Top AI Use Cases Transforming Business in 2025

In customer service, AI agents and chatbots can provide instant, around-the-clock support for customer queries, helping businesses deliver better experiences while saving valuable time. Agentic AI can also help build cloud computing knowledge bases for customer service teams by summarising case resolutions and assisting reps in solving issues faster.

For example, Pacific Smiles Group, a fast-growing Australian dentist service organisation, needed a solution to scale quickly without compromising the patient experience.

To achieve this, the group deployed Agentforce, Salesforce’s complete AI solution, to respond to patient inquiries 24/7 on the company website. The agent can provide information on treatment options and help book appointments, providing patients with faster access to vital dental care.

Again, start small with your agentic AI use cases. We suggest automating your top 10 customer service questions first and then expanding coverage over time.

Related: See how Momentum Energy harnessed Agentforce to handle their service team’s top three enquiries, improving customer service while freeing up their team members’ time .

AI assistants can now orchestrate entire workflows from beginning to end, whether that’s handling customer returns, streamlining internal approvals, or managing bookings.

A great example of this concept in action comes from TripADeal, which uses Agentforce to automate booking and service workflows and deliver always-on support at scale, while freeing up time for core tasks.

To get started here, choose one high-volume, rule-based process that you can automate from instance to resolution, such as:

• Customer returns, such as approvals and producing shipping labels
• Booking workflows - think reservations, confirmations, and follow-up emails
• Internal approvals that require multi-step sign-offs

You can then layer on more complex steps as you test and refine each approach. Mulesoft can help with this by letting you connect data to actions and automate multi-step workflows with low-code tools.

AI has turned forecasting from a static, backward-looking process into a dynamic, real-time decision engine. It can process enormous amounts of data and detect patterns that humans can’t see. And, of course, this frees up time for the actual experts to turn insights into action.

Again, the key here is a gradual approach. Automate one forecast (such as weekly sales) and then compare the AI’s predictions with the actual results. You can then use this information to fine-tune and train the model over time.

CRM Analytics and Tableau can bring AI predictions straight into your dashboards, helping you visualise data in a clean, easy-to-read format and turn insights into action points.

In the manufacturing industry, forward-thinking businesses are already embedding AI into their factories to optimise supply chains and prevent and fix equipment failures proactively.

For instance, by embedding AI into IoT sensors, you could monitor equipment performance in real time and see when assets might be reaching the end of their life. This would let you schedule maintenance before a breakdown occurs, reducing downtime.

Qantas is a great example of this. The airline uses AI technology to analyse flight data and engine performance to confirm that an aircraft is fit to fly. This enables their employees to make critical decisions about risk management more confidently. It also helps the company maintain a proud safety record and minimises flight cancellations.

Field Service and Manufacturing Cloud make this process easier by connecting asset data and predictive insights in one place, helping you turn asset intelligence into a strategic advantage.

In the finance sector, AI tools are particularly useful for managing cybersecurity risks and helping businesses detect suspicious, potentially fraudulent activity ahead of time.

Naturally, these algorithms benefit the end customer. For example, Westpac, one of Australia’s largest banks, recently launched SaferPay, an AI-powered solution designed to enhance financial security. It predicts which payments may be fraudulent and prompts additional verification questions to alert customers to potential scams.

Begin by combining rule-based filters with ML detection. This will help the model quickly flag unusual activity while limiting the possibility of false alarms. You can then review the AI model regularly to check for false positives and keep refining the algorithm.

Expanding on the previous point, AI’s ability to analyse vast amounts of data and detect cybersecurity threats proactively can be a huge advantage. For instance, machine learning can:

• Detect anomalies in real-time
• Prevent phishing attacks
• Power facial recognition systems for authentication
• Run advanced antivirus programs to block malware

All of this gives businesses the tools to identify vulnerabilities and, in the worst-case scenario, significantly reduce the impact of a breach.

A good place to start here is by monitoring email and endpoint activity, as this is the simplest entry point for spotting malicious behaviour early. You can then expand gradually to full AI system coverage.

AI is also transforming how software is built and deployed. With AI-powered code assistants, devs can generate code snippets, write full-scale tests, and catch bugs in less time. This means tasks that once took a team of developers can now be completed by a couple of experts and an LLM, redefining developer productivity.

Naturally, these AIOps also benefit customers by helping IT professionals spot issues before they reach the end user. This can help organisations ship more polished products, boosting ROI.

Start by introducing AI into your code review workflows, as this is a fairly low-stakes task compared with letting AI write code from scratch. Tools like Einstein 1 Studio can help with this by automating routine dev tasks, giving you more time to focus on strategy and innovation.

On the sales automation side of things, AI can predict which leads or deals are most likely to convert, prioritise them automatically, and suggest the best action for reps to take. This guides sales teams to focus on the opportunities that will actually produce a measurable ROI.

In our latest State of Sales Report , we found that 83% of sales teams using AI saw revenue growth in the last year, compared with just 66% of teams not using AI. How do you get started? A good approach is to identify your top-performing lead segments and let AI prioritise them for outreach. You can then measure the win-rate lift and adjust your strategy accordingly.

Sales Cloud can help you score leads and automate follow-ups while providing insights that increase deal closure rates, all powered by groundbreaking AI. Try it for free.

Another broad advantage is that AI can unify customer data from fragmented sources like your CRM , websites, and commerce platforms into a single, governed profile. This effectively gives businesses a 360-degree view of every customer, supporting better segmentation and real-time personalisation.

Start by unifying all of your disparate information with a platform like Data Cloud. This will give you a single source of truth for your customer interactions. You can then pilot a small use case, like segmenting your audience and sending out a targeted email or personalised product recommendation.

Here’s an interesting stat: 80% of Netflix viewing and 70% of YouTube watch time come from suggested content, all driven by AI.

AI recommendation engines work by predicting the products or content that a user is most likely to engage with based on their habits and history. They then surface suggestions in real-time, guiding users to what they most likely want to see next.

For organisations, this increases engagement and boosts conversions. It also has a direct impact on revenue, encouraging more time on platforms for services and higher average order values for commerce businesses.

Again, start with one product category, test it out, and measure the lift in conversions. Commerce Cloud can support you here by helping you implement built-in recommendations and personalise the shopping experience across all of your channels.

In the HR industry, AI can help teams screen resumes and draft job ads, freeing up time for professionals to focus on high-value work like engaging with candidates directly. It can also personalise training paths for staff, leading to faster skill development and better retention.

Resume screening is the best launchpad here because it’s incredibly easy to automate. Simply define your criteria and let AI rank and filter applications based on those requirements. You’ll then get a shortlist that you can screen manually to land on the perfect candidate.

AI can speed up legal and compliance workflows by reviewing contracts, extracting key clauses for review, and flagging any potential risks before they materialise.

Aside from reducing manual work and speeding up approvals, this can actually improve accuracy, too, as it minimises the possibility of human error and ensures key clauses aren’t overlooked.

Begin with high-volume, low-risk agreements first, like NDAs or vendor MSAs, and then move on to more complex contracts as you gain confidence. If you’d like to get started, Revenue Cloud can support you with every aspect of contract lifecycle management, from drafting agreements to reviewing obligations, all in one place.

Let’s finish up with what is perhaps AI’s biggest draw – its ability to analyse vast amounts of information to identify patterns and uncover opportunities.

AI can process millions of data points in seconds to uncover insights and then transform them into visual dashboards that clearly highlight trends that might otherwise be missed. This gives businesses the information they need to make faster, smarter decisions.

Where to begin? Try building a simple dashboard that tracks a few key metrics like sales performance or customer engagement. You can then layer in AI-augmented insights and action buttons so that your team can act immediately when information becomes available.

CRM Analytics and Tableau can help with this by bringing predictive insights directly within workflows, making it easy to monitor performance and respond quickly.

Lastly, let’s round things off with some of the emerging applications of AI. These use cases haven’t yet reached full maturity, but they have the potential to be hugely transformative in the coming years.

• Space exploration: NASA teams are beginning to use AI to navigate spacecraft, plan missions, and even detect objects like asteroids before they pose a risk.
• Climate engineering: AI could play a pivotal role in the fight against climate change. Startups like CarbonPlan are using AI to analyse our climate, laying the groundwork for AI-managed geoengineering projects in the future.
• Language translation: AI has already made significant strides in breaking down language barriers. Tools like Meta’s No Language Left Behind use advanced artificial neural networks for real-time translation.
• Drug discovery: In healthcare, one of the most exciting AI applications is its ability to expedite drug discovery. AI tools can analyse biological research, identify patterns, and predict drug efficacy faster than any human.

These future developments will give organisations and researchers the tools to tackle real-world challenges that we previously thought unsolvable. And that’s just the tip of the iceberg.