We’ve been watching enterprises wrestle with the identical customer support paradox for years: They’ve all of the know-how on the planet, but a easy handle change nonetheless takes three days. The issue isn’t what you assume—and neither is the answer.
Final month, I watched a colleague attempt to replace their handle with their financial institution. It ought to have been easy: log in, change the handle, finished. As a substitute, they spent 47 minutes on maintain, bought transferred 3 times, and was informed the change would take “3–5 enterprise days to course of.” That is 2025. We’ve got AI that may write poetry and remedy advanced math issues, but we will’t replace an handle area in actual time.
This isn’t a narrative about incompetent banks or outdated know-how. It’s a narrative about one thing extra basic: the hidden arithmetic of enterprise friction.
The Invisible Math That’s Killing Buyer Expertise
Each enterprise course of has two numbers that matter: T and n.
“T” is the theoretical time it ought to take to finish a activity—the perfect-world state of affairs the place every part works easily. For an handle change, T is likely to be 30 seconds: confirm identification, replace database, affirm change.
“n” is every part else. The ready. The handoffs. The compliance checks. The system incompatibilities. The human bottlenecks. “n” is why that 30-second activity turns into a 47-minute ordeal.
In accordance with Forrester, 77% of consumers say that valuing their time is crucial factor an organization can present. Aberdeen Group discovered that firms with glorious service obtain 92% buyer retention in comparison with simply 33% for poor performers. But most enterprises are nonetheless optimizing for compliance and danger mitigation, not buyer time.
The outcome? An enormous “T+n” drawback that’s hiding in plain sight throughout each business.
Why Every part We’ve Tried Has Failed
We’ve seen enterprises throw tens of millions at this drawback. Higher coaching applications. Course of reengineering initiatives. Shiny new CRM techniques. Digital transformation consultants promising to “reimagine the shopper journey.” These efforts sometimes yield 10%-15% enhancements—significant however not transformative. The issue is architectural. Enterprise processes weren’t designed for velocity; they have been designed for management.
Contemplate that handle change once more. In the true world, it entails:
- Id verification throughout a number of techniques that don’t discuss to one another
- Compliance flagging for anti-money-laundering guidelines
- Danger evaluation for fraud prevention
- Routing to specialised groups primarily based on account kind
- Handbook approval for any exceptions
- Updating downstream techniques in sequence
- Creating audit trails for regulatory necessities
Every step provides time. Extra importantly, every step provides variability—the unpredictable delays that flip a easy request right into a multiday saga.
When AI Brokers Really Work
We’ve been experimenting with agentic AI implementations throughout a number of enterprise pilots, and we’re beginning to see one thing completely different. Not the standard marginal enhancements however a real transformation of the shopper expertise.
The important thing perception is that clever brokers don’t simply automate duties—they orchestrate total processes throughout the three dimensions the place latency accumulates.
Folks issues: Human brokers aren’t accessible 24-7. They’ve specialised expertise that create bottlenecks. They want coaching time and occasional breaks. Clever brokers can deal with routine requests across the clock, escalating solely real edge instances that require human judgment. One monetary companies firm we labored with deployed brokers for card replacements. Normal requests that used to take 48 hours now full in below 10 minutes. The shopper varieties out their request, the agent verifies their identification, checks for fraud flags, orders the substitute, and confirms supply—all with out human intervention.
Course of issues: Enterprise workflows are designed as sequential approval chains. Request goes to analyst, analyst checks compliance, compliance routes to specialist, specialist approves, approval goes to achievement. Every handoff provides latency. Clever brokers can prevalidate actions in opposition to encoded enterprise guidelines and set off solely important human approvals. As a substitute of six sequential steps, you get one agent analysis with human oversight just for real exceptions.
Know-how issues: The common enterprise runs buyer information throughout 12–15 completely different techniques. These techniques don’t combine effectively, creating information inconsistencies and guide reconciliation work. As a substitute of requiring costly system replacements, brokers can orchestrate current techniques by APIs and, the place APIs don’t exist, use robotic course of automation to work together with legacy screens. They preserve a unified view of buyer state throughout all platforms.
The AI Triangle: Why You Can’t Optimize Every part
However right here’s the place it will get fascinating—and the place most implementations fail.
Via our pilots and outcomes, we found what we name the AI Triangle: three properties that each agentic AI system should stability. Much like the CAP theorem in distributed techniques (the place you possibly can’t have good consistency, availability, and partition tolerance concurrently), the AI Triangle forces you to decide on between good autonomy, interpretability, and connectivity. Simply as CAP theorem shapes how we construct resilient distributed techniques, the AI Triangle shapes how we construct reliable autonomous brokers. You possibly can optimize any two of those properties, however doing so requires compromising the third. It is a “choose 2 of three” state of affairs:
Autonomy: How independently and shortly brokers can act with out human oversight
Interpretability: How explainable and audit-friendly the agent’s choices are
Connectivity: How effectively the system maintains real-time, constant information throughout all platforms
You possibly can choose any two, however the third suffers:
Autonomy + interpretability: Brokers make quick, explainable choices however could not preserve good information consistency throughout all techniques in actual time.
Interpretability + connectivity: Full audit trails and ideal information sync, however human oversight slows every part down.
Autonomy + connectivity: Lightning-fast choices with good system synchronization, however the audit trails may not seize the detailed reasoning compliance requires.
This isn’t a know-how limitation—it’s a basic constraint that forces deliberate design selections. The enterprises succeeding with agentic AI are people who consciously select which trade-offs align with their enterprise priorities. This isn’t a technical resolution—it’s a enterprise technique. Select the 2 properties that matter most to your clients and regulators, then construct every part else round that selection.
The Hidden Prices No one Mentions
The seller demos make this look easy. Actuality is messier.
Information high quality is make-or-break: Brokers performing on inconsistent information don’t simply make errors—they make errors at scale and velocity. Worse, AI errors have a distinct signature than human ones. A human would possibly transpose two digits in an account quantity or skip a required area. An AI would possibly confidently route all Michigan addresses to Missouri as a result of each begin with “MI,” or interpret each occasion of “Dr.” in road addresses as “physician” as an alternative of “drive,” creating addresses that don’t exist. These aren’t careless errors—they’re systematic misinterpretations that may cascade by hundreds of transactions earlier than anybody notices the sample. Earlier than deploying any autonomous system, you could grasp information administration, set up real-time validation guidelines, and construct anomaly detection particularly tuned to catch AI’s peculiar failure modes. This isn’t glamorous work, but it surely’s what separates profitable implementations from costly disasters.
Integration brittleness: When brokers can’t use APIs, they fall again to robotic course of automation to work together with legacy techniques. These integrations break at any time when the underlying techniques change. You want strong integration structure and event-driven information flows.
Governance will get advanced: Autonomous choices create new dangers. You want policy-based entry controls, human checkpoints for high-impact actions, and steady monitoring. The governance overhead is actual and ongoing.
Change administration is essential: We’ve seen technically good implementations fail as a result of staff resisted the adjustments. Profitable deployments contain employees in pilot design and clearly talk how people and brokers will work collectively.
Ongoing operational funding: The hidden prices of monitoring, retraining, and safety updates require sustained funds. Issue these into ROI calculations from day one.
A Roadmap That Really Works
After watching a number of implementations succeed (and others crash and burn), right here’s the sample that persistently delivers outcomes:
Begin small, assume huge: Goal low-risk, high-volume processes first. Guidelines-based operations with minimal regulatory complexity. This builds organizational confidence whereas proving the know-how works.
Basis earlier than options: Construct integration structure, information governance, and monitoring capabilities earlier than scaling agent deployment. The infrastructure work is boring however important.
Design with guardrails: Encode enterprise guidelines—it’s preferable to maneuver them right into a coverage retailer in order that brokers can get them executed at run time utilizing a coverage resolution level (PDP) like Open Coverage Agent (OPA), implement human checkpoints for exceptions, and guarantee complete logging from the start. These constraints allow sustainable scaling.
Measure relentlessly: Monitor probably the most essential metrics in operations with a give attention to decreasing “n” towards zero:
- Common dealing with time (AHT)
- Straight-through processing charge (STP Fee %)
- Service stage settlement (SLA) efficiency
- Buyer satisfaction
- Price per transaction
These metrics justify continued funding and information optimization.
Scale steadily: Broaden to adjoining processes with larger complexity solely after proving the inspiration. Concentric circles, not huge bang deployments.
The Expertise That Modifications Every part
We preserve coming again to that colleague making an attempt to alter their handle. In a world with correctly applied agentic AI, right here’s what ought to have occurred:
They log into their banking app and request an handle change. An clever agent instantly verifies their identification, checks the brand new handle in opposition to fraud databases, validates it with postal companies, and updates their profile throughout all related techniques. Inside seconds, they obtain affirmation that the change is full, together with up to date playing cards being shipped to the brand new handle. No cellphone calls. No transfers. No ready. Simply the service expertise that matches the digital world we truly reside in.
The Greater Image
This isn’t actually about know-how—it’s about lastly delivering on the guarantees we’ve been making to clients for many years. Each “digital transformation” initiative has promised quicker, higher, extra customized service. Most have delivered new interfaces for a similar outdated processes.
Agentic AI is completely different as a result of it might truly restructure how work will get finished, not simply the way it will get offered. It could possibly flip T+n again into one thing approaching T.
However success requires greater than shopping for software program. It requires rethinking how organizations stability velocity, management, and danger. It requires investing within the unglamorous infrastructure work that permits clever automation. Most significantly, it requires acknowledging that the way forward for customer support isn’t about changing people with machines—it’s about orchestrating people and machines into one thing higher than both may obtain alone.
The know-how is prepared. The query is whether or not we’re ready to do the arduous work of utilizing it effectively.
