G2’s 2026 Report on AI Adoption in Customer Support

Key Trends for AI in Customer Support in 2026

• AI has become central and continues to expand. All five vendors are either scaling or operating AI extensively, reporting improvements in productivity, routing precision, and agent satisfaction. They anticipate increased automation over the next two years
  

  Artificial intelligence has entered customer support with a force that few operational teams have been able to ignore. Over the past year, support leaders have watched AI move from a peripheral add-on to a central part of how conversations flow, how issues are routed, and how teams structure their work. 

  What was once a predictable sequence — intake, respond, escalate — now unfolds inside dynamic systems that use AI to triage, draft responses, and in some cases resolve routine issues independently.

  To understand how these shifts are taking shape, we partnered with five vendors who build and operate customer support technologies: Front, CloudTalk, Desk365, Smartsupp, and Missive. Their perspectives offer a practical view into how AI is influencing team structures, shaping workloads, and redefining efficiency expectations. Even with a small cohort, clear themes emerged about where AI is creating momentum, where human judgment remains essential, and how support organizations are adapting in real time.

  This report highlights the patterns and contrasts in their responses, surfacing what today’s vendors are experiencing inside their own operations and what they believe will matter most as AI becomes embedded across every support channel.

  TL;DR: What are the top trends for AI in customer support in 2026?

  • AI is now core to customer support and its role is expanding. All five vendors entered 2026 either scaling or fully operational, with gains in productivity, routing accuracy, and agent satisfaction, and every participant expects higher automation levels over the next 24 months, even as full autonomy and major cost savings remain future goals.
  • Hybrid AI-human operating models are the new norm. Fully autonomous support is still uncommon.
  • Human roles are becoming more specialized. They are focused on complex, judgment-oriented work.
  • Customer expectations are rising. It’s driven by faster, more consistent AI-enabled workflows.

  These insights show an industry transitioning into a new operational model — one where AI works throughout the support journey while humans own the moments that require context, nuance, and trust.

  Methodology

  This report is based on survey input from five vendors who develop customer support technologies. Each vendor completed a structured questionnaire focused on how AI is being adopted within their products and within their own support operations.

  The questionnaire explored topics such as:

  • AI maturity and capabilities
  • How AI is integrated into support workflows
  • Internal changes to team structure, workload, and processes
  • Early outcomes from AI adoption
  • Predictions for the next two to three years

  Because vendors often observe these changes before they reach end users, their perspectives help surface emerging patterns in customer support operations. These findings should be interpreted as directional rather than representative of the broader market and are based solely on the survey responses submitted by participating vendors.

  Before we dive into the details, it’s worth briefly introducing the five platforms behind these insights.

  Who are the 5 platforms contributing insights to AI in customer support?

  This report includes insights from:

  • Front (G2 rating: 4.7/5): A collaborative inbox for managing high-volume customer conversations across channels, combining AI-driven routing with human-led workflows for speed and accountability.
  • CloudTalk (G2 rating: 4.4/5): A voice-first support platform that uses AI for call routing, transcription, and analytics while keeping agents in control of complex customer interactions.
  • Desk365 (G2 rating 4.8/5): A lightweight help desk for small and mid-sized teams, using automation and AI-powered deflection to reduce manual work and scale support efficiently.
  • Smartsupp (G2 rating 4.7/5): Best known for chatbot-led customer interactions that resolve common questions instantly and hands off complex conversations to human agents.
  • Missive (G2 rating 4.6/5): A shared inbox and collaboration hub that uses AI to assist with triage and summarization while preserving human ownership of responses.

  Together, these five vendors span the customer support technology ecosystem — from helpdesk and shared inbox platforms to live chat, chatbots, and contact center software. Their combined perspectives offer a grounded view of how AI is being applied across real support workflows today, and how those approaches are shaping team structures, automation boundaries, and performance expectations.

  How are support vendors adopting AI, and where are they on the maturity curve?

  AI has become a defining force in customer support, and the five vendors in this study illustrate how quickly it is being incorporated into both product capabilities and internal support operations. While their maturity levels differ, all show clear signs of meaningful AI adoption.

  Vendors are moving steadily up the AI maturity curve

  All vendors describe themselves as either scaling or fully operational — none reported early-stage adoption. Among platform providers, this trajectory reflects a broader expectation: customers increasingly assume AI will be built into their tools, not added later, which pushes vendors to adopt and refine AI internally as well. 

  Data at a glance:

  3 of 5 vendors report fully operational AI in production by late 2025, while the remaining two are actively scaling AI across support.

  Core AI capabilities center on automation and assisted handling

  When asked which AI capabilities they use or offer, vendors consistently cited features that reduce manual work or accelerate decision-making. Several shared themes appear across the dataset:

  • Automated responses and AI chatbots
  • Routing or triage powered by AI
  • Suggested replies or agent assist
  • AI-powered knowledge retrieval

  More advanced capabilities, like AI-driven voice agents, were selected less frequently, showing that it is still gated by risk, reliability, and rollout complexity.

  Missive describes a growing emphasis on AI-assisted triage and knowledge search within its shared workspace. According to the team, AI is being used to streamline collaboration across email and ticket-based conversations, while decision-making and final responses remain firmly guided by human judgment.

  AI adoption entering 2026 centers on text-based channels and is expected to expand

  When asked where AI is currently used in support workflows, vendors most commonly pointed to chat, email, and ticket-based channels. These text-first environments have proven to be the most stable and reliable surfaces for AI-assisted handling, allowing vendors to deploy automation for triage, summarization, and response assistance with lower risk.

  Voice remains less common, reflecting higher complexity and reliability requirements. CloudTalk stands out as the exception, already applying AI in production to support phone-based interactions while still routing complex calls to trained agents.

  The degree to which AI participates in support interactions varies across vendors today. Some report that AI already handles a meaningful share of routine inquiries, while others rely more heavily on human-led workflows with AI operating in the background. Despite this variation, all five vendors expect AI involvement to increase over the next 24 months, driven by expanded automation, deeper hybrid handling, and more AI-led routing across channels.

  Data at a glance:

  • All five vendors currently use AI in chat, email, or ticket workflows.

  • Only one vendor applies AI in voice support as of late 2025.

  • Every participant expects AI to handle a larger share of support interactions within the next 24 months.

  Accuracy and expertise, not cost, are the biggest challenges

  Across this cohort, vendors pointed to two primary challenges in scaling AI

  • Accuracy concerns
  • Lack of internal expertise

  Notably, none selected cost as a primary constraint or cited other commonly cited blockers like integration complexity, compliance, or customer trust. 

  This aligns with broader signals from G2 research: G2’s Enterprise AI Agents Report shows strong budget commitment is already in place: 1 in 4 large enterprises is investing $5 million+ in AI agents, and 40% of companies report a $1 million or less AI agent budget (including software, cloud services, and staffing). 

  How is AI changing customer support team structure, workload, and headcount?

  Even though the five participating vendors vary in product category, size, and AI maturity, their internal support teams are experiencing some of the same shifts: workloads are changing, team structures are evolving, and new expectations are forming around what support roles look like in an AI-enabled environment.

  Unlike industry forecasts, these shifts come directly from how vendors themselves use AI inside their support organizations — a perspective often missing from market conversations.

  Internal headcount shows early reductions, but expectations for 2026 vary

  Three of the five vendors reported a reduction in their support headcount, ranging from 1–25%. The remaining two indicated that their team size has stayed the same. None reported growth.

  These early reductions suggest that AI is already reshaping some support teams, even if the scale of change remains modest. Looking ahead to the next 12 months, expectations are split: some vendors anticipate continued stability, while others expect further adjustments as automation matures and their AI capabilities expand.

  Desk365 has already seen measurable headcount reductions alongside strong KPI gains, showing how a smaller support organization can use AI to scale without sacrificing quality or responsiveness.

  Data at a glance:

  • 3 in 5 vendors report headcount reductions after AI adoption.

  • No vendor cited an increase in team headcount. 

  • The reductions are modest but signal the early stages of AI-driven workforce adjustment.

  Work is being redistributed, but changes remain gradual

  According to the vendors, changes are showing up first in how work is distributed rather than through dramatic role elimination. Routine and repeatable tasks — often associated with Tier-1 support and support operations — are increasingly handled by automation, while human roles remain focused on more complex, judgment-heavy work.

  Several vendors pointed to the emergence of new or expanded responsibilities related to AI oversight, knowledge management, and tuning. This suggests that AI is not eliminating work outright, but reallocating it, moving effort away from repetitive handling and toward higher-skill coordination and governance tasks.

  These role-level changes align closely with how vendors describe workload impact. Most reported that the overall workload has decreased slightly, while one saw no meaningful change. Importantly, no vendor reported an increase in workload, indicating that AI is easing friction without introducing new burdens. However, the magnitude of change remains modest, reflecting early-stage adoption where AI supports discrete tasks rather than reshaping end-to-end workflows.

  Together, these responses show that AI’s impact inside support teams is currently incremental rather than disruptive. Work is being redistributed before it is reduced, and responsibilities are shifting before roles disappear. This reinforces the view that structural change is unfolding gradually as automation matures

  Data at a glance:

  • Four vendors saw slight workload reductions. 

  • One vendor saw no change. 

  • None reported increases, reinforcing that AI is streamlining tasks without creating new burdens.

  Hybrid operating models dominate, with early AI-first patterns emerging

  Most vendors describe their current operating model as hybrid, where AI supports human-led workflows without fully automating support end-to-end. In these setups, AI typically assists with tasks like triage, routing, summarization, or suggested replies, while human agents remain responsible for resolution and decision-making.

  Alongside this, several vendors are experimenting with AI-first triage, where automation leads the initial stages of the interaction. In these models, AI handles early routing or resolves well-defined inquiries before escalating more complex cases to human agents. The key distinction is where AI enters the workflow: hybrid models assist humans throughout, while AI-first approaches shape the “front door” of support.

  Notably, no vendor described a fully autonomous support model. Instead, responses point to a spectrum of approaches — from AI-assisted handling to chatbot-led resolution for clearly bounded use cases, with explicit human fallback built in.

  Smartsupp’s chatbot-led flows illustrate this balance in practice: AI resolves a large share of common live chat questions end to end, while human agents step in for complex or high-value conversations, preserving both efficiency and customer satisfaction.

  To support these hybrid and AI-first operating models, vendors rely on a mix of AI tools rather than a single system. When asked which AI tool categories they use internally, responses spanned multiple areas, including:

  • Agent assist tools
  • AI chatbot or automation platforms
  • Workflow automation systems
  • Knowledge search or retrieval tools

  This mix reinforces that AI in support is not a single technology but a cluster of capabilities working together. Even among vendors building AI features, internal operations often depend on multiple AI layers.

  Missive leans on this layered approach by pairing assistive AI, triage, and knowledge tools to help teams manage multi-channel conversations without overhauling existing workflows.

  What measurable impact is AI having on support performance and efficiency?

  While much of the conversation around AI in customer support has focused on long-term transformation, the experiences of these five vendors offer a more grounded view of AI’s current impact. Their internal data shows clear gains in speed, productivity, and agent experience, alongside more uneven results in cost reduction and ticket deflection. Overall, the KPI impact is positive but modest, reflecting early-stage adoption rather than mature, end-to-end automation.

  Speed and productivity improve first as AI reduces friction

  Across the five participants, operational speed and productivity are the most consistently improving metrics. Vendors report higher resolution rates, faster first response times (FRT), and lower average handle times (AHT) after introducing AI into support workflows.

  Improvements in these metrics are generally described as slight to moderate rather than transformational, but they are consistent across the cohort. Desk365, in particular, reports strong gains across multiple KPIs, illustrating how even smaller teams can benefit from well-scoped AI workflows. Taken together, the results suggest that AI delivers its earliest value by accelerating existing processes rather than redesigning them. 

  But when support leaders look for ROI, the story becomes less consistent.

  Cost efficiency remains uneven and lags other gains

  In contrast to speed and productivity, cost per ticket shows the most mixed results. Three vendors reported improvements (two slight, one significant), while two reported higher costs following AI adoption. This split highlights that financial efficiency is not yet a guaranteed outcome of early AI deployments.

  In many cases, initial investments in tooling, training, and oversight offset efficiency gains, especially when AI is used primarily in assistive or hybrid models. As a result, cost appears to be a lagging indicator that improves only after automation becomes more reliable and more deeply embedded across workflows.

  This pattern contrasts with more mature AI agent deployments observed in G2 Data. In G2’s Agent Builder category, companies report a median 40% cost-per-unit savings for their most advanced AI agent workflows, alongside 80% median containment rates for customer service incidents. By comparison, genAI chatbot containment averages closer to 50%, helping explain why cost savings remain uneven at earlier stages of adoption.

  Ticket deflection remains limited in hybrid environments

  Across the cohort, ticket deflection remains uneven and limited. While a small number of vendors reported improvements, most described those gains as slight, and one reported no meaningful change. This pattern reflects how AI has been deployed through 2025: primarily in hybrid or AI-assisted models rather than fully autonomous resolution for the majority of customer inquiries.

  In these environments, AI tends to improve how agents work more than it reduces the total volume of conversations that reach support teams. Meaningful deflection gains typically depend on high-confidence automation applied to clearly bounded use cases — conditions that many vendors are still developing as they enter 2026.

  Desk365 stands out as a more deflection-forward example. Among the five participants, it is the only vendor reporting significant deflection gains, driven by a deliberate shift toward end-to-end automation for tightly scoped Tier-1 issues. By allowing AI to fully resolve routine inquiries while routing complex cases to human experts, Desk365 has positioned deflection as a primary operational outcome rather than a secondary efficiency metric.

   “The most impactful benefit we’ve seen is AI-driven ticket deflection: let AI handle L1 queries end-to-end, while human experts focus on L2 issues that require judgment and nuance.”

   Kumar Krishnasami
  CEO & Founder, Desk365
  

  Desk365’s experience illustrates what becomes possible when automation is trusted to own repeatable work. However, it also highlights why deflection remains limited across the broader cohort: most vendors are still operating in hybrid environments where AI assists resolution rather than fully replacing it. As automation confidence, knowledge quality, and escalation design mature, deflection is likely to increase — but for now, it remains a leading indicator rather than a universal outcome.

  Agent satisfaction shows the strongest and most consistent gains

  Agent experience stands out as the most uniformly positive outcome of AI adoption. All five vendors reported improved agent satisfaction after introducing AI into support workflows, with several noting significant improvement.

  These gains appear tied to reduced cognitive load and less time spent on repetitive tasks, allowing agents to focus on more meaningful or complex work. CloudTalk and Smartsupp, both of which pair automation with clearly defined human roles, illustrate how assistive AI can improve morale without threatening role clarity.

  This trend mirrors broader market signals. G2 research shows that nearly 90% of enterprises report higher employee satisfaction in departments that have deployed AI agents, reinforcing the idea that human benefits often materialize before financial ones.

  How do support vendors expect AI to reshape support over the next 2-3 years?

  When asked to look ahead two to three years, vendors described a future where AI plays a more foundational role in shaping how support work is organized and delivered. Rather than incremental feature expansion, their responses point to a shift in how workflows are coordinated, automation is introduced, and human effort is allocated across the support journey.

  While predictions vary in emphasis, vendors consistently expect AI to become more capable and more embedded, raising customer expectations for speed and consistency while pushing human expertise toward higher-complexity work. The result is a vision of support in 2026 that is more automated, more data-driven, and more intentionally designed around human-AI collaboration.

  AI will evolve from point features into an orchestration layer

  Vendors expect AI to move beyond isolated assistive features and become a coordinating layer that shapes support workflows across channels. Instead of simply helping within individual interactions, AI will increasingly determine how conversations enter the system, when automation takes the lead, and when human intervention is required.

  This shift introduces a more proactive and anticipatory role for AI. Vendors describe systems that recognize patterns across interactions, adjust handling based on confidence and context, and surface potential issues before they escalate. In this model, AI is not just responding to tickets but helping structure the “common path” of support while routing exceptions to humans.

  As AI takes on this orchestration role, ownership of support workflows is also expected to change. Teams will spend less time configuring one-off automations and more time governing AI behavior — defining escalation thresholds, monitoring outcomes, and ensuring consistency across channels and use cases.

  Human agents will move into more specialized, higher-complexity roles

  Alongside deeper automation, vendors consistently predict a continued shift in the role of humans within support teams. As AI absorbs routine inquiries and provides richer context, human agents will increasingly focus on work that requires judgment, nuance, and trust.

  This includes:

  • Complex or ambiguous customer situations
  • Nuanced problem-solving and exception handling
  • Relationship-driven or high-stakes interactions
  • Escalations that require emotional intelligence or cross-functional coordination
  • Scenarios where AI lacks sufficient confidence or clarity

  Rather than signaling a reduction in human involvement, these predictions reflect a rebalancing of responsibilities. As AI moves deeper into repetitive and structured work, human agents move upward into specialized roles that emphasize expertise, empathy, and decision-making – reshaping support work rather than replacing it.

  Personalization will become a competitive standard

  Vendors consistently point to personalization as a key area where AI will reshape support. Rather than one-size-fits-all responses, AI is increasingly used to incorporate customer history, intent, and context into how conversations are routed and handled.

  Front describes AI-led workflows that use context to resolve issues faster while escalating high-trust moments to humans. Missive highlights AI-assisted triage and knowledge search that help teams tailor responses within shared inbox and ticket workflows. Desk365 emphasizes that as AI handles more L1 interactions, teams can maintain relevant, personalized experiences at scale while reserving human effort for complex cases.

  Across vendors, personalization today is less about message variation and more about contextual execution – including lifecycle-aware handling, behavior-triggered assistance, and role- or intent-based routing. These approaches allow support teams to deliver more relevant experiences without fully redesigning workflows.

  Together, these perspectives point toward more context-aware, consistent support experiences, where AI augments human judgment rather than replaces it.

  Support teams will depend more heavily on internal alignment and knowledge management

  Across predictions, vendors repeatedly referenced the rising importance of:

  • Centralized knowledge
  • Better documentation practices
  • Systems that help AI maintain accuracy
  • Humans who refine or govern AI behavior

  This signals that, as AI expands, knowledge quality becomes a strategic pillar. Poor or outdated knowledge will ripple into AI performance; strong knowledge frameworks will accelerate automation.

  Some vendors also expect support roles to shift toward building and maintaining automation systems, not just handling escalations, especially as AI accuracy increasingly depends on well-governed knowledge and workflows.

   “Support will be fully automated, and the added value of personal interaction for key clients will grow. Customer care roles will change from answering questions to setting up AI and automation ecosystems”

   Jakub Horký
  CEO, Smartsupp
  

  Customer expectations will increase, driven by AI-enabled speed and accuracy

  Vendors broadly agree that AI-enabled support is already reshaping what customers expect from service interactions. As response times shorten and automation becomes more reliable, speed and consistency are quickly becoming baseline expectations rather than differentiators.

  Front emphasizes that faster resolution must be paired with quality and trust, noting the importance of clean escalation when AI confidence drops. Smartsupp highlights how chatbot-led flows set expectations for instant answers to common questions, while human agents step in for nuanced cases. CloudTalk points to AI-assisted handling in voice and text channels, raising expectations for availability and seamless handoffs across touchpoints.

  Across these perspectives, a consistent theme emerges: higher speed raises the bar for accuracy. As AI becomes more visible to customers, tolerance for incorrect or inconsistent responses decreases. This shift is increasingly shaping how support teams define responsibility between AI and humans.

   “AI should handle the common path. Humans should own the moments that matter.”

   Kenji Hayward
  Senior Director of Customer Support, Front
  

  At Front, this philosophy shows up in AI-led workflows that prioritize speed and smart routing, with clean escalation paths when confidence drops. Rather than maximizing automation at all costs, teams instrument outcomes such as CSAT, resolution quality, and cost per ticket, cutting automations that fail to meet performance thresholds and redeploying human effort toward edge cases, recovery, and continuous improvement.

  Support will become more strategic within organizations

  Vendors describe a shift in how support teams create value as AI absorbs more operational work. Rather than focusing solely on ticket resolution, support teams are increasingly able to analyze patterns, surface product insights, and contribute to broader customer experience strategy.

  Front highlights how AI-led workflows free teams to focus on edge cases, recovery, and continuous improvement instead of repetitive handling. CloudTalk points to AI-assisted workflows creating space for closer collaboration between support, product, and engineering – especially around recurring issues and customer feedback loops. Desk365 similarly notes that automation enables smaller teams to operate more strategically without sacrificing responsiveness.

  Together, these perspectives reinforce a shift from support as a reactive function to support as a source of insight and cross-functional influence.

  What should support leaders prioritize as AI transforms the workforce?

  The insights shared by the five participating vendors point toward an AI-enabled future that blends automation with human judgment, distributes intelligence across tools, and elevates the importance of well-maintained knowledge. The following recommendations focus on what leaders need to do differently now to prepare their teams and operating models for 2026.

  1. Start with hybrid workflows and design them intentionally

  As AI becomes more embedded in support operations, the biggest risk isn’t under-automation – it’s unclear ownership between humans and AI. Teams that treat hybrid workflows as temporary or loosely defined risk confusion, duplicated effort, and inconsistent customer experiences as automation scales.

  What leaders can do

  • Identify tasks that AI can reliably support today (triage, summarization, suggested replies).
  • Define clear handoff rules between AI and agents to avoid confusion or duplicated effort.
  • Treat hybrid workflows as a foundation, not a temporary stage — these models will continue to evolve.

  2. Prioritize channels where AI reliability can be controlled

  As customer expectations rise, errors in AI-driven interactions become more visible and more damaging. Leaders who deploy automation too broadly, too quickly risk undermining trust before AI maturity catches up.

  What leaders can do

  • Begin by applying AI to channels where accuracy is easier to control.
  • Use early wins to inform broader rollout plans.
  • Explore voice AI cautiously, with pilot programs rather than broad deployments.

  3. Treat knowledge management as a prerequisite, not a follow-up

  As AI takes on more front-line responsibility, weak or outdated knowledge becomes a systemic risk. Poor documentation doesn’t just slow agents – it directly degrades AI accuracy at scale.

  What leaders can do

  • Audit existing knowledge bases for accuracy, freshness, and consistency.
  • Assign clear ownership for ongoing updates.
  • Align knowledge structure with how AI retrieves and uses information.

  4. Build internal AI expertise before complexity compounds

  As AI participation increases, support teams can no longer rely on vendors or tools alone to manage performance. Without internal expertise, leaders risk flying blind – unable to diagnose accuracy issues or refine automation effectively.

  What leaders can do

  • Create internal roles focused on AI tuning, evaluation, and monitoring.
  • Train agents to work with AI tools, not just alongside them.
  • Involve support teams in evaluating AI accuracy and identifying improvement areas.

  5. Measure success by operational quality, not immediate cost savings

  As automation expands, leaders who anchor success metrics too early to cost reduction risk misreading progress or cutting investments before they compound. In 2026, productivity and quality improvements will precede financial ones.

  What leaders can do

  • Measure early success using productivity, accuracy, and quality metrics, not cost savings.
  • Look for compounded efficiency gains rather than immediate budget impact.
  • Use internal improvements to build the foundation for longer-term automation.

  6. Redesign roles for judgment, not repetition 

  As AI absorbs routine work, support roles that remain unchanged will quickly feel misaligned. Leaders who don’t adapt job design risk disengagement, skill gaps, and missed opportunities to elevate team impact.

  What leaders can do

  • Review job descriptions with an eye toward specialization and technical literacy.
  • Strengthen training in problem-solving, customer empathy, and cross-functional knowledge.
  • Encourage agents to develop skills that complement AI, not compete with it.

  7. Plan for an AI ecosystem, not a single solution

  As AI capabilities expand, no single tool will handle every workflow well. Leaders who design around one platform risk rigidity as needs evolve.

  What leaders can do

  • Adopt a layered approach to AI: the right tool for the right workflow.
  • Ensure your systems can communicate with one another, even if they come from different providers.
  • Plan for your AI stack to grow rather than consolidate in the near term.

  8. Prepare for higher customer expectations, not just faster workflows 

  As AI becomes more visible to customers, tolerance for mistakes drops. Speed without accuracy erodes trust, and inconsistent automation can damage the experience more than slow human handling.

  What leaders can do

  • Track AI performance as closely as human performance.
  • Introduce quality reviews for AI-generated responses.
  • Use customer feedback to refine when and how AI appears in the support journey.

  What’s ahead for AI-driven support, and how should teams prepare for 2026?

  Across the perspectives shared by the five participating vendors, a clear picture emerges: AI is reshaping customer support, just not all at once. The changes underway are steady, practical, and layered rather than sweeping. Support teams are not being replaced, but they are being redefined. Workloads are shifting before headcount does. New roles are emerging before old ones disappear. And AI is becoming present in more moments of the support journey, even if it is not yet running those moments independently.

  The organizations that adapt best will be those that invest early in workflow design, knowledge management, internal expertise, and the right blend of tools. The next phase of AI-enabled support will reward teams that build for collaboration between humans and AI, not competition.

  For deeper insight into how buyers are evaluating AI capabilities and making purchase decisions, explore G2’s latest Buyer Behavior Report.