Emerging AI Solutions in 2026: Insights from 1,250+ G2 Reviews and 6 Vendor Surveys on Stack Replacement

According to G2’s analysis of over 1,250 verified Emerging AI reviews and survey responses, nearly one in three buyers explicitly mention replacing or consolidating tools. However, every vendor surveyed agrees that AI cannot yet fully replace humans, as human involvement remains essential for tasks such as exception handling, approvals, and one-off judgment calls.

While AI software is often promoted as capable of replacing multiple tools, workflows, and even entire tech stacks, the reality is more complex. G2 surveyed six vendors developing agentic AI, AI agents, and similar emerging AI tools to understand what their products actually replace. The findings reveal that emerging AI software is less “fully autonomous” and more “selectively powerful.” All respondents confirmed that parts of workflows still require human oversight.

Most AI deployments replace some tools, enhance others, and still rely on humans for critical decisions—a crucial consideration for buyers before committing to a contract.

Methodology:
G2 evaluated emerging AI solutions by analyzing over 1,250 reviews from 2026 YTD in the Emerging AI category, alongside vendor research.

Defining Emerging AI Software:
Emerging AI software encompasses products built on foundational AI capabilities, including generative AI, retrieval-augmented generation, and autonomous agents, aimed at transforming or replacing workflows previously managed with multiple tools. This includes AI-native platforms across categories like content management, demand planning, and product analytics, where AI is integrated at the core rather than as an add-on. Companies such as Kanerika and Amplience highlight that their products replaced entire workflows from launch.

Extent of Tech Stack Replacement:
The degree to which emerging AI replaces a tech stack varies depending on the workflow size. For example, Rapidops, Amplience, and invent.ai reported that their workflows initially involved ten or more tools, consolidating between six and ten tools with their AI platforms. Kanerika reported completing 75 to 90 percent of workflows within its platform even when starting with fewer tools. Thus, the number of replaced tools should be understood relative to the initial size of the tech stack.

Two other vendors, Kanerika and Leo AI, reported starting with smaller workflows of three to ten tools and consolidating one to two tools after adoption. Even small consolidations can have significant impacts depending on team needs. Most vendors describe their products as part of a hybrid model—replacing some tools while augmenting others. Leo AI, uniquely, is treated as a layer atop existing tools rather than a replacement.

There’s no doubt that we’re constantly told that AI software can replace existing disparate software, workflows, and even entire tech stacks. Yet the reality of what gets replaced is far more nuanced than vendors typically pitch. But when G2 surveyed six vendors building agentic AI, AI agents, and similar emerging AI tools about what their software actually replaces, a clearer picture has emerged showing emerging AI software as less “fully autonomous” and more “selectively powerful”. Regarding full autonomy, every respondent confirmed that there’s still a part of the workflow that no AI system can handle on its own, such as making one-off judgment calls and handling exceptions.

Most deployments replace some tools, augment others, and still need humans for key decisions. Buyers need to know this before signing a contract.

What counts as “emerging AI software” right now?

For this report, we treat emerging AI software as products built around foundational AI capabilities, such as generative AI, retrieval-augmented generation, and autonomous agents, that are actively transforming or replacing a workflow a buyer used to run with multiple tools.

This software goes beyond AI agents or agentic AI because it covers AI-native platforms inside categories like content management, demand planning, and product analytics that have rebuilt their core workflow around AI, rather than adding AI onto an existing tool. Both Kanerika and Amplience make this clear by describing that their products entered the market as a system replacing an entire workflow from day one.

How much of your tech stack does emerging AI actually replace?

The honest answer is that it depends on how big the workflow is to begin with, and our data shows two distinct patterns rather than one stand-out trend.

Three respondents, Rapidops, Amplience, and invent.ai, reported that their main workflow required 10 or more discrete tools before their product was adopted, such as BI and analytics, workflow automation, internal dashboards, and custom-built internal tools. After adoption, all three respondents reported consolidating 6 to 10 of those tools into their platform.

This doesn’t necessarily mean it’s a weaker result. Kanerika‘s product typically entered a workflow with fewer pre-existing tools to begin with, and the company says it completed 75 to 90 percent of the workflow within its own platform, regardless of the initial tool count. This data means that the number of tools replaced by highly rated emerging AI tools is superficial unless it’s read and measured against the size of the tech stack to begin with.

“We entered as a transformation partner that evolved from solving specific workflow and integration challenges into delivering end-to-end AI-driven systems across functions.”

Sourabh Gurga
Marketing Manager, Rapidops

“What previously required multiple tools and manual handoffs is now managed within a single, continuous decisioning loop. Planners shift from manually creating and adjusting decisions to supervising outcomes and handling exceptions.”

Hayley Brown
Content & Communications Strategist, Invent.AI

Two other survey respondents, Kanerika and Leo AI, both noted smaller starting workflows (3 to 10 tools) and reported consolidating only 1 to 2 tools after adoption. However, even consolidating or updating one workflow can make a big difference depending on the needs of a specific team. 

When asked how customers position their product internally, every vendor described a hybrid model, replacing some tools while augmenting others. Leo AI was the only exception, with customers treating it as a layer on top of existing tools rather than a replacement.

In fact, based on 1,250+ verified G2 reviews from the Emerging AI category, 29%, or nearly one out of every three reviewers, mention replacing and/or consolidating their current tools with AI.

“One customer initially switched from ChatGPT to Leo because they needed higher fidelity technical sources they could actually trust.”

Maor Farid
CEO & Co-Founder, Leo AI

Why do emerging AI implementations fail to meet expectations?

According to our respondents and G2 review data, users of emerging AI applications in 2026 commonly underestimate and overestimate how it can affect their workflow.

Emerging AI is marketed and intended to change and/or replace workflows, and this change management becomes a complex project. Often, buyers assume using the technology is the hard part when, in fact, the implementation becomes more challenging. Because the platforms can be so comprehensive, users can’t just ‘plug and play’ in a few minutes. The AI needs to be taught the specific business logic of the company, and this initial setup can especially feel overly complicated for a smaller team with fewer resources and less experience with this type of technology. Our survey response data confirms that 67% of them named workflow redesign or change management as the issue that customers most commonly underestimate.

Potential buyers also overestimate what is possible with emerging AI software. Kanerika’s Sushree Swagatika says customers most commonly overestimate “reduction in human involvement” – the expectation that AI will eliminate headcount or fully automate human-dependent steps. Users often assume that one AI platform will clean up their tech stack by replacing all tools, making them redundant. This isn’t to say emerging AI products are failing their customers; rather, customer expectations don’t align with what these tools actually deliver.

“Customers most commonly overestimate reduction in human involvement.”

Sushree Swagatika
B2B Marketing, Kanerika

What this means for buyers evaluating emerging AI solutions in 2026

First and foremost, buyers need to keep in mind that emerging AI software can consolidate tools, but the number of tools is uneven. Depending on the size of the workflow, a vendor able to replace a vast workflow with 10 tools and another company with a lean workflow of only 2 tools can both be successful in integrating emerging AI software, albeit with different goals. 

Second, full autonomy won’t happen as of now, and vendors cite that human involvement is still needed for exception handling, approvals, or one-off judgment calls. And lastly, buyers can’t underestimate the financial and time investment needed just for implementation. 

Regardless of the company, team, or workflow size, any emerging AI software will need to be taught about existing workflows and specific business logic. As emerging AI software continues to evolve, buyers should evaluate these platforms not only for what they can automate today, but also for how well they can adapt to future business needs. Organizations that invest in scalable AI solutions and thoughtfully redesign their workflows will be better positioned to unlock greater efficiency and long-term value as the technology matures.

Explore the top-rated Emerging AI tools on G2 and see how verified buyers are evaluating them.