In this article, we'll explore practical AI use cases across marketing, sales, and customer service to demonstrate how AI can solve common business challenges.
As of now, 35% of businesses have integrated AI into their operations, while 42% are considering it for the future. If you wait too long, you may be left behind. Those who lead the way are already seeing significant benefits, with over 92% reporting measurable results from their AI initiatives.
However, many organisations are still uncertain about how to effectively use AI for their needs.
In this article, we'll explore practical AI use cases across marketing, sales, and customer service to demonstrate how AI can solve common business challenges.
These use cases are taken from this free guide that includes 47 real-world use cases and Huble's SPARK AI Framework and gives you deeper insights and strategies to help your organisation accelerate innovation and drive results.
Sales teams of the future: AI use cases for sales
Sales teams often face common challenges that slow down the sales cycle and reduce overall efficiency.
These challenges include the manual process of lead qualification, inconsistent data across platforms, and time-consuming tasks such as creating quotes or responding to RFPs.
Let’s look at how AI can solve these challenges.
Use case 1: Use AI to enrich the data you have in your database
Challenge: Inefficient lead qualification and data gaps
Sales teams often spend significant time sifting through incomplete or outdated lead data, which can delay responses and reduce the effectiveness of outreach. When critical information about leads is missing or inaccurate, it becomes harder to personalise communications or craft compelling pitches.
Solution: AI-driven data enrichment
AI can automatically enrich lead data, pulling in real-time information from a variety of sources. By updating lead profiles with relevant details, such as company size, industry, and decision-makers, AI ensures that sales reps have a full, up-to-date picture of the prospect.
For example, you may use AI to enrich your lead database by pulling in real-time information from LinkedIn, industry reports, and company websites, updating lead profiles with details like company size, revenue, and key decision-makers.
This allows your sales team to personalise outreach more effectively and prioritise high-value leads based on the most relevant information.
Use case 2: Identify relevant case studies using AI
Challenge: Wasting time finding relevant use cases
Another hurdle for sales teams is finding the right case studies or customer success stories that resonate with each potential customer. Manually sifting through documents to find relevant examples can be tedious, and the time spent doing so could be better used engaging with prospects.
Solution: AI-driven case study recommendations
AI can analyse a prospect’s industry, challenges, and interests, automatically suggesting the most relevant case studies or use cases for each lead.
For example, a sales representative engaging with a fintech company will automatically receive recommendations for case studies on how the products or services have helped other financial institutions increase their operational efficiency.
This allows sales teams to present highly targeted, compelling stories that align with the prospect's specific needs—ensuring they always have the right evidence to move the conversation forward.
Use case 3: Using AI to design customised solutions for complex sales
Challenge: The labour-intensive process of tailoring solutions
In complex sales environments, creating personalised solutions for each customer is a labour-intensive task. Sales reps need to configure different product or service combinations, often taking hours to ensure they meet the customer’s unique requirements. This can lengthen the sales cycle and reduce agility.
Solution: AI quickly matches customer needs with the best solutions
AI can assist in this area by analysing a prospect’s requirements and quickly matching them with the most relevant product or service options. This reduces the time needed for solution design, enabling sales reps to deliver more accurate and personalised proposals faster, improving both efficiency and customer satisfaction.
Use case 4: Automate discount approvals using AI
Challenge: Delays in discount approvals
The manual process of discount approval can create bottlenecks in the sales process. If approvals take too long, it can lead to frustration for both the sales team and the customer, slowing down the entire deal-making process.
Solution: AI automates the discount approvals process
AI can streamline and automate the discount approval process by applying pre-set rules and criteria, making decisions almost instantly.
For instance, you can use AI to automatically approve discounts based on pre-set thresholds, such as discount percentage limits and customer tier. When a sales rep requests a discount, the AI system instantly checks if it meets the criteria and grants approval, speeding up the process and reducing the administrative burden.
This ensures that approvals are quicker and that discounts are consistent with company policies, allowing sales teams to close deals faster and with fewer roadblocks.
Use case 5: Automate RFP submissions with AI
Challenge: Time-consuming RFP responses
Responding to requests for proposals (RFPs) can be a repetitive and resource-heavy task. Each proposal requires significant attention to detail, but much of the content is similar across multiple submissions, leading to inefficiencies.
Solution: AI-powered RFP automation
AI can automate the RFP response process by pulling from a library of previous proposals and customising the content for the specific request.
This reduces manual work, speeds up the response time, and ensures that each proposal is tailored to the needs of the prospect, improving the quality of submissions and boosting the sales team’s productivity.
AI-driven marketing teams: AI use cases in marketing
Marketing teams often juggle a variety of tasks, from segmenting audiences to creating personalised content and optimising campaigns.
These tasks, while crucial, can be time-consuming and resource-intensive, leading to inefficiencies that slow down the go-to-market process. The challenge lies in manually handling large volumes of data and content, which can lead to missed opportunities and inconsistent messaging.
Let’s explore use cases of AI in marketing.
Use case 1: Optimise ad campaigns automatically
Challenge: Time-consuming Ad campaign optimisation
Marketing teams spend considerable time optimising ad campaigns, adjusting bids and targeting settings to maximise impressions and conversions. While these tasks are necessary, they can be tedious and distract from higher-level strategic work.
Solution: AI automates Ad campaign optimisation
AI can automate the optimisation of bidding strategies and campaign settings in real time, ensuring that ad spend is always allocated effectively.
For example, a global e-commerce brand uses AI to automate the bidding and targeting of its paid search ads across Google Ads and Facebook. The AI adjusts bids in real-time based on performance data, ensuring that budget is spent efficiently, and continuously tests different audience segments to maximise conversions.
This means your marketing team can focus on strategies while AI handles the granular aspects of campaign management, ensuring optimal performance without constant manual adjustments.
Use case 2: Quickly generate bespoke pitches and slide decks
Challenge: Slow and repetitive content creation
Creating bespoke pitches, slide decks, and presentations is an essential part of marketing, but it can be an incredibly time-consuming process. Customising content for different audiences or teams can slow down campaign execution and create delays in communication.
Solution: AI to quickly generate bespoke content
AI can help marketers generate custom presentations and slide decks faster by pulling from a database of approved slides and adapting the content for different industries, audiences, or problems.
Marketers can use AI to generate tailored slide decks for different verticals by pulling from a pre-approved database of slides. AI automatically customises each presentation for the specific needs of a prospective client, adjusting the messaging based on industry-specific challenges and opportunities.
This ensures that the messaging remains consistent, aligned with branding guidelines, and tailored to specific needs—allowing your team to move quickly and efficiently.
Use case 3: Track industry events and opportunities automatically with AI
Challenge: Missing key industry events and opportunities
Tracking relevant industry events, webinars, and conferences is crucial for staying ahead of the competition. However, manually searching for these events can be labour-intensive, leading to missed opportunities and delayed decisions on where to engage.
Solution: AI-driven event tracking
AI can automatically track industry events and opportunities, generating a calendar of relevant conferences, webinars, and other key happenings in your sector.
For instance, you can use AI to scan industry websites, newsletters, and social media platforms for upcoming conferences, webinars, and networking events. AI then compiles this data into an automated calendar.
This proactive approach ensures that your marketing team is always aware of important events and can plan accordingly, without wasting time on manual research.
Use case 4: Develop a buyer persona AI agent
Challenge: resource-intensive buyer persona development
Building and maintaining detailed buyer personas requires constant analysis of customer behaviour, which can be resource-intensive. Without accurate, data-driven insights, marketing efforts may miss the mark, resulting in less effective targeting and communication.
Solution: AI-enhanced buyer persona creation
AI can analyse customer data and engagement patterns to create highly detailed and accurate buyer personas.
For example, a retail company uses AI to analyse customer purchase history, browsing behaviour, and social media activity to build and continuously update detailed buyer personas. The AI segments customers by preferences, spending habits, and demographics.
By continuously updating these personas with real-time insights, AI ensures that your marketing team is always targeting the right segments with personalised messaging, leading to more conversions and stronger customer relationships.
Use case 5: Build an AI case study request agent
Challenge: Slow case study collection process
Gathering customer case studies for marketing materials often involves waiting for approval and manually chasing down the necessary details. This process can be slow and inefficient, which delays the production of content that showcases your company’s successes.
Solution: AI-powered case study generation
AI can automatically identify successful customers who would be ideal for case studies and send out requests for approval and information.
You may use AI to identify top-performing clients who could be great candidates for case studies. An AI agent automatically reaches out to them via email with a personalised request for case study participation, streamlining the collection process.
This automation speeds up the process, allowing your marketing team to quickly gather fresh, relevant case studies at scale, ensuring that your content library remains up to date and compelling.
AI for customer service teams: AI use cases in customer service
Customer service teams often face the challenge of managing high ticket volumes, ensuring timely responses, and meeting service level agreements (SLAs). These manual tasks can slow down resolutions and impact overall customer satisfaction.
However, AI offers powerful solutions to streamline workflows, enhance efficiency, and provide more personalised, timely support.
Use case 1: Predict customer issues before they happen with AI
Challenge: Delayed response times and escalations
In many customer service environments, teams are often reactive, addressing issues only after customers have raised them. By the time a problem is identified, the customer experience may already be affected, leading to frustration, longer resolution times, and increased escalations.
Solution: AI to predict and prevent customer issues
AI can analyse historical data, customer behaviour, and product usage patterns to predict potential problems before they arise.
For example, a SaaS company providing project management software uses AI to analyse historical support tickets, product usage patterns, and customer feedback. By integrating machine learning models, the system identifies early warning signs of common issues, such as feature bugs or integration failures, before customers report them.
This allows customer service teams to take proactive steps, addressing concerns before they impact the customer. As a result, the volume of incoming tickets is reduced, and customers receive faster, more efficient service, leading to higher satisfaction.
Use case 2: Talk with your customers in their language with AI multilingual translation
Challenge: Language barriers in global customer support
As businesses expand globally, offering customer support in multiple languages becomes increasingly challenging. Hiring dedicated support staff for each language can be costly and inefficient. Without an effective solution, teams may struggle to provide consistent support across diverse regions and languages.
Solution: AI-powered multilingual support
AI-driven translation tools provide real-time multilingual support, allowing customer service teams to communicate with customers in their preferred language.
For instance, when a French-speaking client contacts your support team, the AI system automatically translates the inquiry from French to English and also translates the response back into French.
This breaks down language barriers, ensuring consistent and high-quality service across regions without the need for a large, specialised multilingual team. AI ensures your business can deliver seamless support to global customers, enhancing their experience.
Use case 3: Ensure timely SLA fulfilment with ai alerts and tracking
Challenge: Tracking and meeting SLAs
Meeting service level agreements (SLAs) is critical for customer satisfaction, but manually tracking SLA deadlines can be challenging. With large volumes of tickets, service teams may struggle to stay on top of all the deadlines, leading to missed commitments, customer frustration, and possible penalties.
Solution: ai to monitor SLAs and send timely alerts
AI can monitor SLA progress in real time and automatically send alerts when deadlines are approaching. For instance, if an issue is categorised as high priority but has not received any updates after a certain period, the AI system automatically triggers an alert to the support manager.
This ensures customer service teams are always aware of their commitments, reducing the risk of SLA breaches. With AI tracking and reminding agents, businesses can maintain timely responses, fulfil SLA requirements, and improve customer satisfaction by delivering on promises.
Use case 4: Send proactive notifications in the event of outages
Challenge: Managing overwhelming inquiries during system outages
System outages often result in a surge of customer inquiries, which can overwhelm customer service teams. Customers continuously ask for updates, making it difficult for agents to focus on resolving the actual issue. This slows down the resolution process and further frustrates customers.
Solution: AI to automate outage notifications
AI can monitor system health and automatically send real-time notifications to affected customers in the event of an outage. For example, if your company’s database is temporarily unavailable, AI will notify users that access may be intermittent.
By keeping customers informed and reducing the volume of repetitive queries, AI allows service teams to focus on addressing the root cause of the issue. Proactive communication ensures customers feel valued and reduces unnecessary pressure on support teams.
Use Case 5: Recognise cross-selling and upselling opportunities with ai
Challenge: Identifying cross-sell and upsell opportunities
Identifying cross-sell and upsell opportunities requires deep insight into customer behaviour and purchase history. Customer service agents may miss these opportunities, especially when handling high volumes of support tickets or lacking relevant data about customer preferences.
Solution: AI to identify cross-sell and upsell opportunities
AI can analyse customer interactions, buying history, and behaviour to identify relevant cross-sell and upsell opportunities. For example, AI detects that a customer who frequently uses basic CRM features is not yet utilising advanced analytics tools. Based on this data, the system prompts a support agent with an automated suggestion to offer an upgrade to the customer.
By suggesting personalised product recommendations during customer service interactions, AI empowers agents to offer additional products or services that can add value for the customer. This boosts sales while providing tailored solutions, enhancing both customer satisfaction and revenue.
Unlock the full potential of AI in your business
AI is transforming the way businesses approach marketing, sales, and customer service. From automating time-consuming tasks like lead qualification and ad optimisation, to improving customer service by predicting issues and personalising experiences, AI is helping teams operate more efficiently.
However, the journey to AI adoption can feel overwhelming, and it’s understandable to have questions about how to integrate AI into your existing processes without disrupting operations or losing the personal touch that customers value.
Huble’s SPARK AI framework ensures that your business not only adopts AI but does so in a way that is aligned with your goals, enhances team performance, and drives meaningful, long-term results.
Ready to start your AI journey? Whether you’re looking to enhance your marketing, improve sales efficiency, or streamline customer service, we’re here to guide you every step of the way.
Contact our team today to learn more and take the first step toward unlocking the full potential of AI for your business.
