1. What are the key challenges in scaling a digital marketing agency across multiple markets, and how does this collaboration address them? 

Scaling across geographies is never just a plug-and-play. You’re dealing with different consumer behaviors, varied platform preferences, and, of course, cultural nuances that can make or break a campaign. One of the biggest challenges is building localized expertise while maintaining a unified brand promise. This collaboration helps bridge that exact gap—by pooling together regional strengths, talent, and tech stacks, we’re able to offer clients global consistency with local relevance. It’s scale with soul.

2. How will this merger help both companies strengthen their regional and global footprint in digital advertising? 

This merger is about synergy. We’re combining complementary strengths AdLift’s performance marketing and SEO DNA with Liqvd Asia’s creative and digital storytelling muscle. Regionally, it gives us deeper roots in India and Southeast Asia. Globally, it positions us as a stronger player with full-funnel capabilities. We’re now equipped to serve global brands looking for both depth and breadth across markets.

3. How do AI and automation factor into the next wave of digital marketing innovation? 

AI and automation are no longer buzzwords they’re the backbone of what’s next. From predictive analytics to hyper-personalized content delivery, AI is reshaping how we target, optimize, and even create content. Automation frees up human creativity by taking over repetitive tasks think real-time bidding, A/B testing, even basic copywriting. The agencies that harness AI not just for efficiency but for insight that’s where the real innovation lies.

4. What are the biggest challenges brands face in maintaining a consistent omnichannel marketing strategy?

Consistency across channels is easy to say, hard to do. Brands often work in silos - search here, social there, CRM somewhere else. The biggest hurdle is integration of data, of messaging, and of teams. Without a unified strategy and tech stack, brands end up with fragmented journeys that confuse more than convert. That’s where agencies like ours step in—to help orchestrate a seamless, cross-channel narrative that actually performs. 

5. What role does data-driven marketing play in optimizing campaigns for performance and engagement? 

Data is your marketing GPS. Without it, you’re just guessing. But raw data isn’t enough - it’s about actionable insights. What content is driving conversions? Which channels are bleeding budget? How are micro-behaviors influencing macro trends? Data-driven marketing lets us test, learn, and adapt in real time - maximizing ROI while staying agile. In short, performance marketing without data is like cricket without stats pointless.

6. How do you see MarTech and AI-driven personalization shaping the future of digital advertising?

MarTech and AI are turning mass marketing into me marketing. The future is all about personalization at scale delivering the right message to the right person at the right time, without sounding robotic. AI can now predict intent, personalize journeys, and even adapt creatives in real time. Combined with a smart MarTech stack, this creates marketing that’s not just seen but felt. And that’s what builds lasting brand love.

1. What challenges have you faced in integrating AI tools into your existing marketing infrastructure?

Great question – and I think the honest answer is many!

When we’re looking at integrating new AI tools into our tech stack, we’re ultimately considering three factors when evaluating potential value and chances for success: data quality and integrity, scalability and adoption potential, and privacy and compliance.

What we have found is that if one of these factors is missing, the rate of success of integrating a new AI tool into the business declines significantly.

And this isn’t just a technical consideration, organizational readiness is just as important. Ensuring the workforce has had the training and has the skills required to adopt new AI tools is critical, and more important than simply introducing new tools.

This is something that we are hyper-focused on, and we’ve set clear goals to upskill our workforce to be AI ready and enabled globally by the middle of 2026.

2. What factors influence your decision to adopt AI-powered tools for search discovery optimization?

Firstly, we know how people research has already changed and that this change is only going to continue at a rapid pace.

This is being driven of course by user adoption and changes in user preferences for search (it’s said that up to 40% of more complex queries are now being done in AI answer engines) – but also in the way AI answer engines work.

We are moving from a world of traditional search engines which are based on lexical search methodologies (exact matches of keywords and phrases) to the AI answer engines which use semantic search to present answers back to users (return results based on the meaning and context behind a query).

This change means that the types of content that are being used to pull information from is changing, as are the sources that are being cited when presenting information back to users. And this would be a big shift for our clients in how their target audiences would discover information about them, their competitors and their industry.

Therefore, we developed our own proprietary agentic AI solution, Hotwire Spark, that is designed to reflect user and target audience behavior in AI answer engines and help us identify the key sources of information and content that is being recommended. The insights and recommendations we provide to our clients from the Hotwire Spark data enables them to optimize content for improved discoverability in AI search but also ensure clear and consistent messaging across channels to get in front of target audiences who are searching for information on solutions and topics related to their brand.

3. What investments are planned to further integrate AI into your marketing operations over the next 1-3 years?

We have recently just announced the launch of our AI Lab, which is a team focused on ensuring Hotwire is AI-enabled and continuously innovating.

This means developing new AI-enabled tools and solutions for our team such as Hotwire Spark but also looking at how we can integrate new third-party tools into our infrastructure to help us continue to deliver high-quality work for our clients. It’s not just one new tool, but many—for instance, along with Hotwire Spark we also launched Hotwire Ignite, an AI-enabled solution that analyses multiple streams of first and third-party data for ongoing account prioritization and optimized go-to market strategies-as well as ensuring our teams have a tech and AI forward mindset to take advantage of these tools.

Our main objective will be to deliver sharper insights faster for clients across the whole lifecycle of campaigns from strategy and planning, creative concepting, through to delivering measurement and reporting solutions to help demonstrate the value of marketing and communications work.

4. How does your organization ensure data privacy and compliance when utilizing AI-driven marketing tools?

Everything that we do regarding tools, whether it be AI-driven or other tools that we are looking to bring into our tech stack, we do so in partnership with our IT and legal team to ensure we are compliant and privacy-first in our decision making.

All our AI practices and tool usage is in adherence to our AI governance policy that has been developed by our parent organization, Enero Group, and which we have adopted across our agency.

5. What role do you foresee AI playing in the future of your organization's marketing and communications strategy?

It is already integral, and that influence is only set to continue to grow over the next few years.

One of our key goals as a business is to be a tech-forward consultancy, and central to the success in achieving this goal is that we invest in the best technologies – whether that be the development of proprietary solutions or third-party tools - and in upskilling our workforce to confidently utilize new tools and solutions for our clients and to enhance our own business.

We have encouraged all our workforce to embrace AI and look for ways to responsibly integrate this into daily workflows and processes. As a leadership team, we know that if we don’t integrate and utilize AI we will be at a competitive disadvantage, and therefore it is central to our current and future planning.

6. How does your leadership team stay informed about emerging AI technologies relevant to marketing?

With how quickly things are developing and evolving, it can be so easy to become overwhelmed!

Internally, would say it’s a mix of reading news, forums, newsletters, LinkedIn posts, listening to podcasts, having daily digests curated by AI … it’s certainly a challenge! But we have a global group of AI champions who are active in consuming this information and testing new technologies, and alongside our Analytics and AI leadership team (Anol Bhattacharya, Laura Macdonald and I) are sharing updates across internal Slack channels to help us stay as up-to date as possible. We also run our own research, for example we have an upcoming Frontier Tech report which examines the disconnect between how business leaders perceive impact of AI and how employees and the wider public feel about it.

And then we also ensure we’re engaging with other experts. For example, we work with think tank House of Beautiful Business to engage with their community, as well as running a series of reports and salon events (including at Davos) to explore what other leaders are addressing. We don’t just participate but are taking active leadership roles – for example, I’m on the board of AMEC where how AI is impacting how companies can more effectively measure comms programs is unsurprisingly a hot topic.

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.