“Workslop.” It’s the latest AI buzzword, and for good reason. It’s the AI-generated content that looks plausible on the surface but is riddled with errors, brand inconsistencies, and even outright fabrications.

In fact, research from BetterUp found that 40% of U.S. employees received workloads within the past month, and it costs enterprises millions that aren’t using AI appropriately.  

Generative AI is a powerful tool, but it’s not a content strategy. It can’t, on its own, create the kind of high-quality, on-brand content that builds trust and drives results. It often has the opposite effect, creating a tsunami of generic, off-brand content that does more harm than good.

So, how can you leverage the power of AI without falling victim to “workslop”? The solution is to utilize AI to monitor itself by deploying agents that ensure every piece of generative content remains accurate and on-brand.

The hidden risks of AI-generated content  

As one of the tell-tale signs of workslop suggests, AI-generated content often lacks the unique voice and tone that separates one brand from the next. Solely relying on generative AI to produce content leaves companies with a tsunami of AI-generated content that doesn’t match brand standards. As brands are under pressure to meet rising content demands, they risk sacrificing credibility and consistency in favor of using AI for efficiency. 

The risks go beyond brand guidelines. AI-generated hallucinations also produce inaccurate content that can harm a brand’s reputation. For example, Deloitte Australia recently found itself in hot water when it shared a report with the Australian government that was riddled with AI-generated errors, including a fake quote from a federal court judgement and references to nonexistent academic research papers. This incident raised concerns about the inaccuracies that can arise when using AI agents without implementing the necessary guardrails. 

To mitigate the risks associated with AI-generated content, businesses require agile AI solutions to protect their bottom line.  

The solution: Agentic AI

LLMs and generative AI models alone won’t protect your brand — but combining them with agentic AI to monitor content ensures it’s aligned, accurate and on-message. The term “agentic AI” is buzzing in every tech circle this year. According to IBM’s definition, agentic AI is an AI system that can accomplish specific goals with limited supervision.  

These AI agents are being quickly adopted as the solution to generative AI workslop, according to a recent report from G2. The data found that 57% of companies already have agents in production, with many more to follow. 

As agentic AI continues to evolve, guardian agents have emerged and are changing the game. Guardian agents are AI designed to monitor other AI. The technology has the potential to capture 10-15% of the agentic AI market by 2030. As we continue to see enterprise adoption of the technology, it’s clear that using guardian agents is key to businesses harnessing the benefits of generative AI while avoiding workslop. 

When used for monitoring content to make sure it’s on-brand, up to date and compliant, this specific type of agentic AI is called a Content Guardian Agent. Think of it as AI watching AI — making sure everything your brand puts out meets guidelines and stays reliable. It’s the secret weapon for producing quality content. 

Marketing teams have no choice to leverage generative AI to keep pace with soaring content demands, especially without adding more hands to their team. But with AI blurring the lines between speed and quality, companies need the right guardrails in place to oversee, audit, and align every piece of content produced to avoid creating more workslop. The brands that will win this new era of content will be the ones using AI not only to create content, but also to protect it. 

The future of brand protection relies on guardian agents 

The sheer volume of AI-generated content has already outpaced our ability to review it manually. Without a system for governing AI with AI, brands risk trading speed for sloppiness.

Content Guardian Agents are the answer. They’re not just a solution to “workslop” — they’re the key to unlocking the next era of content strategy.

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Travel planning has long been a tangled web of disconnected platforms, multiple apps, and endless browser tabs. Travelers juggle flight booking sites, hotel platforms, restaurant reviews, and activity planners, often losing momentum between each touchpoint. This fragmented experience creates friction for travelers and makes it harder for travel brands to build and maintain engagement. 

Now, conversational and agentic AI systems seem poised to resolve those challenges. Achieved not by adding new apps or features, but through their ability to connect every stage of the planning process into a centralized, data-driven interaction that adapts in real time to travelers’ needs. 

The end of travel planning overload 

As travelers demand more streamlined booking experiences, the next competitive edge won’t come from more apps but AI that integrates traditionally siloed functions into one continuous flow. Rather than forcing users to navigate between separate platforms, AI-powered systems handle complex requests through natural conversation. 

This shift is about more than convenience—it’s a reimagining of travel planning itself, and by extension, how travel brands interact with customers. Just as AI tools like ChatGPT and Google Gemini have changed how many people gather information and find answers to questions, AI is transforming travel planning from a scavenger hunt into a guided discovery tour.   

That means that instead of competing for app downloads, companies must focus on creating AI experiences that understand context, remember preferences, and execute complex arrangements through a single intelligent interface. Brands that master this unified approach will capture customer loyalty by becoming the single point of contact for all travel needs. 

Consumer behavior has stabilized around digital-first expectations, with travelers expecting technology to work intuitively. Companies that continue to rely on fragmented experiences risk losing customers to competitors offering more AI-powered end-to-end travel solutions.   

Data-rich interactions drive smarter recommendations 

The power of AI lies in its ability to turn data into value. By analyzing deep customer data—preferences, past behavior, and real-time context—AI systems can deliver smarter, highly personalized recommendations and decisions that drive both conversion and loyalty. Imagine a booking platform that knows, based on a variety of historical and profile data combined with browsing cues, that a traveler is planning a summer seaside vacation, and can automatically recommend a minivan rental and hotel suite large enough to accommodate their family. Wouldn’t that traveler be more likely to convert than if they had received a generic offer?   

Traditional platforms collect vast amounts of data but often fail to connect insights across touchpoints. A user might search for flights on one platform, book hotels on another, and research activities on a third, with each interaction trapped in silos. AI-powered systems change this by creating comprehensive customer profiles that inform every interaction. 

This data integration enables AI to make intelligent trade-offs impossible through conventional interfaces. For example, AI might recommend a slightly more expensive flight because it knows the traveler values shorter layovers or suggest a hotel in a different neighborhood based on known dining preferences. Each interaction makes the system smarter, creating a flywheel effect that improves the system’s ability to serve the customer and strengthens loyalty over time.  

From conversational search to autonomous trip management 

If conversational AI helps travelers search and plan trips using natural language, such as “Find me a flight to Tokyo next month with a window seat and a hotel nearby with a gym,” then agentic AI takes it further by acting on the traveler’s behalf. It can rebook flights if there are delays, suggest restaurants based on dietary needs, or adjust itineraries in real time. This shift turns AI from a simple search interface into an autonomous travel partner that makes decisions and handles logistics without needing user input. 

Travelers are primed for this next evolution. According to Statista, 40% of global travelers have already experimented with AI trip-planning tools, while 12% use AI for both planning and during the vacation. But what does this mean for established travel players? If a single AI agent handles an entire trip-planning experience, do consumers still turn to booking platforms or rely on TripAdvisor reviews? 

This means AI’s role is no longer just in searches and suggestions. It’s in booking, coordinating, and problem-solving, creating a travel experience where planning becomes collaborative rather than transactional. AI becomes the traveler’s co-pilot, handling logistics and monitoring for changes so travelers can focus on the experience, not the mundane, time-consuming details. 

Legacy brands face new competitive pressure 

As big tech giants and AI-first disruptors move deeper into travel, legacy brands face new pressure to strengthen their AI capabilities or risk losing relevance. Companies like Google bring sophisticated technology platforms, mountains of user data, and broad user familiarity that can quickly challenge established travel brands’ market positions. 

Travel brands still have an advantage based on their customer relationships, but this competitive threat requires travel companies to rethink their technology strategies. With this shift accelerating, success depends on travel companies effectively integrating data across touchpoints, investing in scalable AI systems to handle complex scenarios, or partnering strategically where internal capabilities fall short. This transition might seem daunting, but it can start simply, with an audit of existing systems and data. But the important part is to get started—the big tech challenge is not going to abate. 

The challenge is particularly acute for established travel brands with legacy systems, fragmented data architectures, and aging tech stacks. These companies must navigate a tightrope, maintaining existing operations while modernizing infrastructure to support new AI capabilities, often requiring large technology investments. 

The clock is ticking. As AI capabilities advance rapidly and customer expectations shift toward more intelligent, autonomous travel experiences, travel companies must move quickly to assess their AI readiness and develop comprehensive strategies to compete effectively in an AI-driven travel landscape. 

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Customers are no longer just browsing; they are being guided through a personalized experience, which could lead to increased sales and loyalty. At a recent business forum, a peer rightly pointed out how Customer Experience (CX) is still not the top priority for many businesses who claim to be on a digital transformation journey. Those who want to transform CX aren’t going beyond conventional channels.

AI/ML and data analytics grew beyond buzzwords and turned out to be powerful tools that have the potential to reshape the CX landscape. Over the last few decades, I’ve witnessed the transformative power of AI and data analytics in enhancing CX firsthand. In this article, let me remind you how these technologies are revolutionizing CX while sharing insights from my experiences.

Customer Expectation Paradigm

Decades ago, businesses thrived primarily on product quality and effective branding. However, today, customers don’t just expect great products or services; they demand seamless, personalized, and memorable experiences. We can observe shifting consumer behaviors, where brand loyalty now hinges on product quality and the entire consumer journey. 

This sentiment was echoed in a 2021 PwC study, which found that 73% of consumers weigh CX heavily when making purchasing decisions. Forty-two percent indicated they willingly pay a premium for superior, tailored experiences. CX isn’t just a nice-to-have anymore; it’s a competitive differentiator that can impact the bottom line.

The Power of Personalisation

During an enterprise tech conference, a case study presented by an ecommerce firm was particularly enlightening. They transformed their platform into an adaptive marketplace by leveraging AI-powered data analytics. Products weren’t just displayed but curated based on individual user behavior. The recommendation engines used by companies like Amazon and Netflix will always stay in this conversation. These algorithms leverage data analytics and Machine Learning to understand individual preferences and serve up content or products that customers will likely enjoy.

But again, personalization goes beyond product recommendations. It extends to email marketing, content curation, and even digital interfaces. Businesses can significantly improve conversion rates and customer satisfaction by delivering content and offers relevant to each customer.

Chatbots and Virtual Assistants

Another area where AI is making waves in CX is intelligent virtual assistants. Initially, the corporate world was skeptical. But with time, we’ve learned their capability and been given constant training and development.

Imagine a customer visiting your ecommerce website late at night with a question about a product. With a chatbot, that customer can get immediate assistance, enhancing their experience and potentially leading to a sale. Chatbots can handle routine inquiries, allowing human agents to focus on complex business challenges.

With a capacity to quickly learn and adapt, AI-powered chatbots improve with each interaction and become more effective over time, providing consistent brand experience.

Harnessing Predictive Analytics

Predictive analytics, a subset of data analytics, uses historical data to predict future events. In the context of CX, predictive analytics can be a game-changer. 

A recent project we’ve delivered for a telecom company proved to the client and our team how predictive analytics can identify customers at risk of churning. By recognizing early signs and running sentiment analysis, we could identify potential service drop-offs even before they occurred. This not only saves revenue but also fosters loyalty.

Even in the retail sector, predictive analytics have many success stories. Insight-driven forecasting aids inventory planning based on historical data and current market trends; retailers can ensure product availability, greatly enhancing the shopping experience.

Sentiment Analysis and Continuous Feedback Loop

AI-driven sentiment analysis has turned customer feedback into a goldmine of insights, which was otherwise an overwhelming and indecisive manual process. Marketing leaders are pleased with how Natural Language Processing (NLP) algorithms make sense of vast unstructured data swathes.

Remember Spotify’s Discover Weekly playlist? It’s like you have a new playlist every Monday curated just for you. It was an amalgamation of collaborative filtering, NLP, and Audio models. So, every business with substantial data should start dipping its toes into deep learning.

AI-enhanced Customer Journey Mapping

The customer journey is no longer a linear path. It’s a complex web of interactions across multiple touchpoints, from social media to email to in-store visits. Mapping this journey is the step to understanding how customers engage with your brand.

AI can significantly enhance customer journey mapping by analyzing data from various sources and identifying key touchpoints and moments of truth. B2C mobile apps can use AI to track user behavior, interaction patterns, and choices. Insights on user journeys can be used to create personalized recommendations and support, ultimately improving user retention.

Ethical Considerations

Like Stan Lee said, with great power comes great responsibility. AI algorithms must be fair, transparent, and respectful of user privacy. Bias in algorithms, data breaches, and invasive practices can lead to trust erosion and legal challenges.

Transparency is key. Please pay attention to the need to inform your users about how their data is being used and have the option to opt-out. The importance of regular audits, stringent data protection protocols, and clear communication with users about data usage can not be more emphasized. 

Conclusion

AI and data analytics are powerful allies in delivering exceptional CX. They enable personalization, provide round-the-clock support, predict customer needs, and make sense of vast data. However, success in this endeavor requires more than just implementing technology; it requires a commitment to ethical practices and a deep understanding of customer needs.

I’ve seen, observed, and experienced the impact AI and data analytics can have on CX. My learnings from various executions reflect the promise of these technologies and the responsibility that comes with their use. Businesses that embrace AI and data analytics with integrity and a customer-centric mindset will undoubtedly be in charge of transforming CX and setting new industry standards.

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Growth strategies that prioritize extracting value from customers are no longer effective. Instead, the key lies in maximizing value for both customers and buyers to generate value for the business. Brands need to propel growth by aligning crucial revenue-generating and technology functions around the concept of customer value.

In 2024, a year that demands bold exploration of the most influential technology since the internet—generative AI—will also witness other crucial dynamics. Economic uncertainty, declining employee engagement, and the introduction of a groundbreaking self-service developer portal are all on the horizon for the coming year.

According to Forrester, companies actively leveraging generative AI (genAI) to enhance experiences, offerings, and productivity are poised for significant growth, outpacing their competitors. 

The period from July to September 2023 witnessed a remarkable surge in enterprises transitioning from experimentation to expansion in implementing genAI, reaching an adoption rate of 71%, one of the fastest mass adoption rates for a new technology in the enterprise.

However, achieving success with genAI demands comprehensive enterprise support and the transformation of technology challenges, such as BYOAI and coherent nonsense, into opportunities. To unlock the full potential of genAI, leaders should:

• Embed Trust in the GenAI Foundation: Establishing trust as an inherent element of the enterprise’s genAI foundation is crucial for sustained success. Companies that educate their employees and customers on the intricacies of privacy and security empower users to confidently and innovatively embrace this technology.
• Focus on Practical, Measurable Use Cases: Concentrate on practical and measurable genAI use cases that involve augmenting or transforming existing products, services, or business processes. In initial prototypes, prioritize projects that are employee-facing or involve offline generation rather than real-time generation with a chatbot.
• Invest in Continuous Upskilling: Successful genAI implementation requires ongoing upskilling for both employees and leaders. This encompasses technical skills and soft skills to transparently communicate the impact of genAI and foster understanding among employees.
• Leverage Business Data for Differentiation: While foundational models are potent tools, they are accessible to everyone. To create a competitive edge, organizations should build genAI models and applications using their own business data.

Srividya Sridharan, VP and Group Research Director at Forrester, emphasized, “Generative AI has the power to be as impactful as some of the most transformative technologies of our time.” 

The widespread adoption of generative AI has reshaped customer and employee interactions and expectations, elevating AI initiatives from optional to foundational components of competitive roadmaps. Forrester’s research aims to guide business and technology leaders in utilizing genAI for a sustained competitive advantage.

Businesses will pivot around generative AI, utilizing it as the cornerstone to elevate, enable, and involve both employees and customers. Stay at the forefront by keeping abreast of Forrester’s dynamic analysis, unraveling the ways in which generative AI is set to revolutionize everything, from your business landscape to your individual role. Comprehend the associated risks and rewards to navigate this transformative technology effectively.

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Mediafly, the revenue enablement company, unveiled findings from the 2023 State of Revenue Enablement study, that surfaced behavioral and technological insights, trends, and best practices from large, global revenue teams. The survey was fielded from revenue leaders at B2B and B2B2C enterprise organizations with over 1,000 employees and more  than $500 million in revenue. It assessed the complex go-to-market challenges large enterprises face in reliably supporting tens of thousands of sellers, large volumes of transactions and data, consistency across various systems, and more.

The study found significant differences separating high-performing revenue organizations from their lower-performing peers. These include the pace at which they are experimenting with Generative AI, adoption of value-selling methodologies, automation of CRM data collection, and optimization of content. Additionally, high performers have made more aggressive shifts away from traditional sales enablement to full revenue team empowerment.

Experimenting with Generative AI 

Revenue teams have started utilizing artificial intelligence (AI) for operations and enablement. AI helps revenue organizations enhance their personalization efforts and automate their manual tasks, ultimately driving sales efficiency. 

The State of Revenue Enablement study revealed that the adoption of Generative AI is on the rise, with 59% of revenue teams either currently using or experimenting with this technology and actively consolidating their tech stacks. High-performing organizations are leading the way, being 44% more likely to embrace Generative AI solutions.  

The amount of manual work facing revenue teams is massive, and the age of AI comes at just the right time. With this technology’s ability to automate manual efforts, revenue teams can look at achieving their goals on time and with efficiency, without leading to burnout from endless cycles of data processing.  

Challenges in content management 

The report quotes that sellers are spending too much time manually personalizing content, and organizations are unable to stop sellers from sending inaccurate or obsolete collateral. Such incidents not only increase the total time spent on crafting customised content for customers, it also implies time thrown down the drain. 

As per the report, a few persistent roadblocks large organizations face with content management include the excessive time spent by sellers on content creation and personalization, outdated sales content, and the difficulty in governing content to ensure alignment with brand and regulatory requirements.

Organizations have also noted struggles with achieving the required insights to identify the most effective content and its ideal timing, and there is a common issue of sellers sending content that deviates from the established brand guidelines.

Key findings from the study also include: 

• High-performing organizations are 37% more likely to track the impact of sales content using data and insights compared to low-performers.
• High-performing organizations understand the critical importance of CRM data accuracy. They are 20% more likely to depend on CRM data that is automatically collected and uploaded, easing the burden on sellers.
• Surprisingly, only 44% of organizations leverage data-driven processes for understanding pipeline health and forecasts. This indicates untapped potential for improving data intelligence practices.
• High-performing organizations prioritize value-selling methodologies, with 19% more likely to have integrated these approaches into their strategies. Additionally, they are 38% more inclined to use business value assessments in their sales cycles.
• A substantial 70% of respondents expressed a strong preference for vendors offering a single source of truth for revenue data, noting the growing importance of data consistency and reliability.

Times are tough, and revenue teams are under constant pressure to get a lot done in significantly short tenures. At such a time, disruptive technologies like Generative AI will be the go-to technology for revenue organizations to rise from the challenges and move forward towards enhanced results. The winning organizations are emphasizing investments in technology for sales, with a focus on content for influence. Companies that utilize Generative AI for immediate and future impact will survive in the long run.

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Artificial intelligence is woven into the fabric of modern life. It decides which tweets appear on your Twitter feed and recommends watching YouTube videos. AI is involved when Google adjusts your route to follow the fastest way home or shows an ad for the Bluetooth headphones you’ve been dreaming about. 

The popularity of AI-powered voice assistants like Alexa, Siri, and Google Assistant means people today are more comfortable interacting with human-like computers. PSFK research found that 74% of people prefer using chatbots to answer simple questions. However, chatbots still have a bad reputation, especially regarding customer support. We’ve all been burned by frustrating bot experiences where you’re stuck in a loop and can’t get the help you need. 

Many businesses turn to customer service automation to reduce costs and increase efficiency. However, conversational AI technology advances mean support bots can do more than basic deflection. AI-powered chatbots can handle complex conversations that deliver experiences that feel empathic and human. The key is to keep customers at the center of every automated conversation.

Also Read: Generative AI Differentiates High-performing Revenue Organizations

Why human-centered conversation design matters

Accenture predicts that 95% of customer interactions will be AI-enabled by 2025. And while customers are happy to use a chatbot to get the answers they need (particularly if it’ll help solve their issue faster), they’re not willing to sacrifice superior CX.

Support teams at the most customer-centric companies know that customers’ expectations are higher than ever, and they’re willing to look elsewhere if businesses miss the mark. Fifty percent of customers will seek out a competitor after a single bad experience. This means brands must be sure their bots can deliver the same joyful CX as their human agents.

Here are three ways human-centered automation can help companies deliver outstanding CX (and the business benefits you’ll feel).

Start with empathy

Every support ticket has a story. That’s why when customers reach out to support teams, they’re not just looking to have their questions answered. They want connection and reassurance—as well as fast, accurate resolutions.

Hans van Dam is the CEO and Co-founder of the Conversation Design Institute, a leading organization that offers world-class training in conversation design skills. Hans explains that when customers ask for support unlocking their bank account, they might also say they couldn’t pay rent or buy groceries. “We need to recognize that as creators; otherwise, we just build cold machines.”

Also Read: You Need To Unlock GenAI’s Full Potential For Business Growth

Human-centered design starts with empathy, and empathy comes from understanding. So before your team builds any automated dialogue flows, get to know your customers: how they speak, what’s important to them, why they reach out to your brand, and the channels they use.

Truly understanding your customers—and their pain points—is essential to delivering empathic automated experiences. Once you start designing conversations, make sure your customers feel heard and understood. Have your bot repeat a customer’s concern (“I’m sorry you’re having trouble accessing your account”) and show that you care (“that must be frustrating”).

Your bottom line will thank you, too. By creating AI-enabled interactions that keep customers coming back repeatedly, you’ll drive loyalty and grow revenue.

Develop a learner mindset

One of the key principles of human-centered design is being open to learning. So, only assume you know all the answers after validating the data.

Before starting to automate, read past support conversations to find out the most common questions your customers are asking. The best automation providers can leverage AI to analyze this historical support data to determine what your customers ask most often. Many CS teams will be surprised that their existing FAQ page doesn’t offer customers the answers they want.

Most conversational AI platforms also provide analytics and reporting dashboards. These allow you to measure key metrics like bot handled rate (the number of tickets resolved without a human agent’s involvement), message understood rate (which tracks the accuracy of your bot’s responses), and your top intents (the questions customers are asking most often). With this data, you can train your bot to become more accurate, deliver more personalized CX, and drive customer loyalty.

Test, test, and keep testing

Human-centered automation shouldn’t be a set-and-forget system. To deliver the best experience for customers, you have to keep iterating.

Before you launch a new AI-powered bot or conversation flow, make sure you test it thoroughly. Are there any loops that customers could get trapped in? Is the choice of language on brand? Have you included smart escalation paths?

Also Read: How AI and Data Analytics Are Transforming Customer Experience

After launching, make sure there are opportunities for customers to give feedback. Keep training your bot. The beauty of AI is that every automated interaction will improve over time as the AI model learns and develops. This means customers receive more accurate support that instantly solves their issues, resulting in a better experience.

To sum up

When designing AI-enabled experiences, it’s essential to remember why automation was created first: to take over repetitive tasks and give people time to focus on what truly matters. 

Human-centered design is all about customer-centricity. So using this framework when creating automated interactions ensures peoples’ (specifically, customers’) needs aren’t sidelined by efficiency and cost-cutting concerns. By taking a human-centered approach to automation, you’ll deliver faster, better CX and provide customers with the connection they crave.

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