More organizations integrate AI than ever before: 78% of businesses use AI in at least one function, up from 50% just five years ago. The number of business functions impacted is also steadily rising, with 63% of organizations using AI in two or more functions and 45% in three or more functions.
But even as businesses are going all in on AI, only 25% of business leaders surveyed by BCG reported deriving “significant value” from their AI projects. Why? Reasons may vary, but focusing on too many use cases or overlooking the most value-adding ones is a common culprit.
Here’s how to harness the power of AI for your business, from the most valuable use cases to best practices for AI implementation.
5 Top Value-Adding AI Use Cases for Businesses in 2025
How you can use AI for business depends heavily on the industry and domain you’re operating in. Here’s our shortlist of the highest-ROI use cases across five domains, from logistics to FinTech.
Unexpected downtime from truck breakdowns can cost you as much as $5,000 per year and per vehicle. The fleet management software equipped with an AI-powered predictive analytics algorithm, however, can help you avoid it.
Such systems use AI to analyze sensor data in real time to assess the condition and performance of vehicle parts and determine when preventive maintenance is necessary. The result? You can save up to $2,500 per truck in annual expenses by integrating AI into your supply chain management and avoiding downtime, all while keeping on-time delivery rates high.
Your CRM systems, marketing tools, and website analytics software collect tons of customer data. Advanced AI data analytics can help you make sense of it by identifying previously hidden patterns, trends, and insights, and increase your conversion rates and marketing ROI as a result.
Analytical AI systems can improve the accuracy of segmentation and targeting for ads, marketing campaigns, and product recommendations. Generative AI, in turn, can automate email and marketing content personalization. You can also use AI tools with natural language processing capabilities to make sense of unstructured data, such as social media posts.
In healthcare, artificial intelligence and machine learning algorithms can help diagnose patients, identify health risks, predict adverse events in ICU units, and suggest personalized treatment plans.
However, smaller-scale healthcare providers don’t have to jump into such expensive applications of artificial intelligence to transform their business processes and improve operational efficiency. Instead, they can leverage AI to send out personalized follow-ups, appointment reminders, and wellness recommendations.
Our solution for Persuasion Systems, for example, used customized ML models and predictive analytics to enable clinics to provide highly personalized solutions to their patients. As a result, patient care and outcomes improved by an average of 11.89%.
Customer service is the most common use of AI across industries. It’s no wonder why: AI chatbots can answer queries 24/7, and with generative AI, they can do so in a more human-like manner.
In travel and hospitality, in particular, AI chatbots help users book their stays and tickets, answer common questions, and even provide personalized recommendations. While they handle run-of-the-mill queries, the staff can focus on more complex tasks that require a human touch.
While AI is notably used for fraud detection and prevention and risk assessment in underwriting, banking personalization is the avenue you should pay attention to, as well. Thanks to vast amounts of user behavior data, including transaction history and preferences, AI models can deliver personalized financial management and product recommendations.
For example, Wells Fargo’s LiveSync, an AI-powered assistant, helps customers set and work towards financial goals. Bank of America’s Erica, in turn, helps mobile users manage their accounts, monitor their recurring charges, redeem rewards, and make investments.
6 Best Practices for Securing the AI Integration ROI
To transform your business with AI solutions and gain the ROI you expect from them, you’ll need to:
- Assess current infrastructure and data readiness. Evaluate your cybersecurity measures, data availability and quality, infrastructure, available talent, and integration opportunities and requirements.
- Define clear goals and KPIs. Clear objectives and metrics make it easier to define the right solution, track its effectiveness, and adjust it as needed.
- Plan and select appropriate AI models. Choose the AI model (e.g., regression, classification, etc.) and training method best-suited for your use case and business goals.
- Pilot the AI solution and gather feedback. Test your solution at a small scale, evaluate its performance in real-world conditions, and improve it based on end users’ feedback.
- Train staff and update workflows. Avoid resistance to change and enable your staff to make the most out of the new AI capabilities by upskilling them and updating your business processes.
- Monitor performance and iterate. Set up automatic performance monitoring, regularly test the AI system, and improve and fine-tune it in iterations.
6 Common AI Implementation Challenges
We mentioned that only a quarter of businesses capture significant value from AI initiatives. Here are the six challenges you need to overcome to join them:
- Data quality issues. Without high-quality, clean data, your AI model will return inaccurate results. So, select the appropriate internal and external datasets and ensure data consistency, completeness, and accuracy.
- Resistance to change. Your established workflows and business practices will need some revising. Work closely with end users to counter potential resistance to change.
- Integration complexity. New AI capabilities should seamlessly integrate with your existing software stack, but legacy integrations can be notoriously challenging.
- Scalability concerns. Your AI model will need to handle vast amounts of data and varying workloads. Ensure your infrastructure can handle spikes in computing resource needs.
- Security and compliance requirements. Ensure your AI applications comply with data privacy (GDPR, CCPA), AI-related (the AI Act), and industry-specific (HIPAA) regulations. Implement data anonymization, encryption, and role-based access to avoid security mishaps.
- Managing expectations. Carefully assess the ROI of your AI initiative and avoid overselling it to stakeholders. Don’t expect AI to be a silver bullet for any business problem.
Advice for Businesses Looking for an AI Development Partner
Most likely, you’ll need an external AI development and integration partner to help you capitalize on the benefits of AI. Here’s how to select the right service vendor if you don’t have AI expertise in-house:
- Verify experience in your industry. You’ll need a partner who understands your regulatory constraints, industry specifics and benchmarks, and customer expectations.
- Keep track of communication and transparency. You don’t want yes-men and unrealistic promises in your AI initiatives. During the pre-sale process, assess how the team communicates on the project and how transparent they are about their processes.
- Check the technical expertise. AI development typically requires experience with specific programming languages (e.g., Python, Java, Julia), cloud services, and data science and analytics.
- Ensure effective project management. Discuss how the service vendor plans to manage the project and whether they can adapt to your preferred engagement model (e.g., dedicated team or staff augmentation).
- Verify alignment with specific business objectives. Avoid service vendors that sell you on the most trendy AI solutions without regard to your strategic goals.
- Discuss data privacy and security compliance. Your partner should understand how regulatory constraints and security needs translate into technical requirements for your AI solution.
Conclusion
AI is transforming business operations across industries, but AI initiatives can easily go wrong and fail to deliver the expected ROI. That’s why you should carefully select the most effective use cases for artificial intelligence in business and ensure you have the right AI expertise to incorporate AI into your business processes.
Need a reliable partner to implement AI capabilities? Lember builds scalable, reliable AI solutions that align with your concrete goals and KPIs. Book a call with our experts to discuss your AI initiative in depth.
FAQs
What is AI in simple terms?
Artificial intelligence is an umbrella term for a wide range of technologies that allow software systems to perform tasks that would typically require human intelligence.
How is machine learning different from AI?
Machine learning is a part of AI technologies. Machine learning models are trained on vast amounts of data and use statistical algorithms to identify patterns in datasets. While ML is one of the primary AI implementations today, artificial intelligence also encompasses rule-based systems, natural language processing, robotics, and more.
How can AI help me with business strategy?
AI and machine learning solutions can help you improve operational efficiency, enhance customer experiences, mitigate risks, and adopt a data-driven approach to making business decisions.
How will AI affect business in the future?
The use of AI for big data analytics and generative AI will allow businesses to automate more processes in new business functions like risk and compliance, customer service, and product development.
For example, with generative AI, businesses can quickly generate compliance reports or reduce the load on support agents with self-service support via intelligent chatbots. Virtual assistants like Bank of America’s Erica, in turn, can help banking customers manage their finances or accounts. AI-powered analytics, on the other hand, can enable next-gen dynamic pricing and product recommendations, as well as real-time UI/UX personalization.
These and other use cases help reduce costs while improving customer experience and satisfaction. At the same time, businesses will need to ensure responsible AI use to appease customers and regulators alike.
What is AI model development in simple terms?
AI model development is a process during which a team designs, trains, and optimizes AI models. It involves carefully selecting the appropriate AI algorithms, preparing training datasets, “teaching” the model to identify patterns, and validating and fine-tuning the model’s output.
How do AI algorithms learn and improve over time?
AI models are initially trained using supervised, unsupervised, semi-supervised, or reinforcement learning techniques. They can then learn from new data by comparing predictions with actual outcomes or relying on user feedback.