Conversational AI: Benefits and Workplace Uses

Conversational AI is a type of artificial intelligence that can engage in natural, human-like dialogue. Unlike a basic chatbot that relies on scripted automation, it can answer questions, retrieve knowledge, help teams, and speed up work inside a secure, enterprise-friendly environment.

Read about how it works, its benefits for businesses, real-world examples, and how you can bring it into your workplace.

What is conversational AI?  

Conversational AI understands plain-language queries and intent, maintains context, and personalizes responses. It uses natural language processing (NLP), natural language understanding (NLU), machine learning (ML), and other AI techniques to simulate human dialogue.

Here’s how it differs from other types of AI:

• Conversational AI vs. chatbots. Conversational AI is not the same as rules-based chatbots that are commonly used for FAQs or password resets. Basic chatbots match keywords to intent in decision trees with prewritten replies, and unlike conversational AI, scripted bots typically can’t handle multi-turn dialogue.
• Conversational AI vs. generative AI assistants. Generative AI creates new content based on the user’s prompts or inputs. It’s for content creation rather than conversation.
• Conversational AI vs. agent-driven AI systems. While conversational AI is focused on understanding and generating human-like dialogue, agentic AI agents are like independent team members that proactively manage workflows. Conversation AI talks to you and helps you find information; agentic AI is designed to execute tasks on its own.
• Conversational AI vs. enterprise conversational AI. Enterprise conversational AI is geared toward large, complex businesses. These scalable, secure systems are designed to support enterprise security and compliance standards. Enterprise AI assistants, agents, and platforms integrate with business systems, including enterprise resource planning (ERP) and customer relationship management (CRM), and handle high-stakes business processes.

How conversational AI works 

Conversational AI interprets what someone says, determines what information or action is needed, and produces a relevant response. In enterprise settings, a conversational AI platform must also account for permissions and connected systems.

Conversational AI is trained on large language models (LLMs), enabling it to better understand intent, meaning, and conversational context. It uses this knowledge to interact with humans in a natural way, constantly learning from interactions to improve the quality of its responses over time.

1. Processing and understanding input

When a query comes in, NLP translates it into a machine-readable format. Then NLU identifies intent (what the user wants), extracts key entities (such as project names or dates), and interprets meaning. It can interpret partial sentences or casual phrases by comparing the entry to known clusters, like “check time-off balance” or “request time off.” 

In enterprise systems, conversational AI may use context to better understand intent. It could look at conversation history, user role, department, or a relevant Slack channel. For example, a user may ask, “Can you update this?” and conversational AI will determine whether it has enough context to confidently move to the next step. If not, it can ask for clarification or suggest options.

2. Dialogue management, context, and retrieval

Once the system understands intent, it decides how to respond, which typically involves retrieving information from approved sources. A conversational AI search tool might use internal knowledge bases while enterprise platforms also look at connected third-party business systems. Context from prior messages, channel history, user role, and permissions all shape what AI accesses and how it responds. 

It takes this information and applies company logic, like policies or workflows, as it creates a reply. Conversational AI can track the flow of a chat rather than resetting with each prompt. So interacting with it feels natural, like chatting with team members. 

3. Generating and refining the response

The last step is to generate a response. This can include summaries, next steps, or suggested actions. Certain guardrails apply, such as enterprise systems that require citations or handoffs when confidence is low or approvals are needed.

Inside platforms like Slack, this entire flow happens where work already lives, allowing conversational AI to surface knowledge, summarize conversations, and take action across integrated tools without forcing users to switch context.

Examples of conversational AI 

You can apply conversational AI tools to workflows across your enterprise, with solutions that offer varying levels of automation and autonomy. While all can process natural language, each excels in different ways.     

Here are a few examples of conversational AI in the workplace: 

• Virtual assistants. This form of conversational AI primarily follows pre-programmed commands to perform routine tasks that enhance productivity, like managing schedules, setting reminders, and answering common questions. Virtual assistants do not operate autonomously and rely on user input, like text or voice commands, to function.
• Chatbots. Often used in customer service, chatbots also require user input and operate based on a scripted workflow. They are most commonly used to answer simple questions and provide 24/7 support for customers on a web or app-based interface.
• Autonomous AI agents. AI agents are designed to understand and respond to customer inquiries and field a wide range of tasks with minimal to no human intervention. They are capable of independent, data-driven decision-making, complex problem-solving, and proactivity.

Benefits of using conversational AI 

Conversational AI reduces friction between employees and information, surfacing data when and where it’s needed. When conversational AI is implemented across operations, companies see measurable benefits. Slack’s Workforce Lab found that daily users who use AI are 64 percent more likely to report very good productivity and 81 percent greater job satisfaction than team members who don’t use AI.

Here’s how conversational AI can impact performance in your organization: 

Faster response times and improved support

Conversational AI can greatly reduce response times by handling common questions instantly and routing more complex issues to the right teams. The Salesforce State of Service report found that nearly 90 percent of service professionals said conversational AI accelerates self-service resolution rates and resolution times. Consider support environments, where AI-assisted triage gathers background information upfront and surfaces details from prior cases before a human steps in. It keeps conversation history intact, making sure digital agents or humans have the context needed to reply quickly. 

More efficient access to information

Forty-seven percent of digital workers struggle to find the information they need to do their jobs. With conversational AI, teams can ask questions in plain language and receive direct answers or summaries. When knowledge is spread across channels and tools, this benefit is even more noticeable. On average, AI in Slack can save employees 97 minutes weekly just from conversational AI search, summarization, and channel recaps.  

Increased operational efficiency

By automating repetitive interactions, such as status checks, data lookups, or routine updates, conversational AI frees teams to focus on higher-value work. Anyone can chain tasks together to build workflows using intelligent automation tools powered by conversational AI. With these, teams can trigger actions across business systems, like creating help desk tickets or updating CRM records. One security developer platform saved around 8,300 hours monthly by automating routine processes in Slack.

More personalized experiences

Conversational AI can tailor responses to employees and customers, based on role, history, or context. In customer support, personalized experiences drive satisfaction and loyalty. More than that, over a third of consumers would prefer an AI agent if it meant they didn’t have to repeat themselves. In enterprise environments, personalization delivers relevant information to employees while respecting permissions and data boundaries.  

Scalable support without costs rising at the same pace

As organizations grow, support demands increase. Conversational AI lets teams scale assistance across IT, HR, and operations while keeping costs in line. This is possible by optimizing workflows in ways that work best for each organization, whether adding AI agents for frontline support or improving knowledge access for representatives. More than half of organizations using AI agents realized cost savings and improved customer experience. 

Implementing conversational AI into your business 

Conversational AI is most effective when integrated directly into an agentic work operating system like Slack, where it can access your organization’s data and the third-party integrations you rely on. When implementing conversational AI, it’s important to choose a solution that integrates with the business tools you already use, such as your communications, video conferencing, project management, or file-sharing platforms. 

It’s also a good idea to review your existing processes and workflows to identify where AI could have the greatest impact. Try soliciting employee and customer feedback to gain deeper insights.

How to select the right conversational AI tool 

To ensure a positive return on investment (ROI), consider how workflow improvements could help your organization achieve its high-level goals. This will help you compare conversational AI chatbots vs. assistants or platforms to see which approach will move you forward faster. 

Follow these steps to choose the right conversational AI platform:

1. Identify needs and goals. Before you can choose the right AI tool, you must define your goals. Consider what processes could be improved using conversational AI, such as after-hours support or personalized recommendations. Then prioritize tools based on your needs.
2. Gather employee and customer data. Additionally, collect insights from customer data and solicit employee feedback to understand the pain points that exist within your company. This may reveal other processes that could be optimized for better results.
3. Consider security and privacy. To build trust with your customers, your conversational AI tool should be secure and private. For example, Slack AI follows enterprise-grade security and compliance requirements, ensuring all customer data stays within Slack and is not used to train any public LLMs.
4. Evaluate tools. Once you know what you want to achieve, research different options that can help you get there. Be sure to consider user friendliness, integration capabilities, and important features like automation to boost efficiency across your operations.
5. Create a change management plan. Communicate effectively with your employees about new AI tools — including how the tools will benefit them — before implementation. Also be sure to provide sufficient AI tools training. This can help ensure adoption and increase enthusiasm about new tools across your business.
6. Monitor performance. Once conversational AI is implemented, be sure to track key performance indicators (KPIs) to measure your tool’s performance. Analyzing data on a monthly, quarterly, and yearly basis can provide insights to help you use conversational AI more effectively.

What to look for in a conversational AI tool

To help teams interact naturally with LLM-powered chat tools, look for platforms that put conversational interfaces where employees work and collaborate. Natural language automation systems should meet advanced needs while allowing nontechnical team members to use them with minimal training. 

When choosing a conversational AI tool, focus on these key functionalities: 

• Natural language search and summaries. Conversational AI for knowledge retrieval and summaries should answer questions and summarize content across conversations, documents, and tools. Consider platforms that pull information in real time and can summarize content from multiple company sources.
• Conversational task execution. A workflow automation tool should trigger single or multistep actions, workflows, or updates through simple language and help users turn plain language requests into new automations.
• Secure, enterprise-grade context. Make sure your conversational AI productivity tools support built-in permissions, role awareness, and data governance. Consider options that ground responses in company data (versus the internet as a whole) to improve reliability and accuracy. 
• Integrations and extensible workflows. Explore solutions that connect across core business systems and third-party tools through prebuilt connectors, allowing employees to share automations with teams and modify them as workflows evolve.

Slackbot: Conversational AI that works where work happens

If you use Slack, you can tap into Slack AI capabilities to bring conversational AI directly into channels, messages, and workflows. Whether from the sidebar, in a channel, or on a canvas, you can get answers or take action with Slack and any connected applications without having to leave your workspace. 

With Slackbot, every team member has a personal AI assistant to bring them up to speed before meetings and keep them on top of today’s priorities. You can use it for enterprise searches or to recap documents, meetings, and conversations. It can also create and manage to-do lists for you and your team and generate draft briefs, agendas, and summaries.

Workers across a variety of industries use Slackbot to build consistent routines with fewer manual steps and more time for work that matters.

Real-world applications of conversational AI  

Conversational AI can be applied across all enterprise business areas to improve efficiency and engagement. Here are some common use cases for conversational AI across industries:

• Sales teams. Natural language automation systems provide always-on, enterprise-wide sales enablement, giving teams the tools and intel to respond in the moment. Digital sales agents pull product decks and pricing sheets or build comprehensive briefs, helping sellers increase value while closing more deals.
• Marketers. With conversational AI, teams summarize insights across channels to build personalized campaigns with automated outreach. Enterprise-level generative AI chatbot solutions ground responses in company data, helping marketing teams maintain brand consistency, segment audiences, and refine messaging based on sales and service feedback.
• Ecommerce. For enterprise sellers, conversational productivity tools automate routine tasks, like account updates across systems, while identifying upselling and cross-selling opportunities. Virtual AI assistants also become personal shoppers, helping consumers and business buyers find, compare, and purchase products.
• Customer-facing teams. Intelligent help desk automation surfaces account context, summarizes prior interactions, and drafts responses quickly. In shared channels, human representatives collaborate with enterprise AI assistants to serve customers faster, with always-on AI agents, like Slack’s pre-built Channel Expert powered by Agentforce, providing precise answers and suggested next steps.
• HR and people operations. For enterprise-level HR departments with thousands of employees, AI-driven employee support tools allow for round-the-clock onboarding and personalized self-service. Employees can chat with an HR bot about time off, expense reimbursement, or onboarding steps and receive accurate, permission-aware answers without waiting for a callback or other manual reply.
• IT and internal support. AI-assisted support bots answer common questions, gather incident details, and intelligently route issues to the right teams, reducing resolution times and volumes. Using conversational support automation for high-volume VPN or password requests can significantly reduce new ticket volume for enterprises.

Best practices for using conversational AI  

To get the most value from conversational AI, implement it purposefully, with clear boundaries and measurable goals. Start with practical applications where tools are supported by context and governance. 

Consider these best practices to reduce risk and scale responsibility:  

• Target high-impact, narrow use cases. Start with specific, repeatable challenges where conversational AI can deliver immediate time savings. Often, these are use cases such as answering policy questions or summarizing project updates, as they don’t require complex decision-making.
• Prioritize data integrity. Make sure AI pulls from accurate, approved, and up-to-date sources. Information should be grounded in your company knowledge. This keeps responses reliable and auditable. 
• Design for seamless human interaction. Build clear escalation paths so human team members can step in when needed. Exceptions, approvals, or human-in-the-loop checks maintain trust without slowing down day-to-day work.
• Establish rigorous governance. Define access controls, and usage policies from the start. Setting permissions early builds accountability and supports your security and compliance goals as AI use expands across teams.
• Start small and iterate. Launch with a limited scope to measure outcomes like accuracy and adoption, then refine continuously before expanding to additional workflows, integrations, or more autonomous capabilities.

Conversational AI improves customer and employee experiences

Conversational AI helps businesses optimize operations for higher productivity and efficiency while improving key experiences. Virtual assistants and chatbots can assist teams and customers with common queries before, during, and after business hours, and even point them to self-service options to provide continuous support. AI agents like Agentforce bring your crucial third-party app data into one place, making it simple for humans and AI to work side by side to streamline workflows and get smart answers fast. Get more done in less time without compromising quality by integrating the right conversational AI tools into your workflow.

Learn more about how Slack for enterprises can support your team.

Conversational AI FAQs