AI adoption is at an all-time high, but there’s a growing gap between companies experimenting with artificial intelligence and those getting measurable results from it. While 88% of organizations now use AI in at least one business function, many are still struggling to scale their deployments and prove their value.
The challenge has shifted from deciding whether to adopt AI to figuring out where it can make the biggest impact. Businesses are investing in everything from autonomous customer service agents to predictive analytics, but successful implementation requires the right data, clear objectives, and a practical plan.
In this article, we’ll examine the latest AI adoption statistics for 2026, explore how different industries are using the technology, and look at real enterprise deployments that have delivered measurable results.
TL;DR
- AI adoption has reached 88% of organizations, up from 20% in 2017, according to Stanford’s 2026 AI Index, reflecting how widely artificial intelligence has spread across business functions.
- Only 44% of organizations are scaling AI across their enterprises, showing that widespread experimentation hasn’t necessarily translated into successful deployment.
- Adoption rates vary significantly by industry. Information services lead at 39.7%, followed by finance and insurance at 33.9%, according to U.S. Census Bureau data from May 2026.
- Customer service, marketing, software development, and operations are major areas of AI adoption, with applications ranging from autonomous support agents to demand forecasting.
- Poor data quality, security concerns, and employee skills gaps remain major obstacles. Businesses need reliable integrations and clear oversight before expanding AI deployments.
- Successful AI implementation starts with specific goals and measurable outcomes. Organizations should establish performance benchmarks, test with real scenarios, and expand deployments based on demonstrated results.
- Enterprise AI is already delivering measurable gains. Our work with Brinks Home helped reduce cost per contact by 67%, while Roku achieved a 52% AI containment rate, demonstrating what AI agents can accomplish when connected to real business processes.
Key AI adoption statistics for 2026
AI adoption continues to grow in 2026, with businesses expanding their use of AI technologies across more departments. However, the latest research shows a considerable gap between experimenting with AI and deploying it successfully across an organization.
Here are some of the most important AI adoption statistics from recent research:
- 88% of organizations used AI in at least one business function in 2025, up from 78% in 2024, according to the Stanford AI Index Report 2026.
- 79% of organizations regularly used generative AI in at least one business function in 2025, compared with 71% in 2024, according to Stanford’s 2026 report.
- 44% of organizations using AI were scaling it across their enterprises in 2026, up from 38% a year earlier, according to McKinsey’s State of AI 2026.
- 56% of surveyed organizations used AI across at least three business functions in 2026, compared with 51% the previous year, according to McKinsey.
- 54% of organizations with at least $1 billion in annual revenue were scaling AI across the enterprise, compared with approximately one third of smaller organizations.
- 40% of large enterprises were scaling AI agents in at least one business function, up from 27% the previous year. Among smaller organizations, the figure was 22%.
- Global corporate AI investment reached $581.7 billion in 2025, according to Stanford’s 2026 report, reflecting growing investment in AI development and the AI infrastructure needed to support deployment.
These figures show that businesses are moving beyond initial experiments with AI, although adoption remains uneven. Larger enterprises are progressing faster, while smaller organizations often face greater constraints around technical resources and implementation.
The next challenge is turning adoption into measurable business outcomes. Investing in AI infrastructure is only part of the process. Organizations also need employees with the right AI skills to integrate new technologies into their existing operations and use them effectively.
For customer service organizations, this includes moving beyond basic chatbots toward AI agents capable of resolving customer requests, accessing relevant business systems, and handling more complex interactions without constant human intervention.
How has AI adoption changed over time?
Artificial intelligence adoption has accelerated considerably over the past decade, particularly since generative AI tools became widely available in late 2022. Businesses that previously relied on AI for specialized tasks, such as fraud detection and predictive analytics, are now integrating it into everyday operations.
According to the Stanford AI Index Report 2026, organizational AI adoption increased from 20% in 2017 to 88% in 2025. The sharpest recent increase occurred between 2023 and 2024, when adoption jumped from 55% to 78%.
Here’s how adoption has changed over time:
Source: McKinsey organizational AI surveys, as reported in Stanford’s 2026 AI Index. Figures represent survey respondents, not every business worldwide.
Several developments contributed to this growth. Advances in natural language processing made AI applications more capable of understanding everyday conversations, while cloud computing gave businesses access to powerful models without building their own computing infrastructure.
The introduction of widely accessible generative AI tools also contributed to faster adoption. Stanford reports that organizational generative AI usage rose from 33% in 2023 to 79% in 2025.
Customer service offers a clear example of this progression. Early chatbots typically followed predefined rules and answered basic questions. Modern AI chatbots for customer service can understand customer requests, retrieve information from connected systems, and complete tasks that previously required a human representative.
However, widespread adoption doesn’t mean every organization has successfully integrated AI into its operations. Many businesses are still developing the processes and employee expertise needed to use AI effectively. As discussed in our guide to AI change management, introducing agentic AI also requires organizations to rethink how employees work alongside these systems.
AI adoption rates by industry
AI adoption rates vary considerably across industries. Businesses in information services and finance have adopted artificial intelligence more extensively, while industries such as retail have reported lower adoption.
According to the U.S. Census Bureau’s Business Trends and Outlook Survey, approximately 19.8% of U.S. businesses reported using AI in May 2026. Adoption was substantially higher in information services and finance.
The following table shows reported AI use across selected industries as of May 3, 2026.
| Industry | AI adoption rate |
|---|---|
| Information | 39.7% |
| Finance and insurance | 33.9% |
| Retail trade | Approximately 14% |
| All U.S. businesses | 19.8% |
Source: U.S. Census Bureau, Business Trends and Outlook Survey, May 2026. These figures measure AI use within the previous two weeks. They should not be directly compared with Stanford’s organizational survey because the samples and methodologies are different.
Information and technology
The information sector recorded the highest adoption rate among the sectors highlighted by the Census Bureau, at 39.7%.
Businesses in this sector use AI for software development, content generation, data analysis, and IT operations. Many already have the technical infrastructure required to integrate AI applications into existing systems.
Technology companies are also among the earliest adopters of more advanced applications, including AI agents capable of performing tasks across multiple business systems.
Finance and insurance
Approximately 33.9% of businesses in finance and insurance reported using AI as of May 2026.
Financial institutions have used machine learning for years to identify suspicious transactions and assess financial risks. Generative AI has expanded potential applications to document processing, customer support, and internal knowledge management.
Natural language processing also allows banks and insurers to interpret customer requests and provide automated assistance through digital channels.
Retail and ecommerce
Retail businesses reported an AI adoption rate of approximately 14%, considerably below the national average.
AI applications in retail include product recommendations, inventory forecasting, and personalized customer communications. Larger retailers may also deploy conversational AI to handle order inquiries, returns, and delivery questions.
For example, AI assistants can support customer service teams by retrieving relevant information and suggesting responses during customer conversations.
How business size influences AI adoption
Industry isn’t the only factor influencing adoption. Company size also plays a substantial role.
The Census Bureau found that 37% of businesses with at least 250 employees were using AI as of May 2026, compared with less than 20% of businesses with fewer than five employees.
Larger organizations generally have more resources to invest in AI infrastructure and employee training. Smaller businesses may face greater challenges when integrating AI into their existing operations.
However, smaller organizations can still access advanced AI capabilities through cloud computing services and subscription software, without developing or maintaining their own models.
The adoption gap also highlights the importance of having relevant data available. Organizations need reliable information from their business systems to support accurate responses and useful automation.
For customer service teams, this can mean connecting AI agents to order histories, account information, and knowledge bases. Businesses exploring these applications can learn more about how AI agents have evolved from traditional customer service chatbots and the capabilities required for more advanced customer interactions.
How businesses are using AI across departments
Businesses are using artificial intelligence across more departments, with applications ranging from customer support automation to demand forecasting. Traditional AI continues to play an important role in predictive analytics, while generative AI and autonomous agents are taking on tasks that previously required direct human involvement.
Companies that have adopted AI systems are also looking for ways to connect these technologies with their existing software. The goal is to get more value from AI spending by improving how everyday work gets done.
Here’s how businesses are putting AI to work across major departments.
Customer service and contact centers
Customer service teams use AI to handle routine inquiries, resolve common issues, and provide support outside regular business hours. More advanced systems can access customer records and complete requests without requiring a human agent to intervene.
Some of the most practical applications include:
- Automated customer support: AI agents answer frequently asked questions, check order statuses, process returns, and handle routine account requests.
- Intelligent call routing: AI identifies the reason for a customer’s call and directs them to the appropriate agent or department.
- Agent assistance: AI suggests responses and retrieves relevant information while human representatives handle conversations.
- Conversation analysis: AI reviews customer interactions to identify recurring complaints and opportunities to improve service.
At Quiq, we help enterprises move beyond basic chatbots with AI customer service solutions that can resolve customer requests across messaging and voice channels.
For example, an AI agent connected to a retailer’s order management system can check delivery status or initiate a return without transferring the customer to a support representative.
Successful AI implementation requires access to accurate customer information and clear rules for when conversations should be escalated. Our guide to contact center AI covers additional applications for customer service operations.
Marketing and sales
Marketing and sales departments use AI to understand customer behavior and provide more personalized experiences throughout the buying process.
Predictive models have supported lead scoring and customer segmentation for years. Generative AI has expanded these capabilities by helping teams create content and interact directly with potential customers.
Common applications include:
- Lead qualification: AI analyzes prospect information and previous interactions to identify potential customers.
- Content creation: Marketing teams generate initial drafts of campaign materials and adapt messaging for specific audiences.
- Personalized recommendations: AI suggests products based on browsing activity and previous purchases.
- Sales forecasting: Predictive models analyze historical performance to estimate future sales.
- Customer engagement: AI agents answer product questions and help prospects book appointments.
AI can also help sales teams convert customer interest into completed purchases.
For example, we worked with Ideal Image to introduce Izzy, an AI sales agent that helps customers explore treatments and book appointments. Within six months, Ideal Image reported three times as many bookings and a 5.2 times return on investment.
You can learn more about how these applications work in our guide to AI in sales.
When evaluating AI spending, marketing and sales leaders should measure outcomes such as qualified leads, appointment bookings, and completed purchases rather than focusing exclusively on the volume of AI generated content.
IT and software development
IT departments and software development teams use AI to write code, troubleshoot technical issues, and maintain existing applications. Developers can describe a function in natural language and receive code suggestions that they can review and test.
Some of the most common applications include:
- Code generation: AI coding assistants suggest functions, explain unfamiliar code, and help developers complete repetitive programming tasks.
- Bug detection: AI reviews code for potential errors and recommends fixes.
- Software testing: Development teams generate test cases and identify areas that need additional testing.
- Technical documentation: AI explains existing code and helps maintain documentation as applications change.
- IT support: AI assistants answer employee questions and help diagnose common technical problems.
AI is also useful for connecting different enterprise applications. For example, an AI agent can retrieve information from a CRM or update a customer’s account through an API.
Our AI Studio allows enterprises to build and manage AI agents that connect with their existing systems. This gives development teams more control over how agents access information and perform actions.
When implementing AI in software development, organizations should establish clear security policies and require developers to review generated code before deploying it. Testing and monitoring are equally important for AI agents that interact with sensitive business systems.
Operations and supply chain management
Operations teams use AI to forecast demand, manage inventory, and identify problems before they interrupt business activities.
Traditional AI has supported predictive analytics in manufacturing and logistics for years. Generative AI is expanding these applications by helping employees analyze operational information and interact with business systems using natural language.
Common applications include:
- Demand forecasting: AI analyzes historical sales and seasonal patterns to predict future product demand.
- Inventory management: Predictive models help businesses determine stock levels and identify products that may run out.
- Predictive maintenance: AI analyzes equipment data to identify potential failures before they cause production delays.
- Logistics planning: AI evaluates shipping routes and helps transportation teams respond to delivery disruptions.
- Supplier management: AI analyzes supplier performance and identifies potential supply chain risks.
According to McKinsey’s economic research on generative AI, generative AI could contribute between $2.6 trillion and $4.4 trillion in annual economic value across the use cases studied. These figures represent estimated potential rather than financial returns that businesses have already achieved.
AI adoption also extends to human resources. For example, AI recruiting software can help hiring teams analyze candidate profiles and identify applicants whose experience matches open positions.
For operations leaders, successful AI implementation starts with identifying a specific business problem and establishing how improvements will be measured. A warehouse might use inventory accuracy as its primary metric, while a manufacturer could focus on reducing unplanned equipment downtime.
Businesses should also account for the quality of their operational data before implementing AI. Incomplete inventory records or outdated supplier information can produce inaccurate recommendations, regardless of how capable the underlying AI model is.
What’s driving enterprise AI adoption?
Enterprise AI adoption is accelerating as businesses find more practical applications for generative AI and autonomous agents. According to McKinsey’s 2025 State of AI report, 88% of surveyed organizations reported regular AI use in at least one function, compared with 78% the previous year.
Several factors are encouraging business leaders to expand their AI investments.
Pressure to improve operational efficiency
Cost reduction remains a major motivation for enterprise AI adoption. Organizations want to handle growing workloads without increasing staffing requirements at the same rate.
Customer service offers a practical example. AI agents can resolve common billing questions and provide account assistance, allowing human representatives to concentrate on more complicated cases.
At Brinks Home, we helped introduce AI agents that reduced the number of calls reaching human representatives by approximately two thirds. The company also recorded a 67% reduction in cost per contact.
More capable and accessible AI technology
Advances in large language models have made it possible to build applications that understand complex requests and complete tasks across connected systems.
Businesses no longer need to develop their own models to use cutting edge technology. They can access existing models through cloud services and specialized enterprise platforms.
For example, agentic AI workflows allow businesses to automate processes that involve several decisions, such as verifying a customer’s identity before updating account information.
Growing customer expectations
Customers increasingly expect immediate responses and convenient support across their preferred communication channels.
Businesses are responding by introducing AI agents that can provide assistance outside regular operating hours and handle requests without long queues.
An ecommerce customer checking a delayed shipment, for instance, can receive an update directly from an AI agent connected to the retailer’s order management system.
Competitive pressure and digital transformation
Companies are also investing in AI because competitors are finding new ways to improve their operations.
In professional services, AI can help employees analyze documents and retrieve information from large knowledge bases. Manufacturers can combine computer vision with predictive models to identify product defects and monitor equipment.
However, adopting technology solely because competitors are doing so rarely produces meaningful results. Business leaders need to identify specific problems before selecting the AI applications they intend to deploy.
Biggest challenges preventing AI adoption
Despite growing investment, many organizations struggle to move beyond initial AI experiments. McKinsey’s 2025 research found that nearly two thirds of respondents said their organizations had not yet started scaling AI across the enterprise.
The obstacles generally involve data quality and integration, alongside security concerns and organizational readiness.
Poor data quality and disconnected systems
AI systems need accurate information to produce useful results. Unfortunately, many organizations store business data across applications that don’t communicate effectively.
Consider a retailer implementing an AI customer service agent. If order details are stored in one platform and return policies in another, the agent needs reliable access to both.
Without those connections, it might provide outdated shipping information or promise a refund that violates company policy.
Before deployment, organizations should:
- Identify the systems containing the information AI needs.
- Remove outdated or conflicting information from knowledge bases.
- Establish secure connections between AI applications and business systems.
- Assign responsibility for maintaining the underlying information.
These preparations are particularly important when AI agents can perform transactions rather than simply answer questions.
Security and regulatory concerns
Large language models introduce security implications that businesses must address before giving AI access to customer information or internal applications.
Potential risks include exposing sensitive data, generating inaccurate information, and performing unauthorized actions.
For example, a customer service agent should not be able to change another customer’s account details simply because someone asks it to.
A practical LLM governance framework should define:
- Which business systems the AI can access.
- What information the AI can share with customers.
- Which actions require human approval.
- How conversations and system actions are recorded for auditing.
- Who is responsible for investigating errors and security incidents.
Risk management also requires ongoing testing. An AI agent that performs correctly during an initial demonstration may encounter unexpected requests once thousands of customers begin using it.
Our Verified Intelligence capabilities help enterprises address these concerns through guardrails, simulations, and visibility into agent decisions.
Employee resistance and skills gaps
AI adoption can create uncertainty about how the labor force works and which responsibilities employees will retain.
Customer service representatives, for example, may worry that automation will replace their roles. Others may be unfamiliar with the systems being introduced and unsure how to use them.
Organizations can address these concerns by involving employees during implementation and explaining how their responsibilities will change.
A contact center introducing AI agents might train human representatives to handle escalated cases and review AI responses. Supervisors could learn to identify recurring errors and recommend improvements to the AI’s instructions.
Our guide to AI change management covers how enterprises can prepare their teams for these changes.
Difficulty measuring returns on AI investments
Many companies report improvements during isolated pilot projects but struggle to demonstrate measurable gains across the business.
A chatbot might answer thousands of questions, for instance, without actually reducing the number of customers who need human assistance.
Businesses should therefore look beyond activity metrics and measure completed outcomes.
For customer service, useful metrics include resolution rates and cost per contact. Sales departments might track completed bookings, while operations teams could measure inventory accuracy or equipment downtime.
Organizations should also include software subscriptions, integration expenses, and ongoing maintenance when calculating their returns.
How to implement AI successfully
Successful AI implementation starts with a clearly defined business problem. Companies that introduce AI without identifying what they want to improve can end up with expensive applications that employees rarely use.
The following steps provide a practical starting point.
1. Choose a specific business problem
Start with a process that consumes substantial resources or regularly causes delays.
For example, a retailer might discover that 40% of incoming support requests concern order status. Automating these inquiries could be a useful first project.
Avoid attempting to automate an entire department immediately. Select an application with a clear scope and measurable outcomes.
2. Establish a performance baseline
Before implementing AI, document how the existing process performs.
For customer service, this could include:
- Monthly conversation volume.
- Average cost per resolved contact.
- First contact resolution rate.
- Customer satisfaction scores.
These figures provide a basis for evaluating whether AI produces a meaningful improvement.
3. Prepare the required data and integrations
Identify the information the AI needs to complete its assigned tasks.
An agent handling order inquiries may require access to shipping information and customer account records. An internal IT assistant might need approved documentation and access to a ticketing platform.
Model training isn’t always necessary. Many enterprise applications use existing language models connected to company knowledge bases and APIs.
Our AI assistant builder guide explains the technical considerations involved in preparing enterprise AI systems for production.
4. Establish clear permissions and safeguards
Decide what the AI can do independently and when it must involve a human employee.
For example, an AI agent might be permitted to check a refund’s status but require approval before issuing refunds above a certain amount.
Test situations where customers provide incomplete information or ask the AI to perform actions outside its authority.
Our Process Guides help AI agents follow business instructions and use approved tools while working toward a customer’s requested outcome.
5. Test the system with real scenarios
An AI demonstration rarely reflects everything the system will encounter in production.
Use historical interactions to test common requests, unusual customer behavior, and situations requiring escalation.
For a contact center, tests should include customers who change their request halfway through a conversation and those who provide conflicting account information.
Review the AI’s responses for accuracy and confirm that any actions taken in connected systems are correct.
6. Launch gradually and measure results
Introduce the system to a limited group of users or a specific communication channel before expanding deployment.
Compare performance against the baseline established earlier. If the AI resolves fewer inquiries than expected, examine the conversations to identify whether the problem involves missing information or unclear instructions.
Continue monitoring performance after launch. Changes to business policies and customer behavior can affect how well an AI application performs.
Real examples of successful enterprise AI adoption
Enterprise AI adoption becomes easier to evaluate when businesses publish results from actual deployments. The following examples show how organizations have applied AI to specific operational problems.
Brinks Home: Reducing customer service costs by 67%
Brinks Home faced growing pressure on its phone support operations. Many customers contacted human agents for routine requests involving payments and device troubleshooting.
We worked with Brinks Home to introduce AI agents across digital channels, allowing customers to complete common requests without calling a representative.
The results included:
- 67% lower cost per contact, supported by automation and AI assistance.
- 60% of customer transactions handled digitally, compared with 12% previously.
- 30% reduction in inbound call volume over the reported period.
- NPS improvement from negative 55 to positive 50 across digital channels.
The implementation also connected AI with the company’s existing customer service systems, allowing customers to complete transactions rather than simply receive answers.
Read our Brinks Home customer story.
Roku: Resolving 52% of support conversations with AI
Roku needed to support customers across hundreds of products and a large selection of subscription services. Traditional chatbots required extensive maintenance because each support scenario depended on predefined conversation flows.
After evaluating 29 vendors, Roku selected Quiq to introduce an AI agent capable of handling product troubleshooting and account inquiries.
The agent connects with Roku’s existing systems to retrieve information about subscriptions and billing. It can also guide customers through troubleshooting without requiring a separate script for every product issue.
The initial deployment achieved:
- 52% AI containment rate, reducing the number of conversations requiring human assistance.
- An increase in chat usage from 30% to 43% of the relevant contact channel mix.
- Automated conversation analysis, allowing the support team to monitor resolution quality and identify problems.
Roku also retained its existing knowledge base rather than rewriting hundreds of articles specifically for AI.
Read our Roku customer story.
ZeniMax: Reducing player safety investigations from 30 minutes to seconds
ZeniMax, the video game publisher behind several major titles, handles customer support requests involving account access and player conduct.
Player safety complaints were particularly demanding because agents often had to search multiple systems to investigate reported behavior.
Using Quiq AI Studio, ZeniMax developed AI applications that gather relevant account information and prepare recommendations for human review.
The results included:
- Up to 30 minutes of account research eliminated per safety ticket.
- Approximately 50% of support tickets receiving AI assistance.
- 70% of email volume resolved without a live agent.
Human employees still make decisions on sensitive player conduct cases. AI prepares the evidence so they can review cases faster.
Read our ZeniMax customer story.
A restaurant and retail chain: Achieving a 92% resolution rate
A major restaurant and retail chain experienced a sharp increase in customer inquiries following the introduction of a loyalty program. Its guest relations team accumulated approximately 30,000 unresolved tickets, creating a response backlog of several months.
We introduced an AI agent connected to the company’s knowledge base and customer service systems. The agent handled routine guest questions through digital channels and classified conversations for reporting.
The deployment produced measurable improvements:
- 92% resolution rate for AI-assisted guest inquiries.
- 67% reduction in cost per contact.
- 10,000 chats handled in a single day during a peak period.
- A backlog of 30,000 tickets cleared, allowing the company to return to same-day responses.
The AI agent was launched in approximately six weeks, including testing and preparation for the guest relations team.
Read our restaurant and retail customer story.
These examples also demonstrate why enterprises need to define success before selecting technology. Each organization had a specific operational problem and measured whether AI improved the outcome.
What to expect from AI adoption beyond 2026
AI adoption will likely continue expanding beyond 2026, although the focus is shifting from experimentation toward broader deployment and measurable business performance.
Several developments are likely to influence how enterprises invest in AI over the coming years.
More autonomous AI agents
Organizations are expected to expand their use of AI agents capable of completing tasks across business systems.
Rather than answering a customer’s question about a delayed order, for example, an AI agent could check the delivery status and initiate an approved replacement when necessary.
As these systems become more autonomous, businesses will need stronger controls over what agents can access and which actions they can perform.
Our guide to agentic AI orchestration explains how enterprises can coordinate AI agents while maintaining visibility over their decisions.
Greater emphasis on measurable business outcomes
As enterprise AI spending grows, business leaders will face pressure to demonstrate returns beyond pilot projects.
Organizations will increasingly evaluate whether AI improves operational performance and produces financial benefits.
For customer service, this may involve tracking completed resolutions and customer satisfaction. In manufacturing, companies may focus on lower defect rates or reduced equipment downtime.
The ability to measure these outcomes will influence which AI applications receive additional investment.
Expansion of multimodal AI
AI systems are becoming more capable of processing information beyond text.
Computer vision already allows manufacturers to inspect products for defects, while speech recognition supports automated voice interactions.
Future enterprise applications could combine these capabilities to handle more complicated tasks. A customer might send a photograph of a damaged product and explain the problem verbally, allowing an AI agent to assess the request and begin an approved resolution process.
Stronger governance and human oversight
Greater AI autonomy will also increase the importance of security and accountability.
Organizations will need to monitor how AI agents reach decisions and record the actions they perform. This is particularly important in industries handling sensitive customer information or regulated transactions.
We expect enterprises to place greater emphasis on testing AI behavior before deployment and maintaining visibility into its performance afterward.
At Quiq, we’re already addressing these requirements through Verified Intelligence, which combines agent guardrails with simulation and decision visibility.
Future AI adoption will depend increasingly on how reliably organizations can operate these systems in production. Companies that establish clear performance goals and maintain effective oversight will be better positioned to expand their use of AI as the technology develops.
Turn AI adoption into measurable results with Quiq
AI adoption is growing, but implementing AI successfully requires more than choosing the latest technology. Businesses need solutions that work with their existing systems and deliver measurable improvements to customer experiences and operational performance.
At Quiq, we help enterprises put AI to work with autonomous agents that resolve customer inquiries across voice and digital channels. Our agents go beyond answering questions. They can access customer information and complete requests, from checking order statuses to processing returns, without requiring constant human involvement.
Whether you’re exploring AI for the first time or looking to expand an existing deployment, we can help you build AI agents that deliver real business value.
Book a demo with Quiq to see how our AI agents can improve customer service and deliver measurable results for your business.
