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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchIn Gladly’s framework, AI maturity in customer experience progresses through six levels: Static Automation, Conversational AI, Agentic AI, Contextual AI, Adaptive AI, and Discerning AI. The levels describe a shift from scripted answers toward systems that use customer context, take actions, adapt, and handle exceptions. They are Gladly’s vendor-authored categories—not a universal standard or an independently validated scorecard—and a higher-level label does not prove better customer outcomes.
What the six levels describe
Gladly introduced its six-level framework in a guide published August 5, 2025. The official page frames AI as a spectrum; the detailed descriptions below are from Gladly’s 11-page guide, available as a converted copy hosted by Manuals+. The stages are useful for discussing capabilities, but the framework does not provide a standardized assessment instrument or cross-vendor validation.
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| Level | What the system does | Example or key constraint |
|---|---|---|
| 1. Static Automation | Follows programmed FAQs and preset flows; routes inquiries and collects basic information. | Handles routine volume, but nuanced or consequential issues still need people. |
| 2. Conversational AI | Uses natural-language processing, machine learning, or large language models to interpret free-form requests and conversational context. | Can personalize responses or detect sentiment; vague requests and incorrect generated answers remain risks. |
| 3. Agentic AI | Takes bounded actions, makes limited decisions, and coordinates tools or backend systems. | Could reschedule a delivery; reliable data and integrations are prerequisites. |
| 4. Contextual AI | Uses customer history and other context to shape answers, recommendations, or proactive assistance. | Could alert a customer about a delayed purchase; depends on accurate history and real-time data. |
| 5. Adaptive AI | Adjusts to new information, learns patterns, and, in Gladly’s description, self-corrects. | Could respond to a late delivery with an update and priority tracking; raises resource and explainability concerns. |
| 6. Discerning AI | Uses what Gladly calls intent-aligned exceptions rather than applying rules mechanically. | Could allow a loyal customer to return an item outside the usual window; human judgment remains important. |
1. Static Automation: scripted answers and routing
At the first level, a bot operates within rules and content its designers have set. It may answer a programmed FAQ, collect a name or order number, or direct a customer through a predefined menu. The bounded scope can make routine interactions easier to automate, but the system is not necessarily interpreting a novel question or resolving the underlying issue.
Gladly says complex questions, consequential actions, and nuance still call for human agents at this level. A practical distinction is whether the interaction can be handled reliably by the same known sequence. If the customer’s request falls outside that sequence, the design needs a clear route to a person rather than a dead end.
2. Conversational AI: understanding free-form requests
Conversational AI accepts natural-language input rather than requiring a customer to select only from fixed choices. Gladly’s description includes natural-language processing, machine learning, and LLMs, with capabilities such as retaining conversational context, personalizing replies, and detecting sentiment. Those are capabilities the framework associates with the level, not a guarantee that every product labeled conversational AI performs all of them reliably.
The main change from static automation is flexibility in understanding how people express intent. But understanding language is not the same as being correct: Gladly notes that vague requests can remain difficult and generated answers can be wrong. It recommends keeping the information used by the AI current. Customer-facing systems also need a way to clarify ambiguity or hand off questions they cannot answer safely.
3. Agentic AI: taking bounded actions
Agentic AI goes beyond composing a response: it can use tools, coordinate with backend systems, and complete a defined action. Gladly gives rescheduling a delivery as an example. In customer service, the important distinction is not simply that a bot sounds conversational, but that it can make a change in another system.
That shift makes reliable integrations and data quality essential. An action based on stale or mismatched order information can create a real customer problem, rather than merely an unhelpful answer. Gladly presents AI agents as support for human experts, particularly where empathy is needed—not as a reason to remove human judgment from every interaction.
4. Contextual AI: using customer history and live circumstances
Contextual AI brings relevant customer history and current circumstances into the interaction. Rather than respond only to the latest message, it may use prior interactions or purchase information to tailor a reply, make a recommendation, or proactively share an update. Gladly’s examples include notifying a customer about a delayed purchase or suggesting a relevant product.
Context is only useful when it is accurate, sufficiently complete, and connected to the systems where it lives. Incomplete history can produce irrelevant personalization; outdated real-time information can make a proactive update misleading. The framework therefore ties this level to dependable integrations and accurate historical and live data.
5. Adaptive AI: responding as conditions change
Gladly describes adaptive AI as responding to new real-time information, learning patterns, and self-correcting. Its example is a system that reacts to a late delivery with an update and priority tracking. This is the framework’s description, not a settled technical definition of adaptive AI; its guide also characterizes adaptation in terms that include self-modifying code.
The claimed flexibility brings operational questions. Gladly flags computational demands, specialized hardware and personnel, and the difficulty of explaining decisions made by a system that may behave like a “black box.” For customer service, teams need to understand what information triggered a change and how to correct it when the system’s response is wrong.
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6. Discerning AI: making intent-aligned exceptions
At the highest level, Gladly describes AI that can make exceptions aligned with a customer’s intent rather than apply a policy mechanically. Its example is allowing a loyal customer to return an item after the normal return window. This is an aspirational description of the framework’s top level, not evidence that a particular customer-service product can safely exercise such discretion.
Gladly also emphasizes limits: these systems do not have lived human intuition and cannot independently create wholly new concepts or reliably distinguish plausibility from absurdity. Exceptions can affect money, policy, and customer trust, so organizations must decide which decisions may be automated, which require review, and how to explain the result.
How the levels differ in practice
The ladder’s central progression is from executing preset instructions to handling more context and exercising more autonomy. The distinctions below help separate capabilities that are easy to blur together.
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Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Repair Windows errors before they cause bigger problemsFix Now →Scan for outdated or missing drivers - takes under a minuteDriver Scan →- Conversational versus agentic: Conversational AI interprets and responds to language; agentic AI can also take bounded actions through tools or backend systems. A system may be conversational without having permission or integrations to change an order.
- Agentic versus contextual: Agentic describes doing things; contextual describes using customer history and circumstances to inform what the system says or does. Action without sound context can be the wrong action.
- Contextual versus adaptive: Contextual AI uses relevant information for the current interaction; adaptive AI, as Gladly describes it, adjusts as new information arrives and learns patterns over time.
- Adaptive versus discerning: Adaptation concerns responding to new information or patterns; discernment concerns making exceptions aligned with intent. The latter raises especially direct questions about judgment and policy authority.
How to judge whether a customer-service AI is ready to take action
A maturity label alone cannot establish readiness. Before allowing an AI system to change an order, issue a refund, or make a policy exception, assess the task and its safeguards directly.
- Define the permitted action. Specify the exact decision or change, its boundaries, and cases that must go to a person. “Can resolve support” is too broad to govern consequential actions.
- Check the data it will use. Confirm that customer identity, order status, policy details, and relevant interaction history are accurate and current for the decision.
- Test the integration path. Establish that the system can read the right records and write changes reliably, and determine what happens if a connected service is unavailable or returns conflicting information.
- Set confirmation and escalation rules. Decide when the AI can complete an action, when it should ask the customer to confirm, and when it must transfer the interaction to an agent.
- Make decisions reviewable. Keep enough information about the inputs, action, and outcome for staff to understand and correct a mistake, especially when a decision is difficult to explain.
- Measure outcomes, not labels. Track whether the task is completed correctly and how customers and agents are affected, alongside the scope of automation and escalation. A more autonomous system is not automatically a better one.
How Gladly’s framework compares with Deloitte’s
Deloitte uses a separate four-level model in its March 6, 2026 analysis: basic automation, processing with agents, process reimagination, and organizational reimagination. Its levels emphasize the organizational scope of AI and the degree of process change, whereas Gladly’s six levels describe a progression in customer-experience AI capabilities. The models should not be merged or treated as validating one another.
Deloitte reports results from the Deloitte Center for Integrated Research’s 2025 Tech Value Survey, which had 548 respondents across five industries. Within Deloitte’s categories, 67% of “Transformers” and 61% of “Automators” reported large or very large ROI across all 46 KPIs. It also reports that 73% of Transformers and 69% of Automators frequently or very frequently used all 46 KPIs. These figures describe Deloitte’s survey respondents and its own categories; they do not show that advancing through Gladly’s stages causes ROI or that a specific vendor product produces those results.
Sources
- Gladly, “6 levels of AI maturity to uplevel your CX,” August 5, 2025 — official framing and framework overview.
- Gladly’s 11-page guide, converted copy hosted by Manuals+ — detailed descriptions and examples. The host notes the converted format; diagrams may differ from the original.
- Deloitte Insights, “AI maturity and digital value,” March 6, 2026 — separate maturity model and survey results.
Frequently Asked Questions
What are the six levels of AI maturity in customer experience?
Gladly’s framework names them, in order, Static Automation, Conversational AI, Agentic AI, Contextual AI, Adaptive AI, and Discerning AI.
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What is the difference between conversational AI and agentic AI?
Conversational AI interprets natural-language requests and responds; agentic AI can also use tools or backend integrations to take bounded actions, such as rescheduling a delivery.
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What does contextual AI mean in customer service?
It means using relevant customer history and current circumstances to shape responses, recommendations, or proactive updates. The usefulness of that context depends on accurate data and dependable integrations.
Does a higher AI maturity level guarantee better customer experience?
No. The levels describe capabilities in Gladly’s framework, not a validated outcome score. Reliability, customer impact, oversight, and measured results matter alongside autonomy.
Is Gladly’s six-level model an industry standard?
No. It is a vendor-authored framework. It does not provide a standardized assessment instrument or independent cross-vendor validation.
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