AI Use Cases: Insights from AI
Decision Makers

AI goes mainstream with widespread enterprise adoption

Regardless of industry and geography, organizations are looking for ways to leverage AI technology.  71% of respondents surveyed say that their organizations are actively using AI across multiple departments and use cases, signaling that it’s moved from small pilots to widespread, day-to-day operations.

Less than a quarter (24%) are still in the experimentation phase, exploring AI in limited use cases, while just 5% remain in the early research stage.

Notably, none of the surveyed companies said they’re waiting to evaluate AI in the future or believe their teams aren’t capable of using it, underscoring that AI hesitation is over.

Organizations are scaling AI across departments

As organizations roll AI solutions out to different departments, they’re reaching beyond early adopters. IT support leads the way at 69%, emerging as the most AI-enabled department across nearly all revenue bands, followed closely by customer service (59%) and marketing (45%).

Departments like finance, HR, product development, and project management are seeing moderate AI uptake, while legal (24%) and sales (21%) show the lowest levels of AI use, suggesting these functions may still be cautious about AI integration.

AI adoption centers on process, workplace, and service functions across revenue bands.

The role of AI within organizations is becoming more strategic and targeted. Instead of isolated experiments, most enterprises are channeling their efforts into three primary business use cases: improving the workplace, enhancing customer service, and automating processes.

Regardless of revenue, organizations agree that process automation is the most important use case, accounting for 44% of all responses. From compliance and risk management to workflow automation, businesses are using AI to reduce friction and improve consistency in day-to-day operations.

AI in the workplace follows at 31%, with enterprises deploying it to enhance employee productivity. Common applications include task automation, analytics, idea generation, information discovery, and content creation, augmenting how employees work, learn, and make decisions.

AI for customer service claims 24% and is being adopted to deliver better, faster, and more scalable support through applications like chatbots, virtual assistants, and translation.

Industries are aligning AI usage to their core business priorities

Industry-specific adoption patterns show how different sectors are aligning AI with their core business needs.

In technology and software, and financial services, the focus is firmly on insights and analytics, with 77% and 72% respectively, highlighting the role of data as a competitive edge.

Retail, business services and healthcare are leaning toward AI for customer service and support, with 77%, 75%, and 69%, respectively, reflecting their customer-centric operating models.

Meanwhile, process automation use cases are gaining traction across business services (66%), technology and software (66%), and retail (63%), signaling a growing need to optimize processes.

AI priorities vary by region

In Western markets like the US, UK, and Germany, there’s a clear consensus towards using AI for process automation and orchestration, followed by workplace-related cases, while customer service use cases take third place. This pattern reflects a focus on enterprise-grade efficiency and operational scale.

In India and Southeast Asia, while process orchestration remains a top priority, the customer service use cases take second place over workplace use cases, indicating a stronger emphasis on customer engagement in fast-growing digital economies.

Japan and South Korea, though geographically close, show contrasting priorities. Japan mirrors India, placing the process orchestration first and customer service second. South Korea, however, places workplace use cases at the top (37%), showing a greater emphasis on employee productivity.

Australia follows the broader global trend of placing process orchestration first. But New Zealand flips the pattern entirely, making AI for customer service its top priority (36%), a signal of its sharper focus on customer-facing transformation.

The UAE aligns with Western economies, focusing more on process orchestration, followed by workplace use cases, and then customer service.

Information-led use cases lead workplace AI adoption

Examining how AI is applied in workplace use cases reveals a clear hierarchy. Analytics and insights top the list, underscoring the enterprise’s need for data-driven decision-making.

This is followed by information discovery, where AI is helping employees in information discovery through complex knowledge repositories. Content generation and workflow optimization are also gaining traction, whereas idea generation and translation, while still niche, show signs of value.

Workplace AI finds the fastest traction in data-heavy sectors

The data suggests that industries like insurance, energy, and pharmaceuticals are predominantly adopting workplace use cases, where the burden of documentation and data-intensive processes creates a natural fit for such applications.

Automotive, business services, and healthcare organizations are also embracing AI in the workplace, following closely behind the early adopters. In contrast, industries such as hospitality, telecommunications, and travel, which rely heavily on customer service and person-to-person interactions, are looking less towards using AI for workplace use cases.

Workplace AI readiness hinges on talent and data security

The main challenges to implementing AI for the workplace are a lack of talent (39%), data security and leakage (38%), and the cost of LLMs (34%). This shows that organizations need to revisit their AI strategy, where they start embedding AI readiness into workforce planning, prioritizing secure-by-design architecture, and shifting from large deployments to iterative, low-risk rollouts.

Interestingly, only about a quarter of participants mentioned concerns like slow decision-making, leadership hesitation, or pressure to prove ROI, suggesting that most organizations have strong leadership support and enthusiasm for AI.

Organizations demand tangible results from AI

Organizations are measuring success in the AI for the workplace domain through a practical lens, prioritizing operational efficiency (13.9%), employee productivity (10.5%), and output quality and consistency (11%). This signals that enterprises are holding AI to high standards and expecting clear, tangible improvements in work performance.

Enterprises prioritize building AI expertise

Looking back on past AI initiatives, more than two-thirds of organizations plan to strengthen their AI expertise, either by upskilling internally (34%) or hiring externally (33%).

In addition to hiring the right talent, more than half plan to improve data quality as they work on their next AI initiatives. Other key improvement areas include solution security and improving the underlying technology infrastructure.

High confidence in AI for workplace use cases

Despite the challenges and learning curve, enterprise confidence in applying AI for workplace use cases remains high. 89% of organizations plan to increase investment over the coming months. 9% of organizations say that it will stay the same, and only 1% say they’ll scale back. 

Analytics and automation lead process AI adoption

A closer look at how enterprises are using AI for process orchestration, analytics, and insights takes the lead, closely followed by process automation, helping enterprises reduce manual effort and scale execution with consistency. Information discovery and workflow optimization are also used by enterprises, enabling better access to data and smoother task orchestration across teams.

High-volume sectors drive AI process orchestration

Many industries have deployed AI to orchestrate processes. Transportation and government stand out as the most active sectors embracing AI to manage their complex, high-volume operations and improve speed and regulatory compliance.

Closely following are sectors such as business services, insurance, telecommunications, travel, and financial services, which also show high AI adoption, likely because they handle frequent processes with relatively lower risk. In contrast, industries like healthcare and retail are adopting AI more cautiously, possibly due to less urgent needs for process streamlining and higher sensitivity around operations.

Talent gaps and security concerns slowdown the rollout

Transformation at this scale comes with its challenges. Participants say the greatest challenges their organizations are facing when bringing AI tools into their processes are a lack of AI talent and expertise (44%), data security/leakage concerns (40%), and regulatory compliance (37%).

Concerns, like costs, leadership approvals, and internal resistance, are not seen as major roadblocks, indicating strong executive alignment and view towards investing in AI for process automation and orchestration as a necessary investment.

AI success is measured by operational gains

On responding to how organizations measure the value of their AI for business process orchestration initiatives, two metrics stood out: quality of output (13%) and operational efficiency (12.5%). Notably, ROI didn’t even rank in the top five, suggesting that while financial returns matter, enterprises are more focused on AI’s ability to strengthen execution and reduce operational risk. 

AI talent and data quality top lessons

When considering past AI initiatives, nearly 70% of participants plan to hire additional AI expertise in preparation for future AI efforts. They are, however,
divided as to whether they will be upskilling internally or hiring externally.

Beyond hiring AI talent, companies report their biggest lessons have involved improving data quality (54%), strengthening security (47%), and enhancing cross-team alignment (40%). These findings suggest that while often underestimated early on, these factors quickly become critical to successfully scaling AI initiatives.

Businesses commit to expanding AI in core operations

Enterprise sentiment towards future investments in AI for business process orchestration is overwhelmingly optimistic. 88% of organizations say they plan to increase investment in AI for the process in the months ahead, with just 1% expecting a reduction.

Support-led use cases lead customer-facing AI usage

Looking at how enterprises use AI for customer service, customer support & chatbots take the lead as the most widely adopted applications. Organizations are using AI to automate responses and assist human agents with real-time suggestions. The next most widely used applications are analytics and insights, helping organizations understand customer behavior and spot issues before they escalate. Virtual assistants are used to guide users through tasks or provide round-the-clock support.

Translation and personalization, though they remain relatively underused, present a clear opportunity to enhance tailored customer experiences.

Customer-intensive industries drive AI for customer service adoption

Industries that rely heavily on customer interaction, such as hospitality and travel, lead in adopting AI for customer service, using it to streamline queries, reduce wait times, and ensure consistent support during peak demand.

Sectors like telecommunications, financial services, retail, and technology are following closely. In contrast, sectors like insurance and automotive, despite managing large volumes of customer interactions, are taking a more cautious approach when it comes to using AI for direct customer service applications.

AI for customer service faces talent and security barriers

Implementing AI for customer service brings its unique challenges, with data security and leakage (44%) topping the list. As customers interact with chatbots and self-service tools, ensuring their personal information is protected is critical to maintaining trust.

Lack of AI talent (40%), costs (38%), and pressure of ROI (33%) also weigh heavily on organizations, reflecting the challenge of scaling AI in high-volume environments. In contrast, infrastructure issues, leadership hesitation, and internal resistance remain relatively low, suggesting a strong organizational appetite to make AI work for customer experience.

Success in AI centers on customers

When it comes to measuring success, enterprises are keeping it customer-focused. Customer satisfaction leads (14.7%), followed by operational efficiency (10.8%), highlighting a dual priority of delighting users while keeping service delivery sharp and scalable.

Talent, data, and budget top AI for customer service priorities

Based on past AI projects, two-thirds are looking to strengthen AI expertise, either by upskilling internally or hiring externally. This requires organizations to both seek new employees with AI expertise and train existing employees to develop those skills.

Additionally, over half plan to improve data quality in preparation for future AI initiatives to take better advantage of their AI tools.

Securing more budget for their service-related AI initiatives was also identified as a top priority to achieve their goals and support their technology infrastructure requirements.

High confidence in AI for customer service use cases

Despite the challenges and learning curve, enterprise confidence in AI for customer service use cases remains high. 89% of enterprises say they will increase investments in AI for customer service in the coming months. Only 1% plan to scale back. That shows that AI is becoming a permanent part of how enterprises engage, respond, and retain their customers.

Consensus on the top AI use case for ROI

When it comes to expected ROI, organizations show comparable levels of alignment in how they rank the top three use case areas.

With enterprises of all sizes, applying AI for process automation and orchestration leads the way, identifying it as the area with the greatest ROI potential. Close behind is workplace productivity, while AI for customer service follows. The tight range of responses across company sizes tells that enterprises may differ in their approach, but they’re starting to agree on where AI matters most.

AI ownership aligns with functional expertise

When it comes to ownership of AI initiatives, enterprises are showing consistency in how responsibilities are distributed. CIOs are leading the way across all three use cases, reinforcing their expanding role as drivers of business transformation. At the same time, boards and finance teams have the least involvement, suggesting that AI is being prioritized as an operational tool rather than a financial debate.

Interestingly, CEOs are not among the top three influencers for any use case, though their involvement is slightly higher when it comes to AI for customer service, where customer experience and brand perception are at stake.

Department heads, on the other hand, are playing a stronger role in AI for workplace initiatives, where productivity and team-specific tools are more relevant. Developers and technical staff are taking the lead in AI for business process orchestration initiatives, even more so than Chief AI Officers or CEOs.

Enterprises favor buying over building AI solutions

Regarding building vs buying, the clear majority of enterprises are leaning toward buying over building. Only 28% of organizations said they would build their own AI solutions, while the remaining 72% are exploring various purchase-led strategies.

Among them, there’s a preference for buying holistic third-party out-of-the-box, ready-to-deploy AI solutions (31%), indicating a strong desire for speed. Another 25% prefer buying third-party solutions and customizing them to their environment, and the remaining 16% prefer to assemble best-of-breed tools to create integrated stacks.

This data suggests that enterprises probably prefer control over complexity. They’re increasingly investing in partners who can deliver proven solutions with the ability to tailor, scale, and integrate, without needing to reinvent the wheel.

Practicality and performance lead AI vendor decisions

When evaluating potential vendors, output quality and accuracy (45%) is the most frequently considered characteristic, regardless of whether process, service, or work is believed to be most important.

Model/Solution efficiency (34%) ranked as the second most important consideration for companies shopping for an AI solution, and ease of use, industry expertise, and integration capabilities tied for third. This suggests that practical usability and contextual relevance now outweigh technicalities.

Kore.ai Business Solutions