1. What role does predictive AI play in anticipating and resolving customer pain points before they escalate?

Predictive AI plays a crucial role in identifying potential customer pain points by analyzing behavioral clues and feedback data in real-time. For example, Qualtrics Digital Experience Analytics uses indicators like rage-clicking to spot issues before they escalate, enabling organizations to intervene proactively and enhance the customer experience. 

Another great example is ServiceNow, a Qualtrics customer, which uses real-time insights from over 24 survey programs to inform their customer journeys. By proactively recommending easy-to-consume content that matters most to our customers and meets them where they are, ServiceNow is creating a unified, personalized, and guided digital experience to help our customers get to value fast. Additionally, ServiceNow is continually reimagining its customer journey to ensure that customers are connected to the right resources and partners at every stage, ultimately driving success on their platform.

2. What are the biggest challenges businesses face in turning customer feedback into actionable insights, and how does AI address them?

Businesses face several challenges in turning customer feedback into actionable insights, primarily due to the volume and variety of data they receive from multiple channels. This can be overwhelming to process manually. AI helps address these challenges by automating the analysis, categorizing, and summarizing the data to highlight key themes and sentiments efficiently. Another significant challenge is dealing with unstructured feedback, which can be complex, as it often includes text, voice, and other forms of data. AI-powered text analytics and natural language processing (NLP) can convert this unstructured feedback into structured insights, revealing the emotions and intentions behind customer comments.

Timeliness is crucial as well, with businesses needing to respond quickly to feedback in today's fast-paced environment. AI facilitates real-time processing and analysis, allowing companies to swiftly gain insights and make informed decisions to enhance customer experiences.

Qualtrics addresses these challenges head-on with its suite of advanced AI capabilities, including newly launched features like conversational feedback and established strengths in conversation analytics. With conversational feedback, businesses can seamlessly engage with customers across various channels, capturing rich insights through natural interactions. This real-time engagement allows companies to respond more dynamically to customer needs.

More than 50 brands are already using Conversational Feedback and are doubling the feedback collected, with 90% of survey respondents opting to answer follow-up questions when prompted. 

3. How can AI-driven CX solutions help businesses measure and optimize customer loyalty and retention?

AI-driven customer experience (CX) solutions, such as Qualtrics' Location Experience Hub, offer businesses unparalleled real-time insights into customer interactions and experiences, analyzed down to the individual store level. These solutions empower businesses to swiftly identify and address trends, enabling rapid responses that enhance customer loyalty and retention. By delivering granular insights, businesses can make informed decisions that significantly improve the overall customer experience and solidify customer relationships.

A prime example of leveraging AI-driven CX solutions is KFC's global omnichannel experience management program. By collecting both structured and unstructured feedback from sources such as in-store transactions, online surveys, and delivery platforms, KFC has seen a 300% increase in customer feedback. This influx of valuable insights equips team members with the information needed to refine and improve the customer experience continuously.

Qualtrics enhances this process through customized dashboards that deliver insights tailored to employees based on roles and locations. Feedback from various channels is aggregated and instantly analyzed, highlighting issues that require immediate attention. For example, restaurant managers receive specific feedback pertinent to their location, allowing them to make meaningful changes, while market managers and executives access broader insights related to larger business units. This comprehensive approach not only boosts customer retention but also cultivates a more engaged and proactive workforce dedicated to delivering exceptional experiences.

4. How does Qualtrics’ AI improve sentiment analysis and voice-of-customer (VoC) programs?

Qualtrics’ AI-enhanced tools, such as Insights Explorer and Assist for CX, significantly improve sentiment analysis and VoC programs by analyzing both structured and unstructured feedback to provide a comprehensive view of customer sentiment. Users can easily access insights without needing a background in data analytics; they can pose straightforward questions like, “What are the top three customer complaints affecting loyalty?” or “What themes are emerging from recent feedback?”

For example, Qualtrics Assist quickly surfaces relevant insights and offers informed recommendations based on expert methodologies and industry benchmarks. This accessibility allows employees at all levels to understand customer sentiments and act on them effectively.

5. What are the best practices for organizations to integrate AI-powered CX tools without overwhelming existing teams?

Organizations need omnichannel listening and comprehensive customer journey data to ensure a holistic understanding of customer behaviors and preferences. By capturing insights across multiple touchpoints, companies can better inform their AI strategies, allowing for more personalized interactions and proactive responses to customer needs. This cohesive data foundation not only enhances AI effectiveness but also enables organizations to create seamless and engaging experiences that drive customer satisfaction and loyalty.

To successfully integrate AI-powered CX tools, organizations should adopt a centralized strategy rather than running disparate programs. This ensures a cohesive approach to AI that aligns with overall business objectives. While 89% of executives report having at least one AI initiative, only 12% have a comprehensive strategy in place.

Market leaders are notably more successful, being 2.3 times more likely to take a strategic approach. Key actions to realize the value of AI in customer experience include setting clear AI ambitions, establishing guidelines for responsible use, creating a strong technology and data foundation, and designing a governance team to oversee implementation. Companies should also focus on launching high-impact use cases to build momentum, developing employee training strategies, and fostering a culture that embraces AI as a core driver of customer experience.

6. How can businesses measure the ROI of AI-powered CX enhancements and ensure they are driving real value?

According to new research, almost half of executives (42%) anticipate seeing a significant measurable impact from using AI to improve experiences within two years, with another 42% expecting results within three to five years. There is huge business incentive to do this – Organizations stand to gain an estimated $1.3 trillion by using AI to improve the experiences they deliver to customers.

To measure ROI effectively, businesses need to establish a clear AI ambition and value strategy that outlines where to invest in AI initiatives.

Key performance indicators should be defined upfront, along with risk and ethics guidelines for responsible AI use. By creating a solid data foundation and implementing AI-related governance, companies can track their progress and outcomes more effectively. This organized approach not only helps ensure that AI-driven improvements translate into real, measurable value but also facilitates ongoing evaluation and refinement of AI initiatives to align with strategic goals.

1. What role does conversational AI play in transforming how users interact with digital platforms?

The way customers discover your brand or your products and services is very different when it's done through conversational AI. The results are very specific.

For the last twenty years or so, customers typed a search query in Google, and that required them to chase down various links.

But now with conversational AI, you ask a question, and you get a pretty direct answer. Or you can refine your request in far more natural ways. It isn’t natural to have to ask for things like [jaguar speed -animal] , but it’s really easy to conversationally look for the exact information you are seeking just like you would with a person.

Even better, if you use these tools frequently, it starts to learn about you. It starts to know your preferences and provide even better answers tailored for you.

2. What are the key technical advancements behind Yext Scout’s AI-powered search?

As a marketer, AI-powered search is a black box. There is no dashboard to update your information. There are no metrics to measure your performance. So how does a Marketer survive in this new world?

AI Search and their LLM’s still need the web. The web and its data are the fuel that they run on. All of the information the LLM is sharing with your customers comes from the web.

With Yext, we are connected to more web publishers than any other solution in the market. We help brands manage their reviews and social presence across the web. We help brands firmly establish their 1st party websites as a source of truth. We do this really well.

And so what we can see now, and map out with Scout, is that your AI strategy is your Digital Presence strategy. We can show a marketer where their information is being picked up from and give them real actionable recommendations on how to improve their visibility.

3. What are the key benefits of integrating AI-driven search solutions into digital platforms?

AI search is still somewhat in its infancy. You know, there is the Mom test. Does my Mom use SearchGPT, Perplexity, Gemini, or Copilot? No, not yet. But do I think she is far off? No, not at all.

This technology is getting baked into our everyday technology. My Mom has an iPhone. She uses Siri. Apple is baking OpenAI into their devices through Apple Intelligence - they are still honing it - but we are really close to crossing the chasm in my opinion. Taking advantage of agentic AI technology like Scout is how brands can put their best foot forward — or they risk getting left behind.

4. How does Yext Scout enhance user experience by understanding search intent?

Scout is really meant to be your AI search and competitive intelligence agent. It's for anyone looking to measure and optimize brand visibility and sentiment across both traditional and AI-driven search. Scout delivers a unified platform that delivers deep search insights, competitive benchmarking, and actionable recommendations, and all with a seamless execution in one place.

Unlike legacy SEO tools that only focus on Google and other traditional digital networks, Scout uniquely tracks AI-driven search presence, sentiments, and share of voice, and provides prioritized recommendations that can be deployed seamlessly from within the Yext platform.

5. What are the security and compliance considerations when adopting AI-driven search technologies?

LLMs and AI search agents are trained on everything that's found across the web. They crawl listings, reviews, first-party web content, social profiles, and more. Scout is helping you to know how you are already appearing in the public domain.

Which comes to the point: more than ever, it's important to have accurate, clear, and consistent data across your entire digital presence.

To illustrate further: with SearchGPT, there could be a small listings website that your brand is mentioned on — but that you're not active on, and the information there might be out of date. In AI search, this information could still get pulled up as a citation and thus give outdated or incorrect information to your customers.

It's incredibly important to have a tool like Scout and Yext with a knowledge graph that can take all of your data, manage it in a clean, consistent way, and push that information out in an accurate, consistent, and manageable way across the entire web. That gives you the best chance to show up in AI search.

6. What types of organizations can benefit most from Yext Scout’s capabilities?

Right now we have honed Scout to really help out multi-location businesses. Think of large franchises across verticals like Food & Beverage, Retail, Financial Services or Healthcare.

In Scout, we provide a map-like overview so these brands can really quickly see at a glance which locations are performing well, and where they can benefit from our recommendations. We provide all of the data to help them identify the problem spots, but with our deep data science expertise and massive depth of data, we can find the trends and opportunities that would be impossible for any one brand to find on their own.

1. How do you balance fraud prevention with customer experience to reduce false declines?

False declines are a costly problem for merchants. They not only lose the order value, merchants incur sunk acquisition costs and risk damaging their reputation with each false decline. It's also a negative experience for customers: 40% do not return to a merchant after a false decline, resulting in lost future business. The challenge lies in identifying orders that are statistically risky but don't fit clear fraud patterns. Merchants want to prevent fraud, but some of these orders are legitimate but flagged as false positives.

The key is balancing a seamless checkout experience for legitimate customers with robust fraud prevention. Requesting additional verification for every order might prevent fraud but increase cart abandonment. A balanced approach uses verification only when necessary and employs sophisticated fraud detection.

Adaptive Checkout addresses this challenge by tailoring each checkout flow to the order's risk profile. This minimizes friction for low-risk orders and requests additional verification only when needed.

The process begins by filtering out blatant fraud before authorization and enriching orders with additional data, making it easier for banks to identify and approve legitimate customers. By surgically analyzing each order's risk, even riskier orders have a better chance of approval, turning potential false declines into approved transactions. This selective verification approach is crucial for improving conversion rates without sacrificing fraud protection or positive checkout experiences for legitimate customers.

2. How do you integrate machine learning and behavioral analytics to identify fraud patterns? 

The power of machine learning is the ability for it to ‘learn’ independently as it runs. There are various ways to implement this, but we believe the best approach is a layered one. 

It starts with having vast amounts of quality data. Riskified trains its machine learning models on hundreds of millions of data touch points from across our global merchant network. Our algorithms learn to distinguish safe behavior from suspicious patterns and stop existing fraudulent behaviors. But to truly stay ahead of emerging fraud tactics, we also apply real-time anomaly detection using unsupervised machine learning, which flags unusual behavior based on combinations of various order characteristics — such as many orders suddenly exhibiting copy-pasting of credit card details alongside proxy use. Our engine can detect it and stop fraud  MOs before they spread. 

Machine learning also adapts the checkout flow for every transaction, surgically applying additional security measures for select higher-risk orders and enabling merchants to confidently approve more genuine orders while blocking fraud at various stages of the process.

3. What impact does AI-powered fraud prevention have on cart abandonment rates and conversion optimization? 

Cart abandonment has many causes, but a long or overly complicated checkout process is a major one. Consider an order that initially appears suspicious, such as one placed from a new device or with a recently issued credit card. Using AI and Riskified’s merchant network data, we can compare an order against millions of data touchpoints to draw a clear picture of a shopper’s true identity. This allows us to identify legitimate customers and expedite their checkout. Conversely, imagine a returning customer with a stored credit card and no unusual activity in their order data.Why ask them for a CVV if there is no need? Some customers may not have this information readily available, potentially leading to cart abandonment. AI enables us to precisely request verification, such as a CVV or a one-time passcode, only when needed. This reduces checkout friction and increases successful conversions of legitimate orders.

4. What best practices should businesses follow when implementing AI-driven payment security solutions? 

It’s important to think of fraud prevention and conversion as two sides of the same coin. Make sure that whatever solution you’re assessing for payment security also helps to optimize conversions and prevent the false declines.

Make sure to connect with other stakeholders to understand the full scope of security issues related to payments. For example, does the payment security solution address post-purchase concerns like returns and policy abuse? Is there a need to protect against abusive behaviors like serial returns or false claims of INR? To empower merchant fraud and customer service teams with real-time ability to address these challenges, ideally, the fewer integrations you have to deal with, the better. 

Lastly, look for more than a solution - look for a partner that future-proofs your business. AI and ML solutions should never be a black box - merchant teams need technology that provide them with the visibility, flexibility, and control they need to tailor solutions aligned with their business strategy and success.  

For example, when you have declines, do you understand why they were declined? Can you add your own logic into the decisioning? How can you get a clearer image of the customer’s identity? Mapping this out can help you choose the right solution and make decisions that will improve revenue and security in the long run. 

5. How does AI-powered checkout impact payment authorization rates across different industries? 

Machine learning-based solutions can detect far more patterns than a rules-based solution – and unlike static rules, they can adapt and learn in real time. 

Using a sophisticated AI-powered solution that detects and screens out fraud prior to issuer authorization ensures that fewer fraudulent orders reach the issuer. Elite fraud prevention solutions are able to analyze high- quality data and share, that data with issuers at scale. This helps them filter out fraud while understanding the context behind orders better. For example, there can be a significant difference between the risk of buying a $1000 fridge online versus buying a $100 gift card at the same merchant. AI solutions provide the context that distinguishes between safe and risky orders. In the long run, this will lead to more trust with the issuer, higher authorization rates, and less risk of falling into a monitoring program.

6. How can AI-powered fraud prevention solutions be customized to meet specific business needs? 

The ideal AI-powered solution should be very customizable. No two businesses are alike, no one can decide your risk tolerance, and no one knows exactly how your business operates – so generic products won't cut it. 

Start by making sure fraud is viewed properly. Alongside the standard ML models, Riskified has developedgeo-specific, vertical-specific and sometimes even merchant-specific models, all of which is crucial for accuracy. 

Also, when you review transactions for fraud, do you want to review them after authorization, where you can manually overturn declines and leverage additional data like AVS match in the US? Do you prefer to review orders pre-authorizatio? Or maybe leverage both pre- and post-auth review?

For example, policy abuse is extremely specific to the merchant and their defined policies, so having ML to determine risk is an elemental part of the equation. And each merchant will want to deal with policy abusers differently – warning them, blocking them at checkout, denying claims, etc. 

Even in CNP fraud, how strict do you want to be? If you want to send a one-time password to some risky orders with verifiable phone numbers, how long would you give your customers to verify the code? High-end fashion offering limited stock would have a different view from fast fashion. And some forms of verification, like 3DS, will work better with consumers in one region, and less well in others. 

There is no one-size-fits-all solution, and it will look different from merchant to merchant, as it should. 

1. How can AI-powered apps balance automation with organic discovery in travel and outdoor recreation?

That balance is something we’re really focused on with Globe Chaser. AI handles the behind-the-scenes work, like smart route suggestions and location-based activity planning, but we leave space for real-world spontaneity. The goal isn’t to over-automate. It’s to empower users to make the most of their time outdoors. So while the app helps guide and personalize the experience, it still feels like authentic exploration.

2. How does the app leverage real-time data to optimize route planning and adventure recommendations?

We use real-time GPS data to detect the user's current position. If location sharing is enabled, the app automatically checks for nearby routes or adventures that match the surroundings. When no preloaded routes are available, our AI engine, called AVA, steps in to generate a personalized adventure based on the user's location and preferences. Route suggestions are powered through Google’s API, ensuring the paths are walkable and engaging. Our database of routes and experiences is growing every day, and we’re also working on integrating real-time weather data to make adventures even more dynamic and safe. The goal is to offer a smooth, location-aware experience that requires minimal planning on the

3. How do you integrate gamification and interactive features to boost user engagement?

Gamification plays a key role in how users experience Globe Chaser. Players can earn points while exploring, compete in team-based battles, and even purchase in-game coins to unlock extra features and enhance their adventures. Whether it’s families, groups of friends, or corporate teams, the competitive element keeps people engaged and coming back. We're also working on a badge and level-up system that’s already part of our product roadmap. The goal is to make every adventure feel rewarding, dynamic, and a little addictive — in the best way possible.

4. What privacy and data security measures are crucial for AI-powered outdoor exploration platforms?

Data privacy is a top priority. With Globe Chaser, we use strict internal access policies, and anonymized analytics. Location data is only used when necessary and never kept longer than needed. We also give users full control over their personal data, including the option to request to delete it completely. Outdoor exploration should feel safe, both physically and digitally.

AI can actually be a powerful tool for bringing people together. It helps us recommend local group adventures and routes, highlight community-created content, and connect users with similar interests and activity levels. With Globe Chaser, users can create their own scavenger hunts, upload photos, and compete in teams, both locally and online (players can also simulate real world adventures without leaving their home). It’s not just about solo exploration. It’s about being part of something bigger.

We’ve also started working with tourism companies, hotels, and even city councils to use Globe Chaser as a tool to make their regions more attractive. Whether it’s guided city tours, themed adventure trails, or interactive explorations for visitors, we’re helping local communities offer new, tech-powered experiences that bring people together in the real world.

5. In what ways can AR (Augmented Reality) or VR (Virtual Reality) enhance outdoor adventure apps in the future?

AR has a lot of exciting potential. Imagine pointing your phone at a landmark and instantly seeing facts, hidden clues, or educational content layered onto the real world. It makes the experience more immersive and fun. VR, on the other hand, could help users preview adventures or explore places they might not be able to visit in person. Used the right way, these technologies can make the outdoors even more engaging without taking away the real-life magic. Plans to integrate augmented reality and even VR are already underway, opening the door to even more immersive and interactive outdoor experiences.

1. What are the potential benefits of adopting a managed services model for marketing services compared to traditional in-house teams or agency engagements? 

As a MarTech expert, I've seen firsthand how managed services can transform marketing operations by providing unprecedented flexibility in navigating complex technology landscapes. Our model allows companies to quickly shift experts across platforms like MAP, ABX platform, data providers, and ad platforms without the costly overhead of continuous retraining or new hires. We help organizations optimize workflows, reduce technical debt, and leverage best practices developed across multiple B2B clients, ultimately enabling them to do more with less. By offering an external perspective and deep expertise, managed services can unlock efficiencies that traditional in-house teams often miss.

2. How does the integration of AI tools streamline the go-to-market processes, leading to increased efficiency and cost savings?  

AI tools are revolutionizing go-to-market processes by dramatically improving efficiency across multiple key areas, including engagement, sales enablement, operations, content creation, marketing planning, and performance reporting. We're seeing significant time savings in content development through AI-powered co-creation and editing tools, which allow marketers to generate and refine content much faster than traditional methods. Performance metrics are also being enhanced, with AI enabling more sophisticated tracking of campaign effectiveness, engagement rates, and potential cost reductions across marketing and sales initiatives. By automating manual tasks and providing advanced insights, AI is helping marketing teams focus on strategic activities while reducing overall operational costs and improving workflow productivity.

3. What metrics should we use to evaluate the effectiveness of AI-powered marketing initiatives? 

When evaluating AI-powered marketing initiatives, we focus primarily on efficiency metrics like the speed and quality of content creation and editing. Conversion and engagement metrics remain critical, tracking performance from initial interaction through pipeline and revenue generation. We're exploring emerging KPIs that assess AI's ability to engage with new accounts or target personas previously difficult to reach. Tracking cost savings and performance improvements across specific use cases helps organizations understand the tangible value of their AI investments.

4. How does the integration of AI in marketing workflows impact the roles and responsibilities of our marketing teams? 

AI is reshaping marketing roles by automating manual tasks while introducing new responsibilities for content verification and strategic oversight. While AI streamlines processes like market research and content ideation, marketing professionals now need to invest more time in editing and quality assurance. Marketers must adopt an AI-forward mentality, focusing on leveraging tools to automate workflows and create strategic value. As AI evolves, marketing roles will increasingly require skills in AI tool management and the ability to critically evaluate AI-generated content.

5. How can we ensure data security and privacy when utilizing AI platforms in our marketing operations? 

Ensuring data security and privacy when utilizing AI in marketing operations requires strict adherence to company information security policies and a cautious approach to handling sensitive data. Marketers should integrate AI in ways that enhance workflows while operating in controlled environments, avoiding the input of confidential information into public or unsecured platforms. Staying informed on evolving data privacy regulations, investing in team AI literacy, and maintaining human oversight alongside AI capabilities will help organizations maximize AI’s potential while safeguarding their data.

6. How can integrating AI-powered platforms enhance our marketing and sales workflows? 

By providing specific use cases that improve existing processes, such as ad testing, email copywriting, and strategic market planning. By ramping up the capabilities of in-house teams and sellers, organizations can leverage AI tools for research, automation of sales cadences, and intelligent trigger-based enablement strategies. The key is to focus on targeted AI applications that solve specific pain points and incrementally improve efficiency, rather than attempting a wholesale transformation of all workflows at once.

1. How do AI agents enhance process automation, and what key inefficiencies do they help eliminate? 

AI agents enhance process automation by handling high-volume, repetitive tasks with speed, precision, and consistency. They streamline workflows by validating contracts, generating content, making rule-based decisions, and executing complex calculations. By eliminating bottlenecks such as manual data processing, human errors, and delays in decision-making, AI agents not only increase operational efficiency but also improve compliance, reduce costs, and enhance scalability. Additionally, they enable real-time data analysis, ensuring faster insights and better decision-making, while freeing up teams to focus on innovation and high-value strategic initiatives.

2. What role does natural language processing (NLP) and machine learning play in AI-driven automation? 

In the Pipefy context, the connection between natural language and AI enables us to scale our solutions. NLP acts as a bridge between humans and AI, allowing agents to process, interpret, and generate human language naturally. With Pipefy’s AI Agents, NLP ensures fluid interactions by adapting to users' language and behavior to deliver increasingly accurate and relevant responses.

Generative AI allows us to easily create agents that can operate within processes, continuously improve their performance, and optimize process automation in a smart and efficient way.

3. What industries or business functions can benefit the most from AI-driven workflow automation? 

Companies from all sectors can benefit from using AI Agents in their workflows, as they bring increased autonomy, efficiency, and precision to processes. Pipefy brings together the powerful combination of a no-code platform—enabling autonomy for business teams—with deep knowledge of the service management market, delivering solutions and use cases across industries like insurance, financial services, and HR, along with artificial intelligence that powers cognitive reasoning within workflows.

In insurance, for example, AI can streamline claims processing and risk assessment. In financial services, it helps automate compliance checks, fraud detection, and data analysis, driving significant improvements in operational efficiency and cost reduction.

4. How does AI-powered automation help businesses scale operations with the existing infrastructure? 

AI Agents are capable of profound transformations in operations and changing the working style between humans and agents, evolving the traditional automations. They allow processes to become more agile, intelligent and strategic, bringing a new era of operational efficiency and also allowing team members to focus on higher-value activities. They unlock technology within the company due to their easy setup and adoption, offering an intuitive interface and recommended agents for each type of process, without the need for any technical team for this implementation. AI-powered automation helps organizations accelerate digital transformation - empowering business areas without requiring IT support.

5. What are the biggest risks companies should be aware of when implementing AI in process automation? 

One of the biggest risks is failing to adopt AI, as companies that do not integrate AI into their operations may fall behind in AI transformation. However, even for those implementing AI, challenges remain. Businesses should be aware of potential risks such as data privacy and security concerns, probabilistic decision-making, and over-reliance on automation without human oversight. Ensuring proper governance and clear AI ethics policies can help mitigate these risks and maximize the benefits of AI-powered automation.

Additionally, it’s important to understand the use cases where AI is recommended to operate autonomously, under supervision, or not recommended at all. Because AI operates probabilistically, caution is needed to ensure appropriate oversight and avoid unintended consequences.

6. How do you see the role of AI evolving in process automation, and what’s next for the industry? 

Today, AI is no longer just hype, it’s becoming practical and accessible for companies.

Here at Pipefy, we are focused on this evolution, building an intuitive solution that democratizes AI adoption, empowering both technical and non-technical teams to automate processes with ease.

Looking ahead, we aim to further enhance agent decision-making, outcome prediction, and enable hyper-personalized workflows that drive efficiency and innovation across industries.

Because of Agentic AI, business process automation (BPA) is no longer just about automation, but delivering agents that understand the process and act independently.With Pipefy, our agents and assistants actively participate in processes in a simple and intuitive way, enhancing human-agent interactions.

1. How does agentic AI differ from traditional AI-powered chatbots in terms of user interaction and engagement ?

Traditional AI-powered chatbots operate within predefined scripts that respond based on explicit commands. Agentic AI functions autonomously and leverages generative AI to reason through multistep tasks and self-correction, and it can interact with other AI agents or humans. While traditional chatbots are reactive, agentic AI is more proactive. This enables a more dynamic, adaptive, and personalized interaction. It also humanizes the interactions and improves the customer journey and overall experience through deeper personalization. 

2. How can agentic AI facilitate more meaningful interactions between brands and consumers ? 

The biggest advantage that agentic AI offers is providing a deeper connection and conversation with customers. Brands can better understand, anticipate, and direct consumers to outcomes that align with their overall business and growth goals. This creates more loyalty and higher engagement rates. While AI has proven to be a beneficial tool, agentic AI is really where marketers can create hyper-personalized experiences by structuring and classifying incoming unstructured data, automating decision-making, and integrating with enterprise platforms​. It understands context, sentiment, and past interactions, allowing brands to respond with relevant, human-like engagement. This fosters stronger relationships, as brands can anticipate customer needs, provide proactive solutions, and ensure seamless interactions across different touchpoints.

3. How does agentic AI help event marketers identify and eliminate friction points in the customer journey ? 

Agentic AI has been and will continue to be a game changer for event marketers. Event marketers are faced with various challenges, all laddering up to proving ROI and increasing engagement. The use of agentic AI enables event marketers to analyze attendee behavior in real time, identifying drop-off points, engagement gaps, and logistical pain points​. By leveraging cross-domain automation and data-driven insights, AI can detect where attendees disengage, whether it’s during registration, session selection, or networking. AI has been instrumental for pulling out patterns and analyzing behavioral trends. Agentic AI takes that a step further. Once the technology has detected trends and patterns, AI-powered recommendations can personalize event experiences, ensuring attendees receive relevant content, and can offer real-time guidance to attendees to maximize their time at an event.  AI integrations with platforms like Salesforce and Adobe also help measure engagement and optimize marketing strategies, creating a seamless journey from registration to post-event follow-ups​.

4. How does agentic AI improve marketing automation by anticipating customer needs rather than just reacting to them ? 

Unlike traditional AI-powered chatbots that execute predefined workflows, agentic AI continuously learns from customer behaviors using multistep planning, reasoning, and reflection​. By leveraging agentic AI, it predicts future actions and tailors content, offers, and recommendations in real time. This ensures brands engage customers at the right moment with relevant messaging, increasing conversions and satisfaction. Agentic AI helps to make customer needs more personalized and better anticipated. 

5. How can businesses use agentic AI to build stronger emotional connections with their audience ?

Agentic AI is going to be very important for brands looking to change how they interact and build relationships with consumers. The technology enables brands to craft more human-centric interactions by analyzing emotional cues, past engagements, and real-time sentiment. It can personalize communication styles, anticipate customer concerns, and deliver content that resonates emotionally. Agentic AI can also help by creating personalized, context-aware, and more empathetic interactions that traditional AI cannot. It has an advantage because it can learn from preferences, behaviors, and cues and immediately tailor the customer journey and goals by providing recommendations or taking actions on behalf of the customer. 

6. How can agentic AI streamline the customer journey from awareness to conversion ?

Agentic AI enhances the customer journey by removing bottlenecks at each stage, from discovery to decision-making. Through AI-driven personalization, it can adjust messaging, recommend relevant content, and optimize engagement strategies. Agentic AI’s predictive capabilities help marketers anticipate customer needs while eliminating redundant steps in the buyer’s journey. AI-powered event insights can enable real-time adjustments, ensuring attendees and prospects receive the right touchpoints at the right time.

1. How does the integration of AI in in-store audio messaging compare to traditional methods in terms of efficiency and effectiveness ? 

Leveraging AI in audio messaging fundamentally transforms a complicated process into something remarkably streamlined. With traditional methods, businesses would spend days coordinating scripts, booking voice talent, and scheduling studio time — all while racing against promotion deadlines.

Our Messaging Copilot cuts this down to minutes through a simple two-step workflow. Organizations select their voice, compose their message, and they're done. The efficiency gains are dramatic, but what's truly compelling is how it democratizes professional messaging. Location managers can now create locally relevant content without specialized technical skills. This content also includes internal communications, where businesses can quickly create professional employee messages for training updates, operational changes, or motivational announcements.

The effectiveness piece is where things get interesting. Our research shows that 42% of shoppers engage with in-store audio messages, with 37% making purchases directly because of them. Beyond retail, these engagement patterns extend to all industries, especially hospitality, healthcare, and financial services where timely information delivery significantly improves customer experience. By enabling more timely, localized messaging through AI, we're helping organizations capitalize on audiences at the moment they're most receptive to messaging.

2. How does the use of AI in in-store audio messaging align with current trends in personalized marketing strategies ?

AI in audio messaging aligns perfectly with today's personalization trends by bringing the tailored experiences consumers expect online into physical spaces. Businesses can now quickly create location-specific content that addresses regional preferences, seasonal needs, and local events — something traditional mass-produced messaging simply can't achieve.

This technology enables organizations to react in real time to changing conditions, bridging a critical gap in the omnichannel experience. Whether it's a hotel promoting local events, a healthcare facility providing seasonal wellness information, or a retailer highlighting new inventory, background noise is now transformed into a strategic asset within the broader personalized marketing ecosystem.

3. How does the collaboration between AI-generated content and human voice talent enhance the quality of in-store audio messages ?

AI rapidly generates customized scripts for specific campaigns or locations, while professional human voices deliver the authenticity and emotional connection that technology alone can't replicate, creating a powerful synergy in audio messaging. 

This partnership preserves human warmth while solving the speed challenges of traditional audio production. Orgs can match different voice personalities to specific message types (e.g., energetic for promotions, softer for service announcements) and select regional accents or dialects like British English to match local demographics, resulting in audio experiences that deeply resonate with customers while maintaining brand consistency.

4. How can retailers measure the return on investment when implementing AI-driven in-store audio solutions ? 

Retailers can measure ROI using direct and indirect metrics. Direct indicators include tracking promotion-specific sales lifts, measuring conversion rates during targeted messaging campaigns, and comparing performance across locations using different audio strategies. Customer surveys can capture attribution data by asking how shoppers learned about promotions.

Efficiency gains provide another measurable return — calculating time saved in message creation and deployment compared to traditional methods directly translates to labor cost savings. The most forward-thinking retailers are also integrating audio messaging performance into their broader analytics ecosystem, connecting audio campaign timing with foot traffic patterns, dwell time, and purchase data to build a comprehensive view of audio's impact on the customer journey.

5. What advancements in natural language processing have enabled the development of AI tools like Messaging Copilot ? 

Natural language processing has transformed audio through several breakthrough technologies. Modern AI can now understand context and nuance in ways previously impossible, recognizing brand voice patterns and generating messaging that feels authentic rather than robotic. These systems have learned from millions of marketing examples to understand what language resonates with audiences, all without requiring businesses to build complex systems from scratch.

Voice technology has similarly evolved beyond robotic speech to create natural-sounding audio. Messaging Copilot's Voice Lab takes advantage of this progress by letting users adjust specific qualities like pace, emphasis, and emotional tone. Location managers can now make promotions sound enthusiastic or announcements authoritative without recording multiple versions. This technology delivers professional-quality audio in minutes rather than days, enabling timely, targeted messaging that keeps pace with rapidly changing retail environments.

6. In what ways can AI-generated scripts improve the consistency and relevance of brand messaging across multiple locations ?

AI-generated scripts significantly improve multi-location brand consistency by establishing a standardized framework while allowing for local customization. When orgs operate across diverse markets, maintaining a unified brand voice while addressing regional differences has traditionally required extensive coordination between corporate marketing and local managers. AI resolves this issue by generating content from approved brand templates that can be easily adapted for local relevance.

This approach ensures core brand elements — tone, key phrases, and language — remain consistent while enabling location-specific details like local events, regional preferences, or service offerings. Site managers can quickly modify messaging for their unique needs without straying from brand guidelines, eliminating the communication gaps and delayed approvals that often plague multi-location retail operations. The result is more nimble, relevant messaging that speaks directly to local audiences across diverse sectors while maintaining a cohesive brand experience.