If you only read the press releases, Zendesk Relate 2026 told a strong, clean story. The era of the chatbot is over. Welcome the Autonomous Service Workforce. Resolution replaces deflection. Outcome-based pricing is the new norm. Specialization beats generalist orchestration.

That’s strong. Really strong.

If you also watched the customer panel, listened to the day-two keynote, and had the chance of having analyst one-on-ones, you got a richer story. One in which the strategic bets are well-placed, the customers describe a more nuanced reality than the slogans, and three specific refinements over the next twelve months that would turn a strong position into a durable moat.

I came home quite positive. Here is why, and where I think the next twelve months are important.

What Zendesk announced and why it lands

The headline product story was the Autonomous Service Workforce: a network of specialized AI agents working alongside humans, orchestrated through what Zendesk now calls the Resolution Platform and improved continuously by the Resolution Learning Loop. Agent Builder gives customers a no-code interface to build bespoke agents. The Copilot suite expanded to four personas: Agent, Admin, Knowledge, Analyst. Voice AI handles 60+ languages mid-conversation. Employee Service AI agents from the Unleash acquisition live inside Slack and Teams. Knowledge Graph spans SharePoint, Google Drive, Notion, Guru, Contentful and Document360. Model Context Protocol support is bidirectional. Quality Score evaluates every interaction.

This is quite a handful.

Two of these messages are more powerful than the others. The first is resolution over deflection. Zendesk charges only when a resolution is verified by a second AI evaluation model; outcome-based pricing as the natural commercial expression of the philosophy, and a model Forrester has been telling vendors to move toward for the past year. The second is specialization over generalization. The argument is that 19 years of CX data, billions of  service interactions, and an opinionated service stack beat horizontal platforms using commoditized LLMs.

It is a strong argument. It is also working. Zendesk reported 130% year-over-year AI ARR growth, 20,000 active AI customers out of an 80,000 base, and more than 1,500 competitor replacements in 2025. Salesforce's own May 2026 State of Service survey shows agentic AI adoption in service jumping from 39% to 66% in twelve months. This is independent confirmation that the market is genuinely re-platforming, not just re-branding, and that Zendesk's growth sits inside a rising tide.

What customers told us and what it confirms

The customer panel completed the story. Stacy Niven of Direct Supply, Dena Fuentes of Emburse, Sam Bellach of Lyra Health, Jessica Hsieh of Levi's, Elymae Cedeño of Bumble, and Rob Giglio of Canva each added a dimension the headlines could not.

First, data foundation is more important than vendors usually admit. Stacy described Direct Supply's multi-year rebuild. Half of orders have been manually touched, processes worked in spreadsheets, an internally developed chatbot they walked back on because the product data was bad. Sam Bellach put it plainly: AI is only as good as the data feeding it. Salesforce's research confirms this: 59 to 72% of service professionals name data readiness as the top AI blocker. The Zendesk message would land even more cleanly if it acknowledged this work upfront. The customer panel, by being candid about it, did the job the brand did not need to.

Second, customers want more human connection in the AI era, not less. Jessica Hsieh cited research that 61% of CX leaders see live volumes rising. Elymae Cedeño at Bumble was emphatic that in a trust-and-safety product, humans are foundational. Levi's deploys AI for "where's my stuff" so human agents can be reserved for judgement and empathy. This is a tailwind for Zendesk's design philosophy — human-as-architect, AI-as-tool — and it argues for sharpening the messaging around that strength, not against the strategy itself.

Third, the outcome-pricing model has earned its lead, and the field will likely catch up over the next year. Sam Bellach, who is on Zendesk's Customer Advisory Board,  pushed back on the rigidity in what she describes as a candid debate. This debate is about the chicken-and-egg problem of spending ahead of proven RoI, the lack of mid-contract convertibility between agent-seat and resolution spend, and the ambiguity in what counts as resolved.

Forrester's Q2 2026 Conversational AI Wave found only one vendor scored above 3 of 5 on pricing flexibility. The fact that Sam is comfortable having that debate in public is a signal in itself. Zendesk leads the category and is co-designing the next version with its best customers.

Fourth, the most interesting moment of the week. Rob Giglio's part of the day-two keynote was structured around his recent frustration with someone else's deflection bot; he half-named "a name that sounds a lot like Zierra”. His thesis is that deflection causes churn, while resolution drives loyalty. Salesforce's State of Service report approvingly features Smarsh's 68% call deflection as "a phenomenal win". Zendesk is apparently on the right side of a still-unsettled industry debate, and Giglio's anecdote made the case more vividly than any product slide could.

The orchestration position is right. It just needs one more slide

Zendesk's Chief Product Officer Shashi Upadhyay was deliberately precise when I asked about orchestration. Zendesk wants to orchestrate every service interaction, they close the learning loop on every service interaction, and they do not pretend to orchestrate sales or marketing or the rest of the company. That is the honest answer. Salesforce, ServiceNow, SAP, Microsoft and Adobe are all pitching cross-system orchestration, with Google Cloud now positioning on top of them. Zendesk wisely declines that fight, interestingly using the same argument that SAP does against ServiceNow: You cannot govern what you cannot understand.

This strategic position is correct. What the messaging needs is one additional slide saying *we orchestrate service interactions; we hand off to your meta-orchestrator at these named integration points*. This single piece of clarity would turn a defensible boundary into an attractive value proposition. CIO buyers who currently hear "platform" and wonder whether to default to the suite would have a clear reason to choose the specialist for service while keeping their meta-orchestrator for everything else. The position is built. The slide is the missing piece.

The autonomy framing has room to grow into the brand

Salesforce measures 40% autonomous resolution today. Gartner optimistically projected 80% by 2029. The trajectory points exactly where Zendesk has bet. Independent analysis suggests today's genuine autonomy figure across the industry is closer to 20-30%, because much of what is marketed as agentic is nothing more than rebranded chatbot functionality. Zendesk's actual product reality of supervised agentic, with humans correcting, retraining and approving, is materially better than that field average, and is also the design that operationally safe service AI requires today.

This is a real strength, and it deserves equally real framing. "Supervised agentic resolution" or "agentic service workforce" would probably describe the product more accurately and would shift the conversation away from the autonomy bar to the supervised-agentic bar, which is a bar Zendesk easily clears. It is one of those cases where I think that a slightly more conservative brand line might be both more credible and more competitive.

The learning loop is the next big story

In the analyst one-on-ones I asked how Zendesk ensures the Resolution Learning Loop is learning in the right direction. If the system optimizes for what counts as a verified resolution under the current rubric, what stops it drifting toward easy-to-verify outcomes at the expense of harder ones? What stops the rubric from being gamed?

The answer covered the basics: multi-LLM grading, customer dispute mechanism, "a little conservative" on what counts as resolved. That is a solid, customer friendly foundation. What would turn it into a competitive advantage is a public, documented governance posture covering drift detection methodology, rubric versioning, human review cadence, adversarial test cases, audit visibility. Once that exists, the Resolution Learning Loop stops being a feature and becomes a moat that nobody else in the field is anywhere near ready to match. This is the most under-told story in Zendesk's deck.

My point of view

Three bets are working, three twelve-month refinements are available. The refinements: one more orchestration-boundary slide, a slightly more accurate autonomy line, and a public learning-loop governance posture, are all communication and documentation projects, not architecture ones. The strengths are outcome pricing years ahead of the field, a real data moat, an integrated platform, and partner ecosystem leverage, are durable, defensible, and can get stronger.

For buyers, the practical lessons are important, regardless of which vendor wins your shortlist.

Fix your data foundation before going agentic! Every successful customer at Relate 2026 did this first.

Demand outcome-priced contracts and negotiate flexibility into them. Design for supervised agentic, not autonomous. Stress-test demos on the hard cases.

Treat change management as a primary project. The 5-10% edge cases determine real-world performance. And the customers who built for those cases are the ones now reporting the strongest results.

Zendesk has built something real. The next twelve months are about telling that story as clearly as the customers are already telling it.

Kudos to Zendesk!

The news

On October 9, 2024, Zendesk held its AI Summit in New York’s Chelsea Industrial. The AI Summit is an event mainly for customers to inform themselves about what is new at Zendesk but also to network with each other. The event featured an interesting lineup of customer and partner speakers, headlined by New York Times bestselling author and podcast host Kara Swisher.

My estimate is that there have been more than 250 customer representatives in attendance who not only could listen to the speakers but also get in-depth demos of Zendesk’s updated offerings, following real-life use cases.

True to its name, the event centered around the use of AI, in particular bots, to increase not only efficiency, but also customer- and employee satisfaction. CEO Tom Eggememeier opened the event with an emphasis that Zendesk’s AI is built to support humans by stating that it “is designed for humans”, and Zendesk’s service solution is built to strengthen the human – AI partnership.

Kara Swisher talked about the promise and peril of AI, giving the audience some food for thought on the day after Geoffrey E. Hinton, the godfather of machine learning turned AI warner got co-awarded the 2024 Nobel Prize in Physics for his “foundational discoveries and inventions that enable machine learning with artificial neural networks”. While Swisher sees the value that the use of AI can bring, she, too, warned about the hurdles that still need to be overcome, namely the concentration of power that the technology creates and its immense hunger for energy. The tie into the Zendesk story is that customer service is a prime candidate for the use of AI, which turns information to insight while customer service in general has quite some room for improvement – and this is smack in Zendesk’s territory.

Some of the news revealed is that the functionalities that got announced at Zendesk Relate earlier this year are now in general availability. Zendesk announced them as a series of innovations, including AI-powered agents for omnichannel support, enhanced agent copilot, powerful voice, and an agent builder. The company reiterates its vision of having 80% of all service interactions automated. To achieve this, the company also delivers improved agent autonomy and an enhanced agent co-pilot. It adds that it wants to automate up to 50% of voice interactions by partnering with poly.ai.

The bigger picture

There is a continuing need for enabling a good/great customer experience and to engage correspondingly. In customer service scenarios, this makes it crucial to get responses right, fast. At the same time, customer service is a profession that still suffers from high employee churn, with the corresponding cost of re-hiring, re-training and low employee morale. As Swisher said, there is scope for improvement and a good way to achieve this is to focus on creating a positive “human-AI relationship” that helps human agents do what they can do best by helping them with what an AI can do best, which is delivering knowledge and insights as opposed to have humans research it.

Looking at this from a customer angle, customers want faster and better service, i.e., higher quality of resolutions at a faster time to resolution.  

Achieving this requires the right tools. These tools need to help agents focus on the right cases, give them help for their own improvement and, most of all, keep the mundane off their backs.

From businesses, this requires an outside-in mindset that is not solely focusing on efficiency and productivity as its outcomes, but on becoming “better” in a business sense by looking at employee- and customer outcomes.

My analysis and point of view

Zendesk tells a very compelling customer service story by combining autonomous agents, copilots and a sophisticated QA solution that helps steering a customer service organization into the right direction – namely outcomes for customers, which includes agent coaching as opposed to merely monitoring them. The latter does create a climate that a customer service organization’s most valuable assets – the agents – perceive as negative. Where the story is a bit difficult for me is where CX is used as a synonym for customer service, but this is a different topic.

The audience of the AI summit clearly shows that this story resonates. The demo-booths were well attended, and customers asked lots of questions about how to optimally leverage the capabilities. In fact, those customers, who I had the chance to briefly talk to, looked beyond the usual narrative of increasing efficiency. Instead, they looked at how to provide better internal or external service in an environment that needs to manage with limited resources and personnel. Having said this, not every customer is there yet. Many are still too focused on cost reduction by headcount reduction. This is still the current reality that also AWS General Manager of AI and ML Tia White spoke out, while maintaining a more optimistic outlook about refocusing employees on higher value tasks.

On the other hand, as also a recent Salesforce study shows (free download, requires registration), customer service professionals experience that customer expectations are continuously on the rise; customers ask for more: more service quality, more personal touch, and more speed of resolution. This combination puts customer service organizations between a rock and a hard place. And this is where helping customer service agents with automation and (generative) AI helps. As Zendesk CTO Adrian McDermott and SVP Product and Solution Marketing Lisa Kant emphasized, the adoption of AI is both, a marathon and a sprint. To support this, it needs both, fast time to value through short implementation cycles with well running “pre-trained” models, the continuous improvement of these models with clean data, as well as the careful selection and implementation of meaningful business scenarios plus the analytics to identify them.

The sprint part is the fast implementation and the continuous model improvement that come out of the box.

The marathon part is staying on focus by staying use case aligned and defining relevant KPIs that measure success.

The chosen vendor software must enable a feedback loop that helps establishing this.

In my eyes, Zendesk delivers this for customer service scenarios, although some aspects of it like the prediction of a response perception by the customer or the (semi-) automated improvement of knowledge bases, require complementing software. So, buyers who are in the market for a new omni channel customer service solution should have a good look at Zendesk.

_{Disclosure: Zendesk has paid for most of the travel and accommodation cost associated with my attending the AI Summit. This came with no obligation whatsoever. All thoughts and opinions expressed are solely mine.}

The News

On April 15 to April 18, 2024, Zendesk held its annual Relate event, including a half day analyst track on April 15. The event was attended by around 1,600 customers, partners and analysts.

It was about Zendesk’s strategy, which revolves around - no surprise here - AI to deliver better customer experiences. As part of this strategy, Zendesk also made clear how the past twelve month’s acquisitions of Klaus, Ultimate, and tymeshift get integrated into Zendesk’s customer service offerings, enriching and rounding them off. The company is betting big on AI, working on the assumption that interaction volumes between customers and companies are continuing to increase very fast. As a conclusion of this, service needs to become AI driven to accommodate this scale. Secondly, Zendesk sees AI as the technology underlying the necessary high degree of personalization. Together, this is estimated to increase the market size available to CX solutions that automate CX labor tremendously.

At the event, Zendesk had three key announcements. They were

• AI agents to improve self service solutions,
• a copilot that helps agents solve incoming tickets faster and provides insight to further optimize the service and
• a workforce engagement solution that helps improve the productivity of digital and human agents as well as the quality of conversations.

Behind all this lies the recognition that customer service is very much conversational.

Customers and partners that I talked with had a keen interest in learning more about AI use cases. Many of them had started to use AI but estimated themselves still in early stages. 

The bigger picture

The customer service software market has become very crowded with players ranging from service specialists to suite players, with classic call-center companies making inroads, too. Additionally, there is a raft of niche companies that are offering specialized add-ons to improve customer service solutions, from connecting them to workforce management or -engagement solutions, to QA solutions and many more. It is quite easy to see a kind of a consolidation going on here.

A good number of these solutions use advanced statistics or machine learning based technologies to increase the overall efficiency of customer service and/or to achieve a higher degree of automation. If not outright embedded into each other, solutions are regularly integrated using APIs, with customers often choosing to work with a small number of vendors in a kind of an ecosystem approach. With service centers being regularly trimmed to efficiency, companies usually follow one of two base strategies: they either look for efficiency, aka cutting cost, or for offering better service without adding cost. The latter is often combined with setting up the service center as a profit center, as opposed to a cost center.

The latter strategy was exemplified by a Zendesk Relate customer that built a continuous improvement strategy around its ticketing system. Three of the main KPIs that are measured and shall be maximized are customer satisfaction, employee satisfaction, and the ratio of automated responses. The company is achieving its goals using generative AI to solve issues, another AI to predict a customer satisfaction score. Human agents are involved if the AI cannot generate a response or if the predicted customer satisfaction for this response is not high enough. In this case, human agents are tasked with improving it. Quite the same happens with agent responses: The agents’ responses are evaluated for predicted customer satisfaction and sent back to the agents for improvement if the predicted customer satisfaction score is not high enough. This is combined with an automated coaching system that agents are encouraged to use to continuously improve their own productivity. The results of implementing this strategy - which admittedly is a bit more complex in terms of systems, metrics, and culture - are quite amazing.

My point of view and analysis

Let’s cover two topics here, the event itself and the content.

Starting with the event.

Looking at it through a customer lens, it was chock-full of information and, more importantly, the ability to talk to experts about the roadmap and to see working software in the exhibition hall. The customer presentations were very interesting.

Keynotes are always an interesting thing. Robert Richman very relevantly and excitingly talked about customer service requiring a service culture. Having Michelle Obama on the first day was certainly interesting. On the other hand, no one I talked to found much relation to Zendesk in her story. This is the eternal challenge that I have also seen at other events. While it is important to work with “luminaries”, it is sometimes difficult for the audience to relate their stories to the story that the vendor tells.

From an analyst perspective, I would have wished to have more access to company executives to dig deeper into information. Still, the ability to speak to customers and partners was valuable. 

Big kudos to the organizing team that mastered the challenge of creating an event that was interesting for customers, partners, and analysts.

Coming to the content.

As said in the beginning, these days every CX strategy involves AI, if it doesn’t outright revolve around it. This includes Zendesk. From a capability as well as a vision capability, Zendesk seems to be positioned better than many of its competitors in the customer service arena. And there are certainly a good number of competitors. Zendesk’s position is a result of a good product mix, the early creation of “ready-to-use” AI models to improve service efficiency as well as of interesting acquisitions. Of course, it is important to deliver this vision fast. These days, most advantages tend to be of temporary nature.

The good number of competitors is not only because the customer service market is crowded. It is also partly attributable to the fact that Zendesk claims to offer a “complete CX solution for the AI era”. This broadens the competitive field significantly, also into areas that Zendesk does not cover or does not cover with a strong product. CX is not limited to customer service.

On the other hand, Zendesk’s mission is to “power exceptional service for every person on the planet”. This focus on service processes somewhat reduces the ambiguity of the terms “CX” and “CX department”. As one of the execs said “we are great when it comes to tickets” - which is true. In short, the company maintains a focus on customer (and employee) service. It is important for customers to keep this in mind.

Having said that, Zendesk also offers a sales solution, Zendesk for sales. It was notably absent throughout the event; yet is important for the complete CX story. The same is true for the Sunshine platform, although in this case there still was a lot of talk about the platform, without naming it. This makes a lot of sense, both in terms of the need and also a need to make the fresh acquisitions part of this platform. 

Talking about these acquisitions, they are important add-ons to the core service functionality. This is especially true for the QA capabilities that come with Klaus and the AI engine that is delivered by Ultimate. Tymeshift as a WFM solution plays out its strengths increasingly with bigger growing service centers.

Service processes will be automated more and more to achieve and maintain high levels of customer and employee satisfaction. This needs a solution that has the necessary intelligence. Zendesk is on a good way to get this job done. It already now solves parts of this problem and has many of the remaining parts of this solution at hand - and is working on combining them to a whole.

The news

On January 8, 2024, Zendesk announced the acquisition of Klaus, “the industry leading AI-powered quality management platform”.

With AI driving a rapid increase in customer service interactions it is necessary for customer service teams to become more efficient while maintaining their quality of service. This is accomplished by a combination of digital and human agents across an increasing number of channels. Ensuring good quality requires a QA solution that is capable of scoring 100 percent of customer interactions, which is what Klaus’s AI is capable of. In doing this, it “pinpoints conversations with positive or negative sentiment, identifies outliers, churn risk, escalations, and necessary follow-ups. According to Zendesk, most QA software does handle only one to two percent of all customer interactions.

With workforce enablement management capabilities, Klaus enables the identification of knowledge gaps and coaching opportunities with the goal of improving agent performance and productivity. The result is higher customer satisfaction.

According to Martin Kōiva, CEO and founder of Klaus, “Zendesk and Klaus share a vision of Ai-led, personalized CX with businesses fully anticipating and acting on their customers’ needs. QA software plays a critical role in this, ensuring consistency, assessing both human and digital agent performance and providing actionable insights for strategic planning. As part of Zendesk, we will continue to build and deliver thes crucial capabilities, but now at an even greater scale”.

The bigger picture

Customer service personnel works in a high-pressure environment with lots of turnover; even worse, as frontline workers, they are often the first ones to deal with customers who are already less than amused – equipped with tools that are not helpful, either. At the same time, partly due to the high attrition rates, agents often lack the training and experience to deal with difficult situations and do not have the contacts to find relevant information fast.

What they need is being able to concentrate on the “hard cases”, easy access to the relevant information, ideally without the need to use multiple browser tabs, good training and coaching, and empowerment.

In addition, the increasing need for automation, that is caused by a shortage of available personnel as well as increasing customer demands for availability, leads to an increased proliferation of chatbots. At the same time, customers do not trust into AI, as the last state of the connected customer (registration required) report by Salesforce found out.

My analysis and point of view

After the acquisition of Tymeshift last year and the provision of “ready-to-run” AI supported bots, Zendesk is continuing on its path to provide a very rounded customer service platform.

The emphasis on coaching customer service agents is great – and important – to see. Agents are already some of the most monitored and measured workers around, so they will be glad to see some help instead of just feeling surveilled. Especially, as there is so much attrition going on. Businesses that understand that a better trained and enabled team increases agent motivation and therefore in all likelihood the individual and team performance. This has a direct impact on customer satisfaction and with that on business outcomes. Zendesk now can support this better than before.

This is also something that was found in the recent Customer Experience Horizons report by Genesys. 70 percent of respondents will invest into AI-based coaching and training recommendations for their personnel with still almost 60 percent looking into implementing AI-based performance monitoring and evaluation solutions.

Being in times of AI, the ability to also analyze and especially improve the performance of digital agents becomes more and more important. With Klaus, Zendesk is now better able to identify customer service bots’ “knowledge deficiencies”. In my eyes, the next important step from here is to (semi-)automatically reduce these deficiencies, supported by generative AI. This, based on actionable analytics. Why is this important? Because the performance of both, human and digital agents, highly depends on a well-performing knowledge base.  I am interested in seeing how this topic evolves. Embedding Klaus in the fold, certainly should help taking the first steps by helping to find out what the relevant customer and organizational data is, and to initiate steps in improving the state of this data.

But again, looking at the needs of customer service agents, businesses that pursue helping their agents are now in an even better position when using Zendesk.

The News

On September 14, 2022, Zendesk announced the release of its new customer sentiment and intent functionality: Intelligent Triage and Smart Assist. These new AI based solutions shall “enable businesses to triage customer support requests automatically and access valuable data at scale.

Intelligent Triage and Smart Assist are the next step in Zendesk’s vision to create accessible CX AI for companies of all sizes. The technology uses proprietary industry expertise and insights from trillions of customer data points and applies a vertical lens. This creates models custom to each business capable of identifying the intent, language and sentiment of each customer interaction.

This unique approach to applying machine learning creates more personalized and informed interactions to better serve customers. For example, specific inquiries, such as "I'm having problems with payment", can be automatically sent to an agent who is equipped to handle billing for a quicker resolution, while inquiries that include language written in all capital letters or in a sarcastic way will indicate a highly negative sentiment and be routed to the top of the queue.

The new capabilities include:

• Instantly route and prioritize revenue drivers, ensuring agents are working on business-critical requests
• Analyze distribution of requests so businesses can better plan operations, collaborate across departments and identify improvement opportunities supported by data for more efficient CX operations
• Automatically guide agents on how to best resolve a customer’s issue in real-time, understand context, recommend solutions, and improve coaching and training with valuable insights
• Continuously boost accuracy as the AI solutions receive feedback on predictions and recommendations
• Detect sensitive information automatically to meet compliance and security needs or extract confidential data like names, addresses, phone numbers, usernames, and financial info for use in workflows”

The bigger picture

Customer support is a very difficult business. All too often agents are not trained too well and service departments suffer from high turnover. On the other hand, customers expect to solve issues faster and faster. This drives a demand for efficiency. This gets underpinned by the current economic climate in combination with a worker shortage (although this shortage might also be self-inflicted by businesses). 

In general, AI has become an important ingredient in customer service and, more generally, CX solutions. The main use cases are ticket routing and assisting in solution search. While there are a number of chatbot solutions that base on an underlying AI, their performance often is not good enough to be really successful. Another challenge is that implementations regularly take considerable time. Therefore, the pressure on customer service departments through case deflection does not decrease enough.

My analysis and pov

As Cristina Fonseca, VP Product of Zendesk and co-founder and CEO of Cleverly, the company that got acquired by Zendesk in 2021 and that is behind this feature set explained to me, Zendesk was behind the competition. What Zendesk now does is automating the mundane tasks, therefore effectively relieving agents from time consuming tasks. The second part is that the solution comes with pre-trained models that make it ready to run in very short times. Doing the hard work and analysing that 80 percent of inquiries have the same intent across industries has been the key for this. Still, the built-in feedback loop that allows for constant company specific readjustment of the model, is important as there normally is no one-size-fits-it-all solution and there is a high likelihood for service requests changing in nature over time. This is also true for the revenue drivers prioritization of requests, which is likely to be at least industry-, if not business model and therefore company specific. I am curious to see how this works across industries.

Making these features available as part of the suite and not as an add-on is a smart move as it doesn’t change the pricing for existing enterprise edition customers and potentially drives more customers to choose this edition.

With this solution Zendesk gains an edge, albeit probably of temporary nature. Clearly, the company should continue on this path to maintain it, maybe adding coaching capabilities that help agents and supervisors to train themselves and each other. Additionally, if the solution works as expected, why not also using it for case deflection. After all, from a system perspective, it does not really matter whether an agent is guided on how to best resolve an issue or whether the customer him-/herself is guided. From a company perspective, it does. Fonseca maintains that the market is not yet ready for it. This is probably right as many chatbot implementations are rather underwhelming. Still, coming from a working solution can change this. In addition, Zendesk should add an explainability capability, although Fonseca maintains that this is not a priority as there is too much turnover. Still, a good deal of productivity improvement and efficiency will come from achieving a stable workforce. With an explainability component Zendesk can achieve two goals: first it helps coaching the agents and second it will increase the acceptance of the system. Untrained agents will more easily accept the system recommendations, effectively treating them as prescriptions. In which case they better be accurate. The confidence score that is already provided is a first step into this direction. 

Zendesk, a Mobile Native or Not?

• The searching and showing of help center content
• The opening and management of requests (support tickets)
• And the prompting for app reviews

• Chat
• Messaging
• Attachments to chats and messages
• Chat ratings
• Emailing of chat transcripts

What does Zendesk think about AI and Bots?

My Take

1. In-App FAQ that gets pushed out to the phone and is available in an offline scenario
2. Collation of meta data about the phone, user and the incident that created the support call, along with the ability to send that to the customer service center
3. In-App messaging/conversational UI in combination with push notifications
4. Automation to properly route incoming issues and to increase the issue resolution efficiency
5. An ability to integrate into CRM- or other systems
6. An ability to selectively and proactively engage with users, to e.g. support onboarding or push notifications about special situations to relevant parts of the user community.

• Apptentive
• Freshdesk/Hotline
• Helpshift
• Intercom
• LivePerson
• Salesforce Service Cloud and com
• Zendesk

Vendors

Apptentive

Freshdesk/Hotline

Helpshift

Intercom

LivePerson

Salesforce

Desk.com

Service Cloud

Zendesk

What It Means