Remember HAL 9000 from "2001: A Space Odyssey"? The eerily calm, near-sentient robot? It was a dependable coworker for the humans onboard. Well, at least before it turned rogue (more on that later).
HAL represents one of science fiction's most iconic visions of artificial intelligence (AI) with autonomy — an AI system that could observe, reason, and act independently to achieve its objectives.
That's a glimpse of agentic AI in action. As the name already gives away, agentic AI has agency. Running on large language models (LLM), it works without human prompts. It not only creates and plans tasks but executes them on its own. And all of this is not just sci-fi anymore; it's the next big thing for tech giants. Salesforce deems it "era-worthy," and McKinsey touts it as the "next frontier of generative AI."
"The AI agents market is expected to grow at 44.8% CAGR between 2024 and 2030, driven by technological advances in natural language processing (NLP)."
Markets and Markets
One notable development in the agentic AI breakthrough is that it warps how we’ve viewed AI so far: hand-held tools that aid us. Instead, AI agents are proactive digital coworkers that might just be reshaping how teams are structured, how workers interact, how tasks must be divided, and more.
This shift also raises an unsettling, perhaps sinister, question: are so many human workers needed in the first place?
Backed by data from 3,621 reviews, this article explores how AI agents are being used in the workplace today and what that means for the future of work. By examining their most common applications, usage patterns, and other insights, we can understand not just where AI is making an impact but how it’s reshaping team structures, decision-making, and the division of labor between humans and digital coworkers.
Let’s get into it.
Let’s begin with what agentic AI is.
Agentic AI was first developed in the 2000s when machine learning (ML) models allowed agents to learn and improve using large databases. Today, the agentic AI landscape is based on advanced autonomy, enmeshed with an ethical and responsible AI-controlled environment.
Although we at G2 use the term AI agents to refer to “software systems that can reason, act, and automate autonomously,” this is by no means the only current or accepted definition, explains Matthew Miller, research principal at G2.
Largely, AI agents are autonomous digital workers that use tools to accomplish goals. These agents have the ability to remember across tasks and changing states, according to BCG.
But what does this look like in practice? To understand their impact, let’s examine an industry that’s already seeing AI-driven transformation: content marketing.
The human marketing manager assigns the AI agent a vague scenario: create a blog post on the latest digital marketing trends. The agent then researches for it, submits a graphic design request, drafts a blog, and stages it on a content management system. The manager finally steps in to review it before the agent schedules it for publication. It also creates and schedules social media posts for promotions.
While this example demonstrates the autonomous capabilities of agentic AI, many people might wonder how these systems differ from the AI assistants they're already familiar with. After all, hasn't AI been helping with content creation for some time now? This raises an important question about the distinction between truly agentic systems and their more limited predecessors.
Tim Sanders, vice president of research insights at G2, believes there’s a gradient to agentic AI. “I like to call it the Waze-to-Waymo continuum.”
He adds that the entry point to this spectrum is co-pilots or chatbots. And that the top of this continuum is occupied by “system of agents.” “Agents here, on the top level, span across systems and collaborate and deliver on your intent and not just your stated need,” clarifies Tim
Source: LinkedIn post by Tim Sanders
He also argues that agentic AI is a big deal as it solves the delivery problem users and organizations might face with chatbots and automation, “The delivery problem is that individual productivity gains don’t necessarily translate into organizational increased velocity of outcomes.”
For example, human content writers might save 30% of their time using writing assistants, but they might squander it on taking a nap, scrolling through social media, or attending more meetings.
As users of AI agents — the persona is still evolving as sellers experiment with applications — have begun integrating them into their workflows, certain patterns have emerged. These indicate how users prefer to use these agents and their likes and dislikes, which in turn shows the impact AI agents have had on work. Let’s discuss them based on user reviews.
To understand the use of AI agents, we analyzed 3,621 reviews made by verified users worldwide on G2. We found that agentic AI is most commonly used in computer software, IT, and services industries, followed by financial services.
These agents are also widely accessible and beneficial for businesses of all sizes, not just large enterprises.
“AI agents are not just for the big established companies. According to G2 reviews from the past 12 months for AI agents, over half (55%) of reviews are from small businesses.”
Matthew Miller
Research principal at G2
Additionally, the fact that “AI chatbots”, “AI agents”, and “chatbots” are the most popular AI categories in terms of traffic on G2 testifies to the growing interest in agentic AI among users.
After analyzing 500 user reviews between September 2024 and February 2025, we find that 87% are positive, while just 4.6% are negative. This implies significant satisfaction levels and the resulting adoption of AI agents, which are still in their early forms.
Let’s understand the user sentiment more deeply and its implications for both buyers and sellers of AI agents.
The emphasis on ease of use is a driving factor in agentic AI’s adoption. The democratization will accelerate its use across sectors and functions and reach non-technical users.
Users also value customer support, impressing the human-AI collaboration in onboarding and launching agentic AI solutions.
Stressing the decreasing time to ROI, Yukta Rustagi, a market research analyst at G2, adds, “This also implies that organizations leveraging AI are gaining a competitive edge through efficiency and innovation. It reinforces the belief that AI agents are now a more immediate and impactful investment for businesses.”
ROI for AI Agents category (Jan 2023 - Dec 2024)
Source: G2 Market Research
Users have highlighted feature limitations in AI agents. But this doesn’t necessarily indicate functionality gaps. Instead, the technology’s infancy comes to the fore, revealing that we’re still developing shared ideas of what agentic systems should accomplish across domains.
Solutions for individual consumers can be built on general features that serve multiple needs. However, enterprises need specialized solutions that solve problems at scale. Thus, AI agents for larger companies must target specific use cases such as coding, inventory management, and lead qualification.
AI agents present a customization challenge, unlike that seen with previous AI uses. They are emerging as adaptive systems that personalize through interaction rather than configuration. Thus, companies may not need to customize AI agents, which are considered digital coworkers, any more than they customize human colleagues.
Some users struggle with training AI models, which might hobble adoption and delay the time to ROI. AI agents, as autonomous solutions, must need minimal training. They must adapt to users, not vice versa.
The learning curve will remain steep if users continue to think in the command-execution paradigm with AI agents, which operate through goal delegation and autonomous planning. Both sellers and buyers must reimagine onboarding as a collaborative alignment between humans and agents.
Cost concerns among some users indicate low market maturity as businesses struggle to quantify agentic AI value without standardized metrics. Sellers have yet to generate enough case studies and predictable use cases to build a stronger business case for AI agents.
While AI agents are being used across industries, our analysis of G2 reviews reveals that customer experience is the area where their impact is most pronounced. Nearly half of the user reviews mention CX-related improvements, making it a natural focus for understanding the real-world benefits of agentic AI.
While analyzing reviews for AI agents, we found that 217 of them, or 43%, mention customer experience, making it arguably the most common area of impact for companies today.
Echoing the sentiment, Tim believes agents are the most employable in two use cases so far: customer support and sales development.
“Customer support teams currently face high backlogs, which agentic systems can quickly reduce. Sales development leaders see little downside to agentic SDRs, given their hard-to-fulfill quotas of lead generation, booked meetings, and addition to the pipeline,” he explains.
“In the next 10 years, AI in CX will reduce the cost to serve by an order of magnitude, enabling brands to expand touchpoints with customers in a way that has never been possible.”
Jason Maynard
Chief technology officer of AMER and APAC at Zendesk
Matthew has found that agentic AI has had the biggest impact on the customer journey around the consideration phase. “Although the readiness of buyers to rely on agents is increasing, purchasing still remains a pain point,” he claims.
Currently, 30% of consumers would work with an AI agent for faster service. “We predict that this will increase as the systems become more reliable and as users and businesses develop more trust toward the systems,” he says.
Most positive reviews on G2 highlight AI's efficiency in customer support and automation. Whereas negative mentions (4.6%) include concerns about AI errors, slow support response, and lack of customization.
To support our findings on AI agents use cases, we asked two users how their experience with AI agents had been in enhancing customer experience, a popular use case:
G2 Icons are our most prolific contributors and power users of the G2 platform.
User 1: Peter Gill, freight broker
How do you use AI agents to enhance customer experience?
We must understand customer requirements regarding pickup points, delivery points, and commodity types to provide them with the most competitive rates and high-quality services.
Earlier, we used to do this manually. Now, using AI-driven chatbots and virtual assistants, we can offer instant responses to customer inquiries, reducing resistance in the sales funnel. Additionally, AI helps us examine customer behavior patterns, enabling us to understand them better so that we can get repeat business from them.
Result of agentic AI interventions:
User 2: Luca Piccinotti, growth marketer
How do you use AI agents to enhance customer experience?
We use AI agents to analyze market trends, segment target audiences, and forecast customer behavior, which in turn supports the development of precise messaging and tailored campaigns.
Result of Agentic AI interventions:
At first glance, AI’s most immediate impact seems to be on customer experience — handling support queries, automating tasks, and improving engagement. But what happens when these same AI capabilities are turned inward?
Just as AI is transforming different industries, it’s also redefining how work gets done, how teams collaborate, and what it means to be productive. As AI agents move beyond customer support and into core business operations, their role in the workplace is becoming impossible to ignore.
Agentic AI isn’t just about mere automation but cognitive reallocation. It’s creating a new way to look at the division of labor where humans are elevated to higher-order thinking roles.
To understand this real-world impact, we turn to industry experts who have observed AI agents in action, offering valuable insights into how businesses are integrating them, where human oversight is still crucial, and what skills will be needed in this AI-driven future.
Here’s what they have to say:
Some peddlers of AI agents are branding them as digital employees, others as teammates, and others still as tools that stand behind users as opposed to between them, says Matthew.
To this, Tim adds, “They require less human-in-the-loop efforts than LLM chatbots. We should think of these agents as team members that never take time off, get distracted, or develop bad attitudes.”
Mark Purdy, director of Beacon Thought Leadership, says AI agents also perform a variety of specialized functions. For example, agents can gather information from multiple internal databases and external knowledge sources, assessing and synthesizing the insights for business analysts, lawyers, scientists, or other knowledge workers.
AI agents can understand different business problems and contexts, triggering actions and workflows that reduce the strain on human workers,” points out Mark. For example, AI agents can assess email traffic from customers or clients, automatically responding to queries or complaints. They can monitor and follow up on sales leads.
“AI agents can help human managers and leaders make better decisions by running different scenarios or simulations to show the outcomes of alternative courses of action.”
Mark Purdy
Director of Beacon Thought Leadership
In this sense, AI agents can serve as informal sounding boards for different decision-makers, whether at the manager or board level.
However, Mark also emphasizes that while AI agents can act autonomously, there nearly always needs to be some human-in-the-loop element to avoid mistakes or unethical decisions.
When asked how organizations must divide tasks between agents and humans, Mark says the degree of human involvement will depend on many factors. These include the decision's importance, the degree of trust in the AI agent’s recommendations, the consequences of a mistake, and the human worker's experience and judgment.
“For example, there will likely need to be a high degree of human supervision in areas such as healthcare or defense where the consequences of mistakes by AI agents could be very significant, and probably less need in areas such as customer service or back-office processing,” says Mark.
Echoing the sentiment, Sreelesh Pillai, co-CEO at Zepic, says that the company’s AI agents operate independently, mimicking and amplifying human capability while allowing businesses to configure human involvement where necessary.
Leandro Perez, CMO for Australia and New Zealand at Salesforce, points to a productivity challenge facing humans. “For instance, for Australia, the productivity growth has fallen to 30th out of 35 comparable countries,” he says.
“Overworked employees need tools that help them work more efficiently.” And this is where tools like Agentforce, Salesforce’s agentic AI solution, come into the picture.
Leandro cites the example of Fisher & Paykel, one of Salesforce’s customers, which has saved over 3,300 hours monthly by reimagining their processes through AI agents and automation.
“Agentic AI isn't just about efficiency; it’s about unlocking potential with limitless digital labor…you can expect to see every employee leading or working alongside teams made up of AI agents and learning to extract maximum value from them.”
Leandro Perez
CMO for Australia and New Zealand at Salesforce
Leandro argues that the agentic AI shift isn’t necessarily about creating entirely new roles but rather about evolving existing ones. With AI agents handling routine tasks, humans will become orchestrators of intelligence.
He emphasizes strategic thinking, empathy, and resilience as skills that will become more important with the involvement of AI agents.
According to Jason, CX teams must reskill in a similar way to marketing teams in the 2010s to reduce the cost to serve. “As search and social emerged as dominant channels for demand generation, the “technical marketer” became indispensable: part system integrator, part data analyst, and part marketing strategist,” he says.
CX will see the same transition with the need for technical CX professionals who can design the foundational components of AI agents — knowledge, policies, procedures, and systems that support AI agents. “They will then use qualitative and quantitative data to continually improve and optimize these systems,” he says, adding that such skills will command a premium in the job market.
As professionals adapt to this AI-driven shift, their roles will evolve rather than disappear. However, with AI taking on more tasks, a pressing question emerges: what does this mean for the human workforce?
Well, not really.
Agentic AI can, in theory, function autonomously and take over entire processes and systems. But will this integration be at the expense of human workers?
Worker concerns about AI are both justified and misplaced, believes Kate O'Neill, founder and chief tech humanist at KO Insights. “The threat isn't that AI will replace humans wholesale — it's that we might fail to reimagine work in ways that leverage uniquely human capabilities alongside AI.”
She claims that smart companies are already reframing AI from a replacement technology to an enhancement technology. “This isn't just semantic gymnastics; it's a fundamental shift in how we design and deploy these systems,” she adds.
“The future of work isn't a zero-sum game between humans and machines. It's about creating synergies that make both more capable, more productive, and ultimately, more human.”
Kate O'Neill
Founder and chief tech humanist at KO Insights
Kate calls upon AI agent vendors to design their tools explicitly as human amplifiers, not human replacements. That means building tools that enhance human judgment, creativity, and emotional intelligence — the very qualities that make us uniquely human.
Stressing synergy, Sreelesh says the most exciting shift isn't just in creating new AI-specific jobs; it's in how AI transforms existing roles into their “augmented” versions or the next-strategic evolutions of them.
He predicts that as agentic AI becomes more common, we'll see customer service representatives become insight-driven engagement specialists, marketers become customer journey architects, and operations folks become automation strategists.
On a similar note, Jason suggests that agentic AI will spawn highly skilled jobs unseen in CX so far. These will focus on designing and developing the foundations that support AI agents.
He claims that historically, human agents have been asked to be the “glue” across systems and knowledge sources — collecting relevant knowledge and navigating a web of backend systems to solve problems with orders, products, and services.
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However, today, AI agents connected to knowledge sources, systems, and tools are enabling teams to automate these repetitive steps and focus on monitoring the quality and accuracy of responses, providing judgment on the next steps, and approving actions where judgment is needed.
As agentic AI evolves, both sellers and buyers must implement them in a human-centric way. Intrinsic motivation, a key driver for employees, can take a hit if agentic AI is allowed to take over tasks that have provided workers with a sense of mastery and purpose. It’s no more about skill adaptation but reconstructing a professional identity for workers as work gets divided between them and AI agents.
Agentic AI is also bound to flatten traditional hierarchies within organizations. Junior professionals armed with AI agents can perform at senior levels, compressing years-long learning curves into months. This creates unprecedented workforce agility. In addition, senior professionals who’ve traditionally prided themselves on their information mastery must redefine their brand.
AI agents work across systems. They don’t take breaks, go on vacation, or need motivation to perform tasks. In some way, they are tireless and timeless. How safe is customer data with this novel application of AI? And what should a governance framework for agentic AI look like?
In the movie “2001: A Space Odyssey,” the story of the autonomous robot HAL 9000 takes an ominous turn. It goes rogue, doubting humans, blaming them for errors, and disconnecting life support.
The fate of humans with agentic AI is unlikely to be similar, at least in the near future. Before the technology becomes truly autonomous, humans must establish guidelines, check for potential hallucinations, and protect data.
According to Kate, the most pressing ethical concerns around agentic AI go beyond surface-level automation issues to fundamental questions of trust and decision-making authority.
“The core ethical challenge around Agentic AI isn't about algorithms or automation — it's about power.”
Kate O'Neill
Founder and chief tech humanist of KO Insights
Who controls these decisions? How do we ensure customers retain meaningful agency?
“Every time an AI agent makes a choice, it's essentially making a small prediction about human behavior and preference. Get enough of these micro-decisions wrong, and we're not just failing at customer service — we're undermining human autonomy. The stakes are higher than most companies realize,” warns Kate.
The solution? We need unprecedented levels of transparency with agentic AI. “Customers need to understand not just that they're interacting with AI, but how and why these agents make specific decisions,” suggests Kate.
As we progress along the agentic AI gradient, agents will work with each other.
According to Tim, there are a few risks to watch for when this happens: they often need to exchange credentials to actually perform tasks within a multi-step process. That could pose security risks as not all agentic platforms have the same level of trustworthiness.
“Reasoning errors (think hallucinations) have exponential impact as they spread across agentic teams,” says Tim. “Think of how statements can get distorted as repeated across a chain of human beings.”
Responding to these concerns, leading AI agent sellers Salesforce and Zendesk claim their solutions feature security plug-ins beyond those traditionally deployed for AI tools. They say humans still control the wheel, customer data is safe, and workplaces are metamorphosing into more connected and productive spaces.
“Context is the king for accurate, personalized AI outputs,” says Leandro. “Without real-world data about your business and your customers, agent responses are generalized or, worse, rely on hallucinations and guesswork. Data is essential, but so is its secure and ethical handling.”
He explains that they developed the Einstein Trust Layer at Salesforce, which secures and anonymizes data to prevent leaks. “Transparency is also built into Agentforce. Those with digital labor on their teams can easily review the reasoning behind agent outputs and define the scope of agent responsibilities in natural language,” he adds.
In Zendesk's case, the human-in-the-loop approach is integral to using agentic AI.
Jason explains the approach and says they have configurable thresholds that allow human agents or administrators to review and approve AI-generated content and suggested actions.
“Any high-risk action, like issuing refunds or making account changes, can be configured to always have a human operator review and confirm it,” he adds.
As for governance around AI agents, Kate says, "Stop waiting for perfect regulations — they won't come. Instead, build governance frameworks that put human outcomes first."
“Yes, document your processes. Yes, establish clear accountability. But the real work is creating systems that amplify human potential rather than just automate human tasks.”
“Your ethics board should look like your customer base, not your executive team. Bring in the skeptics, the philosophers, the social scientists — and most importantly, representatives from the communities your AI systems will affect."
Kate O'Neill
Founder and chief tech humanist at KO Insights
She believes that the companies that thrive won't be the ones with the most sophisticated AI — they'll be the ones who built the most thoughtful guardrails around it.
The key skill of the future isn't writing prompts or managing AI — it's the ability to collaborate with AI to solve increasingly complex challenges, believes Sreelesh.
Marshall McLuhan, a Canadian communications theorist, was prescient in his observation when he said, 'We shape our tools, and thereafter, our tools shape us.”
That's exactly what's happening with AI. We've created these tools to enhance our capabilities, and now they're reshaping how we work, think, and solve problems, says Sreelesh. It’s altering how intelligence itself operates across organizations.
Having the most advanced AI won't guarantee success — what will set organizations apart will be their ability to balance human and artificial judgment prudently.
This requires developing a way to orchestrate human intelligence and emotions into agentic AI-driven decision-making processes. This will not only delight customers but help enhance employee experience.
With inputs from Yukta Rustagi, Matthew Miller, and Brett Nehls of G2.
Did you know G2 has its own AI SDR with an average conversion rate of 30%? Try it out here.
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