We have reached a point in the hype cycle where "AI" is being sprinkled on enterprise software like a seasoning on a cheap steak: it masks the poor quality of the underlying meat but doesn't make it more nutritious. In the latest CRMKonvo, Bhawani Shankar and the CRMKonvo team tore into the reality of what it actually takes to make "Agentic AI" work in a Customer Experience (CX) environment.

The analysis? Most enterprises are trying to drive a Ferrari without wheels.

Bhawani used this metaphor that I find particularly apt: the AI model is the shiny red car that gets the CEO excited; but the data is the wheels, the engine, and the fuel; and they come as options. If you buy the car without ensuring the wheels are attached and the tank is full of high-octane, verified data, you aren't going anywhere. You are just sitting in an expensive garage making engine noises.

TL;DR

If you want to watch the full CRMKonvo, please go ahead here (optimized for smartphones) or here (optimized for tablets/computers).

Else, be my guest and continue to read.

Or do both …

The Death of the "System of Record"

For decades, we have worshipped at the altar of the "System of Record." The goal was simple: get the data into the CRM. It didn't matter if the data was messy, duplicated, or six months out of date; as long as it was "in the system", leadership was happy. But as Bhawani correctly pointed out, we need to be moving from a system of record to a system of context.

In the old world, a marketing campaign was a monolithic beast planned over months. In the new, agentic world, context changes by the hour. If your AI agent is trying to help a customer but is basing its "reasoning" on a record that doesn't include the tweet they sent ten minutes ago, the click history of the past 5 minutes or the failed login attempt just now, the agent is useless. It isn't just about having the data; it is about having the connected data in a "Golden Context”.

The industry likes to use the term Master Data Management (MDM), but let’s just agree that this is becoming legacy terminology, and that fast. MDM implies a static, central truth. Agentic AI requires something more fluid. It requires an environment where the agent itself can find and connect data points across silos without a human having to manually build a brittle connector for every single API.

The Governance Paradox

Which brings us to the important concept of "dynamic governance." To an analyst, “something fluid” sounds like a polite way of saying "no governance”. Governance, by definition, is supposed to be the set of rules that keep things stable. If the rules are constantly moving, do you actually have a governed system?

The reality is that traditional, top-down, "thou shalt not" governance is dead. The stick does not work anymore. One could say that it never worked anyway; sales teams are stereotypically ungovernable because they are driven by incentives, not policy manuals. They rather ask for forgiveness later than for permission beforehand.

Bhavani’s solution is one I can get behind: incentive-linked, embedded governance. If the system makes it easier for a salesperson to hit their number by providing them with agentic support, they will use it. If the agent automatically transcribes a meeting and populates the CRM, the "governance" of data entry happens as a byproduct of a tool that actually helps the employee. Then, the system serves them, and not the other way round.

However, the human element remains the biggest hurdle. You can cook up the most sophisticated agentic sandwich in the world, but if the humans don't trust the data coming out of it, they will revert to their old spreadsheets and shadow IT "caves”.

Shadow Data: The Enemy Within

We often talk about "shadow IT”, but the real killer for AI is "shadow data”. These are the Excel sheets living on desktops, the manual exports from 2019 that are still being used for "reporting”, and the tribal knowledge that never gets digitized.

If you want to speed up your AI strategy, Bhavani’s advice is simple and straightforward: kill the shadow data. Anything that is not visible, verified, and connected is an impediment to an agent's ability to learn, hence an organizational impediment. An AI agent is only as good as the context it is offered. If half of your customer's context is hidden in a CSV file on a retired manager's hard drive, your "intelligent" agent is effectively flying blind in one eye, if not both.

Three Pillars for the Enterprise AI Buyer: A Reality Check

If you are currently sitting through vendor presentations where a 24-year-old in a slim-fit suit is promising you "Agentic CX Transformation”, here are three things you need to make sure of before you sign the check.

Demand "Golden Context," Not Just "Clean Data"

Stop asking if the data gets clean, and kept so, and start asking if it is connected. An agent doesn't just need a "clean" address; it needs to know why the customer changed that address, what they bought immediately afterward, and if they sounded annoyed on the last support call. If the vendor cannot show you how their AI bridges the gap between your static system of record and your moving system of context in real-time, they are just selling you a fancy search bar.

Verify the Incentive Alignment

Before deploying any AI tool, ask: "How does this make the user’s life easier in the first fifteen minutes?" If the tool requires more manual data entry or tagging to work, it will fail. It just will not be used, regardless of sticks or carrots. The best governance is invisible. You want a system that captures data as a byproduct of work (like meeting transcriptions or automated email logging) rather than one that treats data entry as a separate chore. If the AI doesn't give the employee an immediate win, they will starve it of the data it needs to survive, let alone help them.

Kill the Silos Before You Feed the Agents

Do not attempt to "fix" your data silos with AI. Fix the silos so that AI can function. Bhawani suggests an agentic assessment of your data landscape to identify where the "shadow data" is hiding. If you feed an AI agent data from five disconnected silos, it will hallucinate five different versions of the truth. Clear out the "caves" of manual data and lead everything into a visible, connected stream. Only then will your Ferrari actually have the wheels it needs to move.

The age of the static database is over. The age of the contextual agent is here. Just make sure you aren't the one paying for a shiny red car that is permanently parked.

In the course of the last year or three, I have written a few times about the supply chain and how it affects the customer experience, sometimes badly so. In my last article ‘the impact of the supply chain on the customer experience’, I explained some of the issues and laid out a couple of high level solutions.

Let me elaborate a little on what I wrote then. Of course, there are challenges in two main areas:

• The demand side
• The supply side

The customer expectations are quite simple: A customer expects that things just work, that the vendor, and information given by the vendor, is reliable, accurate and comes timely; that deliveries after order are coming reasonably fast and that there is transparency about the order and delivery status.

This is all quite abstract, so let us fill this with life.

An example to the contrary goes as follows: You buy something, e.g., some whiteware, from a brand with a very high reputation; you are asked for upfront payment (several thousand dollar, remember, high reputation), get an approximate delivery date two months out – along with a warning that there may be a delay of a month or so.

So far, so good. I could live with that although even the two months should be explained …

Now, imagine that one month into the wait time you get an e-mail notifying you about a changed delivery date, pushing it out by a month. Well, not nice, but also not out of the range of expectations. After all you got told so.

However, the next month you get a similar mail. And the next month, and then in some more months.

This is where the customer experience falls apart. What do you do? You call the vendor. Customer service tells you that they do not know when the delivery can happen, and don’t know a reason other than ‘chip shortage’. It is all up there in the clouds (pun not intended).

This is akin to saying: I do not have any insight into my supply chain.

What? How can this happen?

This is quite unhealthy for a business.

Not having insight into the own supply chain can happen generally only under two circumstances

1. Data is not shared internally
2. The suppliers do not provide any data

The first reason can be mitigated pretty easily. It is a matter of internal collaboration – i.e., processes – and then of setting up a proper data exchange between the relevant systems. That way, all organizations have the data at hand that they need to at least fulfill some functions that lie at the base of the pyramid of customer expectations. Still, the experience is poor, as the customer does not only expect timely and accurate information, but actually a delivery at the agreed upon date and time.

The second challenge is not overcome that easily. As I have written earlier, overcoming it also involves a fundamental strategic change. A change away from optimizing the supply chain using minimizing cost as the sole KPI, moving towards a balance between cost, reliability, and speed, therefore addressing the core customer demands at the base of the pyramid in more situations. As we have re-learned in the past two years, a sole focus on cost makes supply chains inherently unstable.

Why re-learned? Because supply chain disruptions are not a new phenomenon. We have seen supply chain disruptions time and again, albeit usually not on a global scale like now. Reasons for these disruptions can range from traffic jams, strikes, vendor-supplier disputes, outages or accidents, up to natural disasters, pandemics or wars.

A second part of this necessary strategic change also involves more and earlier sharing of information with suppliers, so that they can improve their own planning. The other way round, this is true, too: Suppliers need to be willing to share relevant information about their capacities and/or load situation, too.

The third party of the chain are the transportation agents that offer air, ground or sea transportation.

Of course, this also involves a lot of trust and the assurance that this information is not abused in a power play of any kind, or worse, but used for the sole purpose of serving the end customer better by cooperating.

But just to be clear: Not all corners of the triangle cost, speed, reliability can be improved at the same time. Working this triangle is not a silver bullet but will help reducing the likelihood of not being able to deliver.

So, what data needs to be shared, and how, so that the supply chain can run at its optimum at any given time?

There is data on two levels

• Macro
• Micro

On macro level, demand signals need to be made available to the suppliers, so they can plan production (and their own sales) early. Conversely, available capacity data needs to be available to buyers, so they have a chance to distribute their orders amongst a number of suppliers, as needed. At the same time, transportation capacities need to be secured, too. This is, where the transportation agents come into picture. It doesn’t help, if buyer and supplier get into an agreement and the goods cannot get transported.

Again, this requires a lot of mutual trust that the supply can be produced in time and quality, that payments are made in time, and especially, that the provided data is not or cannot be abused. Alternatively, it requires an infrastructure that enables the participants to trust in the infrastructure instead.

On the micro level, it needs the real-time exchange of detail information that enables a just-in-time supply that allows for detailed scheduling and synchronizing work processes and to possibly prepare for contingencies. This may include IoT data out of the network but also external data like weather, traffic, and lots more.

This can easily become an extremely high volume of data that needs to be processed fast to be useful.

All of this might be a good use case for a system that is based on a blockchain that is provided by an independent party or, even better, by a party that all participants have a stake in (the more the better). The blockchain could be used to secure the promises (demands and available capacities).

On the other hand, there are still significant voices (e.g. Bruce Schneier in his readable post on the dangers of cryptocurrencies and the uselessness of blockchain) that ask where the ultimate benefit of a blockchain is, whether it is a solution in search of a problem or whether it solves existing challenges in just another (not necessarily better) way or even question the promise of blockchain security altogether (e.g. this DARPA analysis).

In any case, every scalable transactional system that allows for efficient and automated negotiation and processing should be capable of delivering these services. After all, the juice lies in the business logic and not in contracts being smart contracts or not.

So, what does it take to address the supply chain problem, given businesses have developed a strategy that focuses on a balance between cost, reliability, and speed?

1. A secure (technical) environment that makes it easy for the participants to trust each other or that makes it even possible to replace trust into each other by trust into the environment. This environment must also ensure that the individual participants are not put at a systematic advantage or disadvantage. This requires a strong governance and is in all likelihood implemented by a network of systems that interact based on a standardized protocol.
2. An ecosystem with enough participants on demand, supply and logistics sides that allows for both, the efficiency of scale and the possibility to build flexible networks between suppliers and buyers instead of relying on tight chains. As said above, the participants must not be put at a systematic advantage or disadvantage.
3. Enough and accurate data that is securely collected, managed, bound by consent and purpose; it is to be only used to fulfill the needed services. This data must be equally accessible and usable by all participants (for this purpose).
4. Scalable and transactional and analytical systems that enable the necessary real-time data ingestion and transaction handling. These systems predict demands and/or shortages on macro level and might also help in mitigating micro-level disturbances.

But as said: Trust between all members of the supply chain, from the end customer through the complete value network is key to the customers having a great experience.

Supply chains must evolve into flexible supply networks, based on mutual trust.