systems to enhance threat detection.
• Regularly update and train the models: Continuously update and train AI models with the latest threat data to maintain detection accuracy.
• Conduct internal drills: Run simulated attack drills to test and fine-tune AI responses, improving the system’s effectiveness over time.

Strengthening cybersecurity isn't just a technical challenge, it requires confident leadership that can make informed decisions and guide the organisation through complexity. Corpoladder’s Leadership and Strategy for Senior Executives course addresses this need by helping senior managers build the judgement and strategic thinking needed to manage high-stakes scenarios, including cybersecurity. With a focus on practical tools, real-world simulations, and peer collaboration, the programme equips leaders to support business continuity while driving long-term success.

10. Personalised Employee Learning and Development

AI-powered learning platforms can identify where employees need improvement, recommend suitable training paths, and adapt content based on individual performance and learning styles. This makes learning more relevant, timely, and aligned with both employee growth and organisational priorities.

Why it Matters:

• Speeds up skill development by guiding employees towards high-impact learning areas.
• Enhances learner engagement by delivering content tailored to their current level and job role.
• Gives managers precise data to make informed decisions about future training and leadership planning.

Real-World Example of AI in Personalised Learning and Development:

Unilever introduced an AI-based learning platform to help employees grow in their roles. The tool, called 'Degreed,' suggested courses and materials based on each employee’s job and career goals. It also tracked how people learn and adjusted the content to suit their preferences. This helped Unilever improve employee engagement, support skill-building, and make sure their teams had the right knowledge to meet business needs.

Actionable Tips:

• Start with a tech-savvy team: Implement a small-scale pilot for personalised learning systems with a team that is comfortable with technology, such as IT or marketing.
• Track skill gains: Monitor skill development over a set period to evaluate the effectiveness of the system.
• Gather participant feedback: Collect feedback from participants to refine the learning system before rolling it out to other teams or departments.

These benefits are clear, but successful implementation often comes with obstacles. Let’s look at the key challenges organisations typically encounter when introducing AI into their operations.

Challenges Organisations Face When Adopting AI

Organisations often face roadblocks when trying to bring AI into their business. These challenges can slow down progress, reduce the return on investment, and in some cases, cause projects to stall entirely. Understanding these issues early helps in planning better and avoiding common mistakes.

• No Clear Use Case or Goal: Some organisations start using AI without knowing exactly what problem they are solving. This creates confusion and wastes time and resources. It’s important to first identify where AI can add real value, like improving customer service, streamlining operations, or forecasting demand.
• Lack of Skills in the Workforce: Employees often don’t have the training to use AI tools properly. This includes frontline teams, managers, and even senior staff. Without the right mix of skills, from basic data awareness to using AI tools, most employees can’t benefit from the technology. Training across levels is essential.
• Worry About Job Loss: Employees sometimes worry that AI will take away their jobs. This can cause hesitation, low morale, or resistance. Clear communication about how AI supports people, not replaces them, and offering learning opportunities helps reduce fear.
• Problems with Data: AI needs reliable and consistent data to work well. Many organisations face issues like scattered data, poor quality, or old systems. This limits how useful AI can be. Investing in proper data systems and cleaning up existing data makes AI projects more effective.
• Lack of Support from Leadership: AI projects often need strong support from leadership. If decision-makers don’t understand the value of AI or see it as too complex, they might not prioritise it. Making a clear link between AI and business goals helps build executive backing.

To overcome these hurdles, organisations must align their goals, invest in skill development, and build strong leadership support. When these areas are addressed together, AI adoption becomes smoother and more impactful.

How Does Corpoladder Strengthen AI Readiness and Support Modern Organisations?

Preparing your organisation for AI isn’t just about technical upskilling—it’s about developing a workforce that can adapt, apply, and lead in a rapidly evolving market. Corpoladder supports this transformation by delivering practical, scenario-based training that connects AI literacy with real business needs.

Our training programmes focus on three core areas—Artificial Intelligence, Leadership Development, and ESG (Environmental, Social, and Governance)—and are tailored to suit different industries, roles, and stages of expertise.

Here’s how Corpoladder supports your organisation’s AI readiness goals:

• Role-Specific Training: Programmes are tailored for diverse roles, from frontline staff to executive leadership. This ensures learning remains focused, practical, and aligned with daily responsibilities.
• Flexible Delivery Formats: Training is available through in-person workshops, virtual classrooms, and self-paced modules, offering flexibility based on organisational needs and learning preferences.
• Real-World Practice: Each course includes business scenarios and case studies that simulate real challenges, helping learners apply AI tools in relevant workplace contexts.
• Expert-Curated Curriculum: Our content is shaped by AI specialists, experienced educators, and corporate leaders, combining academic rigour with business practicality.
• Ongoing support: Post-training resources, assessments, and learning pathways help teams reinforce skills over time.

From day-to-day operations to strategic planning, Corpoladder equips your workforce to integrate AI thoughtfully and contribute to sustainable, measurable outcomes.

Conclusion

Artificial intelligence is changing how organisations operate across customer service, decision-making, and internal efficiency. The most successful examples are not large-scale overhauls but specific, practical uses that deliver measurable results. When teams focus on one priority, test its impact, and refine the approach, AI becomes less abstract and more actionable.

At Corpoladder, we support this transition through targeted AI-readiness programmes. From executive leadership to frontline teams, our training equips them with the skills necessary to adopt AI responsibly and effectively.

Get in touch with us to explore how Corpoladder can help your organisation move from experimentation to sustained value with AI.

FAQs

1. What types of businesses benefit most from AI?
Organisations that manage high volumes of data, repetitive processes, or frequent customer interactions see the most benefit from AI adoption. This includes sectors such as retail, manufacturing, healthcare, and financial services. However, AI is not limited to large enterprises—small and mid-sized businesses with specific, well-defined use cases can also experience tangible improvements in efficiency and decision-making.

2. How expensive is it to implement AI tools?
Costs vary based on the scale and complexity of implementation. Many organisations begin with cloud-based, off-the-shelf tools that offer affordable and scalable entry points. For businesses seeking to develop custom solutions or integrate AI with their existing infrastructure, additional investment may be required in areas such as data infrastructure, skilled personnel, and ongoing system management.

3. Can small businesses use AI effectively?
Yes, small businesses can achieve strong results using AI, especially when starting with targeted applications. Tools such as AI-powered chatbots, virtual assistants, or automated reporting software are cost-effective, user-friendly, and require minimal technical knowledge. These solutions can improve productivity, customer service, and data analysis without requiring a whole tech team.

4. What skills do teams need to work with AI?
Successful AI adoption involves a mix of technical and soft skills. While roles involving model development or analytics need statistical and data handling expertise, most team members should be trained in data literacy, digital tools, and business-focused problem-solving. Strong communication and an openness to change are crucial for integrating AI into daily work routines.

5. How is AI different from traditional automation?
Traditional automation follows preset rules and handles tasks that remain constant over time. AI, on the other hand, continuously learns from data and adapts its responses. This allows it to manage complex tasks such as making predictions, offering personalised experiences, or interpreting language, functions that standard automation cannot perform as effectively.