Corporate function employees comprise marketing, sales, customer support, accounting, finance, legal, and HR departments. Each department delivers unique value to your business. But most deliver value only 30% of the time.
A Salesforce survey discovered that sales representatives spend 70% of their workday on non-selling tasks (administrative work, meeting preparations), with 30% left for closing deals and engaging with prospects. On a global scale, the employee engagement rate at work reached 21%, resulting in a $438 billion loss in productivity for the world economy.
The question is: how do you flip the ratio? How do you give employees 70% of their day back for meaningful, high-value work, while limiting the routine overhead to 30%?
Through the strategic adoption of digital productivity tools, including conversational AI technology and copilots. A study by OpenAI indicates that 62% of AI value can be realized in core business functions. These solutions help increase productivity by enabling employees to:
- resolve issues on the spot
- find answers when needed most
- continuously learn (e.g., AI sales coaches collect data on calls with prospects to highlight areas that need improvement).
Unlike traditional automation software, such as robotic process automation (RPA), AI productivity tools do much more than simply entering data. For instance, apart from automatically sending emails, AI can first draft these emails by analyzing previous client calls and interactions.
Read on to explore how to choose, implement, adopt, and measure AI copilots and conversational AI to achieve the maximum productivity levels at your company based on AI in workplace examples from real-world sales, marketing, and customer support teams.
The Xenoss team knows firsthand how to successfully implement AI, as you need to ensure that these tools integrate with your infrastructure, don’t diminish the value of existing automation tools, and, most importantly, don’t create an additional burden for your team.
The employee inefficiency problem
Regardless of the company size and org chart complexity, your teams may struggle with daily inefficiencies and operational challenges. Instead of focusing on their direct duties, employees become distracted and overwhelmed by administrative and low-value tasks.
A startup may have fewer employees, but each employee often has to wear multiple hats and juggle tasks that extend beyond their scope of responsibilities.
Mid-sized companies may struggle with task prioritization, as they already have larger teams but lack effective methods for distributing a growing workload among their employees efficiently.
Large companies often become bogged down in bureaucracy and complex processes that consume time and energy, hindering their ability to operate efficiently.
In fact, employee inefficiency tends to increase as your business grows and headcount expands. Today’s decisions regarding employees’ operations define tomorrow’s business performance and profitability.
Why choose AI to tackle operational challenges, or how to convince your board to invest
AI won’t solve every employee productivity issue, but it can be effective in saving employees’ time, improving decision-making accuracy, increasing sales efficiency, and reducing error rates. Below is a breakdown.
Increased profitability through automation
When trained on high-quality internal datasets, AI systems can be highly effective in analyzing vast amounts of data, supporting informed decision-making, and identifying errors. By automating multiple areas employees juggle during a workday, AI promises to enhance the quality and speed of work, resulting in increased business efficiency and profitability. According to a recent PwC study, companies that adopt AI tools experience three times higher revenue growth per employee compared to those that resist using AI in the workplace.
Reduced errors and improved quality
Tedious work processes, frustrations, and frequent interruptions negatively impact employees’ productivity levels, leading to burnout and increased error rates. The Microsoft Work Trend Index 2025 reveals that 82% of global employees admit they lack the time and energy to perform their job effectively, as they face up to 275 interruptions a day. Among common examples are:
- manual searches for information
- dealing with incomplete data
- administrative tasks
- taking meeting notes
- last-minute edits
Employers in the US are losing approximately $300 billion due to stressed employees. Automating and streamlining repetitive duties with AI in workplace can help teams reduce interruptions and errors, allowing them to stay focused on what matters (service quality, customer engagement, and team collaboration) and improve work quality.
Make HR processes human again
AI-powered communication tools and copilots can automate onboarding, training, mentorship, and performance evaluations. Thus, AI can help businesses accelerate time to productivity (TTP) for new hires and enable long-term employees to continuously assess their achievements, thereby boosting morale and motivation. Whereas HRs will be able to devote more time to genuine one-on-one communication, conflict resolution, and maintaining a healthy working environment.
Bring AI from the shadows into the light
A McKinsey study discovered that employees use AI in the workplace more than their leaders expect them to. Some employees use AI tools in a disorganized (and probably not secure) manner, while others may be reluctant to use them at all. So if a significant number of workers already use AI tools for productivity privately, wouldn’t it be wiser to implement them as a cross-company strategy and achieve consistent productivity improvements? Survey your employees to discover which issues they primarily solve with AI and how these tools save them time and effort on a daily basis. These findings will help you evaluate the relevance and feasibility of AI adoption in your organization.
Cross-team alignment
AI copilots and conversation tools can serve as a single knowledge hub, enabling marketing, sales, and support teams to access the same insights, reducing miscommunication and duplicated effort, which can often result in missed business opportunities. Team misalignment leads to disengagement and a fragmented focus, where everyone thinks of completing the task rather than delivering value.
Identify the real-life issues that drain your employees the most and base your AI initiatives on those findings. As our CRO, Mariia Novikova, puts it:
Copilots and conversational AI solutions: Types, pricing, implementation, and ROI
Conversational AI and productivity copilots are based on natural language processing (NLP) and natural language understanding (NLU) technology. Thus, they quickly understand and process human language, mimic real-life and real-time human conversations, and produce useful outputs in multiple formats (text, audio, video). These solutions become versatile productivity tools as they address multiple needs at once, such as communication, search, and content creation.
AI copilots for instant collaboration
An AI-powered solution designed to collaborate with human users, seamlessly embedded within business tools and workflows to automate routine tasks and provide context-aware insights. They simplify the search for information across systems and files, help employees compile relevant data for easy access, and can generate insights and projections based on files and documents (e.g., analysis of Excel spreadsheets).
Conversational AI solutions to provide answers to every question
Conversational AI falls into several categories, such as AI chatbots, LLMs for content generation, and AI virtual assistants.
AI chatbots for cross-company real-time messaging
As the name suggests, chatbots simulate human communication and provide relevant information in real time in either audio or text format. Through a branded interface and fed with your internal data, they can become an advanced search engine that quickly retrieves necessary data and explains to your employees how to apply it efficiently. Chatbots can also be user-facing and unburden your customer support team by processing a part of customer requests.
RAG-enhanced LLMs for custom content generation
You can also develop large language models (LLMs) that source data from your proprietary knowledge bases to create context-rich content that directly matches your audience’s pain points and aligns with the brand strategy.
AI virtual assistants to support when it’s needed the most
Virtual assistants are similar to copilots, but their primary aim is to support and streamline workflows rather than generate insights or enhance decision-making. AI productivity assistants can be text- or voice-based to manage calendars, summarize meetings, prioritize tasks, and facilitate cross-tool coordination, helping employees regain focus time and cut through organizational noise.
Which AI productivity tool to choose
Select a suitable copilot or conversational AI solution based on the specific daily pain points your employees face. Which issues take up the most of their time and cost your company the most? That’s your starting point. The first thing Estée Lauder did before implementing LLMs was to ask their employees directly how they would like to use them. Be open to conversation with your employees; they might already have the answers as to which tool they need to increase productivity, but aren’t confident enough to voice their needs.
Plus, consider your AI maturity level. If you’re new to AI solutions and haven’t yet tested them in production, it might not be wise to invest in 240 custom LLMs like Estée Lauder did. Instead, you could start with a simple chatbot to help your marketing team simplify data search and create reports with speed. Based on the above examples, AI chatbots offer the highest ROI, with a relatively low price and implementation simplicity.
Scale with time as more teams see the value of AI implementation for their teams.
From Xenoss experts: Infrastructure and tech stack to increase time-to-value
Your data infrastructure layer is the foundation for AI systems, as they require around-the-clock access to high-quality and reliable datasets to deliver accurate outputs. Collecting, storing, cleaning, and processing data from multiple sources is the first task for your internal or external engineering teams before AI adoption.
The next step would be to integrate AI with your proprietary systems. This process also requires engineering input to ensure data consistency, as legacy systems and AI may not be readily compatible with each other.
Dmitry Sverdlik, CEO at Xenoss, gives a possible solution to this issue:
To accelerate time-to-value when implementing AI systems, many teams leverage low-code or no-code integrations embedded in third-party AI tools. They enable seamless integration into existing workflows with minimal disruption. Adding a human-in-the-loop layer is equally important for oversight, ensuring that outputs for client-facing, compliance-sensitive, or financial tasks get reviewed before execution.
Apart from that, copilots and communication AI tools require a layered governance model with robust access controls, transparent audit logs, and compliance with industry-specific regulations such as GDPR, HIPAA, and PCI DSS.
Implementation timeline and associated costs
As for implementation, the cost varies by scope, but quick wins often start with AI tools embedded into a single function (e.g., sales or customer support) before being scaled company-wide.
A pilot AI solution can be integrated in a few weeks, especially with low-code tools, while full enterprise rollouts may take months. Internal expertise in data engineering and IT is useful but not always required upfront, and collaboration with external partners can accelerate deployment.
The highest-priority elements are clean data pipelines and integration into core business systems, since these directly determine whether copilots and conversational AI systems deliver measurable productivity fast.
Xenoss experience
We built a conversational AI chatbot for a global on-demand delivery service. Their support teams were overwhelmed due to rapid business expansion and needed an efficient solution to handle routine inquiries, process them in multiple languages, and maintain brand consistency.
Our AI and data engineering team developed a real-time NLP pipeline integrated with backend systems for up-to-date information retrieval. We combined several classification, recognition, and predictive models to recognize and handle user intents in different languages. For cross-channel use and simplified access, we deployed a chatbot on the company’s website, mobile apps, and across messaging channels.
This solution resolves 40% of inquiries, freeing up support agents from operational burden.
ROI-proven implementations across business functions and industries
The following real-life adoption examples demonstrate how the implementation of conversational AI and copilots enables teams to manage daily challenges.
Case #1: Sales and customer support assistance
Verizon deployed an AI assistant to reduce call time for its 28,000 customer service representatives, freeing them up to sell products to customers. Verizon’s new internal software was created by feeding a version of Google’s language model, Gemini, with nearly 15,000 internal documents. As a result, Verizon reduced call times and improved cross-sell and up-sell interactions, leading to a 40% increase in sales. Enhanced employee efficiency led to measurable business improvements.
Case #2: Advertising and marketing
A metasearch engine for comparing hotel and accommodation prices, trivago, needed a quick solution to unburden their marketing team from manually creating targeted TV ads for over 30 markets. With AI video generation technology HeyGen, the company reduced post-production time, saving their marketing teams an average of 3-4 months of work. Text-to-speech technology enables trivago to produce TV ads in multiple languages, targeting global markets. They successfully localized advertisements in 15 locations in three months. And in less than a year, trivago has created TV ads for all 30 regions.
Case #3: Healthcare support system
With the help of an AI-powered appointment scheduling and confirmation system, Allure Medical increased its appointment confirmation rate by 25% and automated 3,500 calls per month, eliminating the need for manual, time-consuming calls and freeing up administrative staff for more meaningful interactions with patients.
Case #4: Root cause analysis in financial services
Australia’s Bank of Queensland has more than 3,000 employees and nearly 1.4 million customers. They identify overlooked risks with root cause analysis. A/B testing revealed that data analysts using Microsoft Copilot were able to determine the root cause 51.8% faster than those who did not use it. Even the fastest analysts who didn’t use AI couldn’t beat this speed level. The bank concluded that integrating Copilot for 1,000 employees is equivalent to adding 120 new employees in terms of productivity.
These were positive outcomes of AI adoption, but how do you encourage people to use these tools as you intend them to?
Driving adoption: change management and employee enablement
No matter how beneficial new technology is, it’s still new, and some level of resistance to change is inevitable. Transparency and direct communication are key from the outset. Well-prepared AI adoption can take weeks instead of months, which is often the case in environments that already have to put out numerous fires during the post-implementation period.
Simply introducing AI as a new innovative solution won’t make it. Harvard professor John Kotter has developed an eight-step framework for managing change within an organization. He suggests building a coalition of volunteers at your company who are willing to facilitate change and, most importantly, remove barriers to AI adoption. If you integrate AI but your teams still struggle with legacy bureaucratic processes or systems, AI will feel like an additional burden.
Clear change management and leadership strategies encourage AI adoption. According to a Gallup report, employees state that the biggest AI adoption challenges are unclear use cases or value propositions, a lack of established guidance and policies, and a lack of comprehensive training programs. The same report also reveals that employees are three times more likely to use AI when leadership communicates a clear plan.
Here are a few AI adoption best practices we’ve accumulated over the years on AI projects of diverse complexity:
- Start with pilot programs, which can take up to eight weeks, including project ideation, pilot development and testing, and preparing the scope for future scaling.
- Invite the most skeptical employees to participate in pilot projects, allowing them to voice their concerns and opinions before the rollout goes live.
- Directly communicate security and data policy concerns and establish trust with your employees. Be clear that AI tools integration is for employees’ benefit only, and it’s not meant to spy on them and gather sensitive information.
- Create interactive and straightforward training materials for different roles that encourage employees to learn quickly, rather than breeding frustration with overly complicated terms or processes.
- Establish ownership during the AI adoption process by engaging HR leaders to address frustrations and fears, IT security teams to handle security concerns, and external AI product owners to continuously communicate the value of AI and facilitate training programs.
- Assign department managers to foster and monitor AI adoption across their teams as an additional effective solution to AI resistance.
If AI resistance persists, gather employee feedback to learn the exact workflows or processes that discourage your employees from using AI. You may need to fine-tune your solution or research new tools that would prove more effective.
To sustain AI use, measure its impact over time. It’s beneficial to the business and helps employees feel more motivated to use AI.
Measuring productivity impact to prove AI efficiency
Just as change management requires assigning a responsible person, you should do the same for AI efficiency tracking. Business intelligence and data analytics experts are the most obvious choice, but department managers should also be involved, as they’re directly responsible for employee performance and productivity.
To define where your AI productivity tools increase productivity rather than add operational burden for your teams, get back to your expectations and issues that you wanted to solve with AI in the first place. If, after a month of AI use, your marketing teams spend even more time on creating marketing campaigns, as they now need to devote time to correcting AI mistakes, then you would need to redefine your initial AI strategy.
To increase confidence in AI efficiency, compare your findings against industry benchmarks. For instance, a Microsoft study discovered baseline productivity improvements of artificial intelligence in the workplace for corporate tasks:
- 10-13% increase in document editing efficiency
- 11% decrease in email processing time
- 39% increase in response times to customers
- 25% improvement in the accuracy of processing customer requests
These benchmarks are average across industries and teams, but they demonstrate that even a 10% or 15% improvement in employee productivity is progress worth investing in AI.
But remember that AI may require some time to yield first results. Allow time for training, onboarding, and getting accustomed to the new technology. Early wins for employee productivity can be visible after one or two months, but the impact on business may take up to 12 months.
What metrics to measure
As for metrics you can track, choose qualitative and quantitative metrics that were important for defining employee productivity before the AI adoption; this way, changes will be more visible. To achieve the most reliable results, track each department’s performance separately and define core metrics for each (e.g., the number of closed deals per month for sales and the number of closed inquiries per hour for customer support).
Such general financial metrics as revenue per employee and cost per output (measured by the number of tasks completed) bridge business and productivity gains.
Measure AI efficiency on a month-over-month (MoM) basis to gather sufficient data for comprehensive analysis. It’s also crucial to collect employee feedback on how AI tools affect workload, stress, and job satisfaction. Increased morale often correlates with sustainable productivity gains.
Tracking tools and reporting formats
You can use tools like Hubstaff, ActivTrak, and Worklytics to track productivity gains. These tools collect data from your proprietary systems and communication channels to provide insights into employee performance through interactive dashboards. However, they’re subscription-based and can have limited customization capabilities.
To achieve higher customization and embed analytics into your AI pipelines, you can integrate data analytics services like Amazon SageMaker, which provides branded trend analysis reports and dashboards.
Smart BI tools, such as Tableau and Power BI, can also be your go-to options. For instance, Tableau Pulse integrates with messaging channels (Slack, Outlook) to provide reports as on-demand messages in a clear and concise format. This is particularly useful for executives who need a quick recap of business performance but don’t have time for full-fledged analytics dashboards.
Xenoss AI engineers and data analysts can set up a custom analytics environment tailored to your busy schedule, budget constraints, and data infrastructure capacity.
What is AI without human oversight? Limitations and lessons learned to bear in mind
Despite promising gains, AI tools still struggle with tasks requiring deep business context, chain-of-thought reasoning, or nuanced judgment, highlighting the persistent need for human review.
Overreliance on AI can reduce employee skills and make them reluctant to verify outputs. AI often makes confident but incorrect claims, and it’s crucial to include clear instructions in your AI use policy on the importance of fact-checking and human judgment. It’s particularly necessary in high-stakes situations, when brand reputation and security are at risk.
Support and foster human relationships at all levels, as it’s only with a human-first approach that you can yield true productivity gains.
Xenoss can help you build top AI productivity tools that value human time and effort, and create space for creativity, genuine communication, and team support. Our services strike a balance between human and business value, ensuring that AI tools enhance work culture and drive measurable business outcomes.
