Employee Chatbot Use: How to Guide Workplace AI Effectively

Published

5 days ago

September 6, 2025

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Employees increasingly adopt conversational chatbots at the workplace. Yet without strategic oversight, such usage might default to shallow, efficiency-driven usage, undermining the opportunities for personal and organizational growth. Therefore, leaders should intentionally shape an AI use culture that facilitates human flourishing and organizational innovation. Here’s how to achieve it.

Introduction

The rise of advanced AI, and conversational chatbots in particular, has created an urgent leadership challenge that most executives are overlooking. How your employees use chatbots isn’t just their individual choice – it has broader organizational implications. Collective chatbot usage patterns are shaping what I call an organizational “AI use culture.”

Because chatbots are cheap, fast, and accessible, many employees adopt them independently to accomplish work tasks. This means AI use culture will emerge whether you intentionally shape it or not. The key difference is that by intentionally guiding it, you can define how it unfolds. If you ignore it, the culture will likely default to shallow, efficiency-driven uses, like obtaining ready-made ideas, copy-pasting drafts, and outsourcing critical thinking. These approaches feel productive in the moment but gradually undermine the learning and creativity of employees that drive long-term organizational growth.

To avoid this prospect, organizational leaders should actively cultivate a more human-centered AI use culture that balances efficiency with learning, creativity, and collaboration.

Shaping effective AI use culture

Shaping effective AI use culture: A practical guide

Human-centered AI use at the workplace means that chatbots are used in ways that enhance key human flourishing factors and facilitate the fulfillment of professional needs, not undermine them. This doesn’t mean chatbots should be avoided or prohibited. Instead, it requires a shared understanding of how AI should be used and why those choices matter.

As a leader, you can’t afford silence when it comes to chatbots. Your employees are probably already using them, the only question is how exactly. Acknowledge this reality explicitly, and if possible, conduct interviews or cross-department surveys to reveal the general chatbot use patterns. Based on the obtained insights, you will clearly see whether the tendencies for chatbot usage are aligned with human development or are simply outsourcing strategies.

For example, looking at the data, you can ask yourself: Are these usage patterns aligned with a need for professional growth? Does this contribute to skill development? Does this enhance mastery, autonomy, and creativity of employees? If everyone in the company uses chatbots like this, will we have a strong human potential and innovation over the long term? If you find any red flags, it can be a sign to intervene with the following strategies.

1. Establish the “sandwich approach”

One of the most effective methods for preserving human agency while leveraging AI capabilities is what can be called the “chatbot sandwich rule.” Within this method, an employee generates “raw material” first, that is, writing a draft, developing initial ideas, creating a presentation structure, designing a pipeline, whatever their work requires (the bottom layer). Then they use a chatbot for feedback, critical evaluation, and reflection on their ideas (the middle layer). Finally, they rewrite or redesign based on that feedback (the top layer).

For example, before presenting an idea to a project leader, an employee might run through several rounds of critical revision with a chatbot, using it to identify weaknesses, explore alternatives, and strengthen their argument. This approach preserves learning and authenticity while potentially saving time and improving quality.

2. Position AI as an intellectual sparring partner

Instead of asking “Write this for me,” employees should learn to prompt chatbots with “Challenge this idea,” “What am I missing here?” or “How could this approach fail?” Chatbots excel as question-askers and can help employees get to the right answers on their own, thereby learning the pathway to a solution and solving it independently next time. Encourage employees to use chatbots as sparring partners or performance coaches that help define goals, challenge assumptions, and evaluate ideas from multiple angles. This transforms AI from a content-generation tool into a thinking enhancement tool.

3. Develop AI literacy as a core competency

Your employees need skills to evaluate AI outputs critically, understanding potential biases, limitations, and gaps. Train them to ask probing questions: What assumptions are built into this analysis? Where might this information be incomplete? How does this align with our specific organizational context?

4. Balance AI and human collaboration

Some of the best organizational thinking emerges from human discussions where different perspectives result in unexpected connections. Regular human check-ins serve multiple purposes: they reality-test AI-assisted work, ensuring it remains grounded in practical constraints. They bring contextual knowledge, emotional intelligence, and diverse experience that AI cannot replicate. And they challenge assumptions based on real-world implementation experience and insights rooted in organizational culture and politics.

5. Avoid AI creativity trap

To complete this guide: here’s something that every executive should reflect on: if your employees use chatbots the same way as your competitors’ employees, which basically means generic question-answer sessions, quick rewrites, standard brainstorming, how likely is it that your company will be much more creative to innovate your way past competitors?

Research suggests that when organizations rely on similar AI strategies, their outputs might begin to converge toward similar ideas and structures. This convergence isn’t immediately obvious because each company’s outputs appear unique in isolation. But zoom out, and you’ll see troubling patterns of similarity.

Therefore, you should promote non-conventional, creative chatbot use cases. For example, train your project managers to use chatbots as a harsh critic, systematically exploring how initiatives could fail before they launch. Encourage your training departments to use AI as a “learning coach,” helping employees create personalized development pathways. Or ask your HR managers to analyse qualitative survey data with chatbots to reveal implicit information they might be overlooking.

Final thoughts

Your organization’s AI use culture is forming right now, shaped by hundreds of daily interactions between your employees and chatbots. You can either let this happen by default, risking a workforce that becomes dependent rather than empowered, or you can actively cultivate an approach that enhances human capabilities while leveraging AI’s strengths.

The choice you make will determine how adaptive, creative, and innovative it remains as AI continues evolving. In a world where everyone has access to the same powerful AI tools, your competitive advantage won’t come from the technology itself. Rather, it will come from how thoughtfully your people use it.

About the Author

Nick Kabrel is a research associate at the University of Zurich and a Digital Society Initiative Excellence Fellow. His research focuses on organizational behavior and human-centered AI at the workplace.

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Business

Bedford tech company expands with an eye on AI

Published

1 hour ago

September 11, 2025

Paul Briand

Artificial intelligence (AI) is an important subject to Nick Soggu, CEO of SilverTech, a Bedford-based digital experience agency.

AI is near and dear to Soggu not only as SilverTech expands its business pursuits with the recent purchase of another tech company, Paragon, but also personally to Soggu as he works toward a doctorate degree in AI and machine learning.

AI has far-reaching consequences for business and for society as a whole, according to Soggu.

“The ethics and the threat side of things is very real. I think as these reasoning models are being created and now start to get more advanced, they do take on interesting types of personalities,” Soggu said.

“These models are getting to that level of thinking and cognizant behavior capabilities, where they’re starting to do more than regurgitate facts and provide the next best word fit,” he added.

Enhancing the use of AI was cited as one of the benefits cited in SilverTech’s recent purchase of Paragon, which is based in Ohio and brings to the table a broad client list that deepens SilverTech’s portfolio.

SilverTech’s headcount, as a result of the purchase, grew from around 85 to about 150, making it “uniquely positioned to lead innovation and drive transformative growth in the tech sector,” according to the statement that announced the purchase July 24.

Soggu said there were two primary reasons for the Paragon purchase: one technical, one strategic.

The technical part has to do with what Soggu called “digital experience products,” otherwise known as content management systems (CMS products such as WordPress) or digital experience platforms (DXPs).

“Paragon happens to be in a market providing the same types of services we do, but they’re working with a whole different set of these digital experience solutions. And so what you have is the best of both worlds when you combine the two firms,” said Soggu.

Strategically, said Soggu, Paragon had clients in certain areas that SilverTech did not.

“They work with just like we do with a variety of brands that are different than we are, in verticals that are different than we are. And at the end of the day, it’s a diversification strategy in the client concentration,” said Soggu. “They don’t work with a lot of banks and credit unions that we do, as an example, but they have a lot of direct-to-consumer type brands like Ugg and brands of that nature that they’re providing services for. And so it expands our customer hopeful portfolio as well.”

The AI component adds some horsepower to the organization, according to Soggu.

“The future is really about what we’re going to be doing together using artificial intelligence and machine learning technologies for our client base, and the combined horsepower, from an engineering perspective, that we’re able to bring to the table,” said Soggu.

According to the company, the combined strength of the companies offers the following benefits:

Scale the delivery of AI-powered personalization, predictive analytics and intelligent search.

Accelerate innovation in machine learning, conversational interfaces and process automation.

Deepen customer research and deliver digital strategy that is aligned with business strategy.

Expand CMS and DXP platform expertise with top-tier technology partnerships and solutions, and is certified and well credentialed in Sitecore, Progress Sitefinity, Kentico, Hubspot, Contentstack, Contentful, Salesforce, Sanity and Big Commerce.

“This acquisition brings together Paragon’s deep enterprise experience and consulting expertise with SilverTech’s strengths in digital marketing, media and managed services,” said Jeff McPherson, chief growth officer at SilverTech. “Together, we are expanding our ability to serve a broader range of markets with a powerful mix of strategic insight and technical innovation — helping clients harness data, personalize experiences, improve decision-making and drive automation.”

In that growing use of AI, there are threats and opportunities, as Soggu has studied both as a tech executive and PhD student.

“The world of deep fakes and the world of creating content that looks and feels like human created content — and it’s very compelling content — that’s a dangerous and slippery slope,” said Soggu. “I think there’s a lot, from an ethics perspective, that’s yet to be determined, things that we should be certainly concerned about.”

At the same time, according to Soggu, AI can produce efficiencies that ultimately benefit a business.

“The opportunities are very compelling in terms of efficiency gains, in terms of work output gains,” he said. “Large corporations are seeing upwards of 30% to 50% gains in efficiency and work output.”

In his work, Soggu said AI can be applied to some quality assurance tasks.

“We can put a website through its paces using AI tools and technologies to do user testing much more efficiently and effectively than we can with five or six humans involved in that process,” Soggu said.

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Business

Why Trusted Data Is Key to Transformational AI-Driven CX

Published

2 hours ago

September 11, 2025

Jessica Keehn

From boardrooms to shop floors, companies are moving quickly to embed AI into their operations. The goals are clear: drive efficiencies, reduce costs, and deliver smarter, faster, more personal customer experiences.

This makes a lot of sense given that today 89% of businesses are expected to compete primarily on CX. However, the results aren’t always matching the hype.

A recent Gartner study found that while enterprise AI adoption is rising, real impact is often elusive. The reason? Many businesses are still operating with disconnected systems and disjointed data. Without a strong foundation, AI can’t deliver what it promises.

Siloed systems aren’t just a technology problem—they’re a business barrier.

The CX Disconnect: When Fragmentation Undermines Intelligence

Too many organizations still rely on a patchwork of tools for customer experience, supply chain, finance, and HR. While these point solutions solve individual challenges, they create friction and disconnect across the business. In an AI-powered world, friction is the enemy.

AI thrives on complete, clean, and contextualized data to function effectively. If your marketing, sales, service, and fulfillment teams cannot see the same data in real time, or trust that it’s accurate, your AI strategy will not be set up to succeed.

With the best intentions to embrace AI in an effort to achieve incredible efficiency, instead, customers will still lose valuable time on manual integration, inconsistent customer experiences, and AI outputs that are only as good as the (fragmented) data feeding them. The delightful experience aspirations turn into trust lost and frustration all around.

Modular Innovation, Meet Enterprise Intelligence

SAP has reimagined enterprise management with SAP Business Suite, representing a fundamental shift from traditional ERP systems to a modular, composable architecture that integrates AI, data, and applications into a unified platform.

Grounded in harmonized, semantically rich data, this architecture allows businesses to make sense of data that has traditionally been scattered across systems and trapped in silos, so AI has the comprehensive data it needs to quickly generate meaningful insights.

SAP Business Data Cloud (SAP BDC) with native integration of SAP Databricks, serves as a data backbone for business AI. It seamlessly connects all SAP data and third-party data and provides integrated governance to enable real-time AI-driven decision making.

Companies do not lose precious time locating and preparing data for AI. AI systems work on trusted, contextualized data, not just generic data. This produces accurate, reliable, and actionable AI recommendations that enable organizations to scale AI innovation rapidly across business domains.

SAP BDC is the foundation for Joule, SAP’s AI copilot that acts as an intelligent orchestrator across the entire business suite. SAP BDC ensures that Joule has structured business context for natural language processing and that its outputs are accurate so that Joule can provide always-on assistance to break down silos between business operations.

For example, when a customer service or sales representative handles a complex order issue, Joule can:

Check real-time supply chain constraints

Respond to RFPs faster
Personalize the response by pulling in relevant customer history from CRM systems
Speed response with automated case routing and research

The results are faster resolutions, happier customers, empowered employees, and incredible business outcomes with less effort and overhead.

CX + AI + ERP = Real Results

Integrating CX AI with core ERP systems enables end-to-end process optimization that was previously impossible with fragmented systems. When CX systems connect natively to back-office systems, organizations gain:

Real-time personalization powered by operational data
Intelligent workflows that prioritize high-value customers
Predictive insights that help teams act before issues arise

The numbers speak for themselves. According to an Enterprise Strategy Group report, customers using this approach reported these benefits:

Up to 60% reduction in the number of issues service and support teams deal with due to fewer manual errors, automated self-service support functions, automated self-service, and AI chatbots
25% to 50% improvement in time to resolution for issues that did require service or support resources
25% to 70% improvement in productivity of digital marketing and customer operations teams
50% to 90% improvements in sales team productivity by offloading smaller transactional sales, faster quote generation, and streamlined order management
20% to 40% increase in productivity of business operations due to less time spent on invoices, payments, shipments, and returns and more informed decision-making

This is not just incremental change; it’s enterprise transformation, driven by customer needs and powered by AI.

The Future of Intelligent Enterprise Operations

Embedded CX AI within a composable business suite represents a bright future that takes the possibility of AI and makes it a reality.

Businesses can seamlessly orchestrate intelligence across all functions, delivering experiences that feel effortless to customers while optimizing operations behind the scenes.
Artificial intelligence won’t just automate individual tasks, but also orchestrate entire business ecosystems to deliver superior outcomes.
Maintaining enterprise-grade reliability and enabling modular innovation will allow organizations to adapt to changing market conditions while creating competitive advantages.

With the rise of AI, businesses face a pivotal moment in time. Taking advantage of all that technology has to offer demands more than point solutions and departmental optimizations; it requires unified platforms, complete clean underlying data, and a clear unified strategy.

Jessica Keehn is chief marketing officer of SAP Customer Experience.

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Business

AI Stocks to Watch, According to Fund Manager Crushing the S&P 500

Published

4 hours ago

September 11, 2025

Max Adams

Before his portfolio management days, Denny Fish worked as a sales manager at Oracle. He saw the incredible demand for its software products as the dot-com boom flourished, an experience that would later inform his mindset as an investor: What was the next revolutionary idea he could get ahead of?

“I watched the internet boom, I had a front row seat because I was at Oracle and we were in the eye of the storm,” Fish said. “So it shaped my investment philosophy of, ‘Wow, always be looking for that big idea.'”

He continued: “Because the big idea is gonna express itself in a way that nobody can appreciate over multiple years and when you get behind that big idea, don’t let anybody shake you out of it because that’s what’s called a power law in technology investing.”

Two decades later, Fish was perfectly positioned for the AI boom. As a co-manager of the Janus Henderson Global Technology and Innovation Fund (JAGTX), his top holdings are a who’s who in the AI ecosystem: Nvidia, Microsoft, Taiwan Semiconductor, and Broadcom. Those four names alone, all of which he’s held for more than a year and a half, make up 42% of JAGTX.

The impressive lineup has led to a banner few years for Fish. Since the October 2022 lows, his fund is up 136%, crushing the S&P 500’s 81% surge.

Today, Fish still thinks AI is the big idea to get behind. But when asked which stocks he’s most bullish on right now, three out of the four were companies outside his top six holdings in the 25-stock fund.

4 stocks Fish likes right now

The first firm Fish listed — and the one that is among his largest holdings — is Taiwan Semiconductor, as chip demand remains uber-strong. It’s the fund’s third-largest holding at 9.49%.

“If you’re a Broadcom or if you’re Nvidia, there’s only one place you want to go to get your chips manufactured, and that’s TSMC given their process, know-how, and the lead that they’ve created,” he said.

Next, he said Cadence (CDNS), an electronic design automation firm, is well-positioned for continued AI hardware demand. The stock is the fund’s eighth-biggest holding at 2.47%.

“It’s a global duopoly,” he said of Cadence and its competitor, Synopsis. “They have dominant market positions, incredible returns on capital, and there are businesses that you can’t move forward with chip design without one of those two companies.”

Third, Fish is bullish on KLA (KLAC), which produces process control systems for semiconductor chips. The firm “has a very dominant position globally, in that swim lane for, for semiconductor capital equipment,” Fish said.

At 1.88%, it’s JAGTX’s thirteenth-largest holding.

Finally, Fish mentioned Mercado Libre (MELI), a Latin-American e-commerce platform with a fintech business that offers digital wallets, lending, payment solutions, and money transfers. Fish said he’s impressed with the company’s use of AI.

“They’re doing really unique things with AI through their entire portfolio to improve the customer experience and also improve their underwriting and their fintech business,” he said.

Mercado Libre is the fund’s seventh-largest holding at a 2.64% weighting.

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