aheadCRM - Blog #Value

The Uncomfortable Truth About Enterprise AI in 2026: It's Not Intelligence, and That's a Problem

Mon, 16 Mar 2026 21:21:55 -0400

As enterprises scramble to deploy AI, the Great AI Debate’s eighth installment reveals a widening gap between what vendors are selling and what actually works at scale. Dr. Michael Wu and Jon Reed spent this episode cutting through the hype around language models, domain expertise, and the financial reality of building sustainable AI systems; and they didn’t pull punches about where the field is failing.

TL;DR

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

https://youtube.com/live/oVw5GqXaviA?feature=share

Else, be my guest and continue to read.

Or do both …

The Domain Expertise Imperative: Correlation is Not Causation

One of the most dangerous, and frankly lazy, narratives pushed by AI maximalists is the idea that artificial intelligence negates the need for deep domain expertise. This is a fundamental misunderstanding of how these models work.

As Dr. Michael Wu frequently points out, almost all machine learning and AI systems today are built using supervised or reinforcement learning. They are, at their core, sophisticated correlation engines. They do not understand causality. They can surface 50 variables that move together, but they cannot tell you whether A causes B, B causes A, or if a hidden confounding variable C is responsible for both.

If an LLM correctly states that smoking causes cancer, it is not because it understands the biological mechanisms of cellular mutation; it is because it has been fed enough human-generated text asserting that relationship. It creates the illusion of causal reasoning without the substance.

This is precisely why domain experts, whether in healthcare, supply chain logistics, or financial services, are more vital than ever. The AI can process the data at unprecedented scale, but it takes a human domain expert to identify spurious correlations, recognize hidden causative factors, and make the final judgment calls. The machines are there to augment the experts, not replace them. So far, they simply can’t.

Deconstructing the Anthropomorphic Illusion

We must also fiercely reject the anthropomorphizing of AI. It is a fundamental human flaw to project human traits onto inanimate objects. It’s a psychological quirk that leads people to form emotional attachments to digital chatbots or, absurdly, even "marry" them because a machine, unlike a human, requires no compromise and has no expectations.

In the enterprise, this anthropomorphism manifests in the careless misuse of terminology. We call these systems "intelligent," giving ourselves an excuse to let our guard down. We use terms like "grounding" to imply that an LLM has a fundamental anchor in truth.

Let’s be clear: LLMs are not inherently "grounded." You can feed them contextual data, such as a causal graph or a vector database via Retrieval-Augmented Generation (RAG), to influence their outputs at inference time. This is valuable and important, but it is not true grounding. The underlying architecture remains a Transformer: a probabilistic, non-deterministic engine that samples from a distribution to guess the next word. If you ask it the exact same question twice, chances are it gives you two different answers. Heck, chances are that the answer is plain wrong. That’s why they all have a disclaimer. It is not reasoning; it is calculating probabilities. Until we develop architectures that sit on top of a truly grounded foundation of world logic, we must remain vigilant against the illusion of machine certainty.

The Myth of "Bigger is Better"

The market is currently obsessed with the idea that the path to Artificial General Intelligence (AGI) is simply a matter of scale; more compute, more parameters, bigger models. I disagree.

We are reaching the point of diminishing returns for massive, generalized models. A model that can write a Shakespearean sonnet, pass the bar exam, and debug Python code is intellectually fascinating, but functionally excessive for most enterprise needs. I do not need my supply chain optimization algorithm to “understand” 18th-century poetry, nor to code in Python.

The future of enterprise AI lies in rightsizing. Once we distill these massive models into smaller, highly focused, domain-specific architectures, perhaps utilizing a Mixture of Experts (MoE) approach, the cost to run them drops drastically. Smaller, smarter, and tightly scoped models deliver vastly superior ROI because they solve specific business problems without the bloat and compute costs of a massive frontier model.

The Value Equation: Digitization vs. Transformation

Ultimately, we must confront the value equation. Are we actually transforming our businesses, or are we just using AI to do the same inefficient things – just faster and more often?

If you take a broken, convoluted business process and simply layer an AI agent on top of it to speed up the keystrokes, you have not achieved digital transformation; you have merely digitized a bottleneck. True enterprise value comes from using these technologies to fundamentally reimagine workflows, ask questions of your systems that were previously impossible to query, and empower employees to operate at a higher strategic level.

Governance in the Age of AI

Finally, we cannot ignore the geopolitical and regulatory backdrop, such as the EU AI Act. While some decry regulation as a handbrake on innovation, pragmatic risk frameworks are essential; my emphasis is on pragmatic here. Bad actors will not slow down, so neither can we. However, we must limit our deployments to lower-stakes environments until we fully understand a model's limitations. You cannot hold an AI accountable when it makes a critical error in a clinical diagnosis or a financial audit. Accountability remains—and must always remain—with the humans who deploy it.

The score in 2026 is clear: AI is a powerful utility, but it is not magic. Enterprises that succeed will be those that pair domain expertise with right-sized, cost-effective models, applying them to genuinely transformative use cases while maintaining strict, human-led governance.

Three Things Enterprise Software Buyers Should Take Away

These are the hard truths for CX software buyers, paired with the uncomfortable questions they need to ask vendors to cut through their marketing noise

Call out the "intelligent" BS

Vendors love claiming their CX chatbots are "grounded" and can "reason". Spoiler: they can't. They are just probabilistic auto-completes guessing the next word based on correlation.

Ask the vendor:

Since your model is fundamentally probabilistic and non-deterministic, what hard, deterministic guardrails actually stop it from hallucinating a fake refund policy for our customers?
"Are you using the term 'grounded' just to describe a basic RAG (Retrieval-Augmented Generation) setup, or is there an actual foundational world-model logic driving this?"

Keep your human experts

AI is completely clueless about cause and effect. If you want to resolve complex customer issues instead of just failing at scale, you need your human domain experts to babysit the AI and catch its inevitable blind spots.

Ask the vendor:

LLMs don't inherently understand causality, they just mimic it based on training text. How does your system allow our domain experts to review, correct, and input hidden causal factors that the AI missed?
Is this tool designed to genuinely augment our tier-3 support agents, or is it just a generic copilot that's going to force them to spend more time auditing its mistakes than actually helping customers?

Stop paying for bloated models

You don't need a massive LLM capable of writing Shakespeare just to route a simple customer complaint. Demand smaller, domain-specific models. They do the job better, and you won't get fleeced on unnecessary compute costs.

Ask the vendor:

Why should we pay the massive compute premium for a generalized frontier model when a smaller, right-sized model trained specifically on CX data and use-cases would be cheaper and more effective?
If we strip away the massive scale of the underlying LLM, what unique, domain-specific value is your company actually bringing to our specific industry's workflows?

CRMKonvo - Freshworks on Platform, CRM and useful AI

Sat, 21 Nov 2020 09:35:26 -0500

Freshworks has is now officially a fresh (sorry, I really couldn't resist this pun) member of the club of platform players. The company introduced its own flavour of CRM and a platform that they build upon. What is next? Lot's of ground to cover. A CRKKonvo with Prakash Ramamurthy, Chief Product Officer, Peter Stadlinger, Head of Products CRM and David Krauss, Senior Director Product Marketing at Freshworks.

ogether with Marshall Lager, Ralf Korb and Thomas Wieberneit they discuss market perspectives, what the value for customers is and how the innovations that the team has recently introduced fit in there.

Prakash, Peter, and David bring a wealth of knowledge to the conversation, including a pretty interesting dive into how to train an AI based upon the idea that the human who is in front of the machine is still one of the most important trainers, due to tacit knowledge and wisdom that cannot be codified. Which also explains the trifecta of priorities that Freshworks follows with its CRM:

UI/UX first
AI that actually works
native customer 360

It is also about value, where we do a short deviation towards pricing and, of course, platform.

Enjoy a fascinating discussion with empathic points made.

https://youtu.be/k32C69kMMb0

Customer Experience is the way! But where is its value?

Tue, 28 Apr 2020 11:00:19 -0400

These days, everyone, including myself, is talking about a great experience being the new differentiator. About product, and service being less and less of a factor distinguishing businesses. There is talk of customer experience, user experience, brand experience, product experience, consumable experiences, but mostly this is summed up under the umbrella of customer experience. With this, of course, businesses are reacting with creating customer experience initiatives, building strategies, and implementing solutions, from customer journey orchestration via 1 to 1 marketing solutions, or voice of customer programs. The result is significant investment and CEOs being convinced that their customers have a very good experience. Just that their customers tend to not agree to this assessment, which leads to a considerable experience gap. Abbildung 1: Experience Gap. Source Qualtrics What is the experience gap? At the end of the day, it is the mismatch between brand and product promise and delivery to these promises. This experience gap is a strong indicator that something is at odds with the assessment of the own performance. This might be a consequence of wrong KPIs, wrong measurements or, in the worst case, a wrong or failed strategy. It might also simply be a matter of confusing all these terms, that I mentioned above. So, let’s start with putting some stake in the ground by doing some definition work.

Brand Experience

On Techtarget we can read that brand experience “is a type of experiential marketing that incorporates a holistic set of conditions created by a company to influence a feeling a customer has about a particular product or company name.“ This is a very inside-out definition, which I’d like to turn around to become an outside-in definition. Brand experience is the perception that a customer has about a brand. Brand perception gets formed by the consistency of brand promise (marketing) and delivery to the message that gets delivered by marketing. This is independent of what the message is. Let’s look at some examples: The brand of German discounter Aldi is all about low price while being local. With that the brand is in a continuous perception battle with Lidl, a competing discounter brand. On the other end, there are high end brands that place their focus on luxury and high performance, like Italian car maker Ferrari. Both examples are success stories. Some not so successful examples are German car maker Volkswagen trying to get into the luxury car segment with their Phaeton model. Promise and delivery did not fit in this example. Or fashion retailer Gerry Weber. Similar story, but an even worse outcome.

Product Experience

Product experience covers the customer’s perception of a product, from learning about it, up to no more using it. This covers design, usability, but also the value that the product gives to the customer, in terms of solving the customer’s problem using it (job to be done). Of course, this then also compares to the product’s price.

Employee Experience

Employee experience is the worker’s perception of their employer. This perception is built up from the first contact via the hiring process through the whole journey of being employed. This experience covers how corporate values are lived, interactions with colleagues, superiors, etc., availability and usage of technology, to count just a few topics. It also includes how empowered employees are and feel to be. A good employee experience is an important enabler for a good customer experience.

User Experience

Wikipedia defines user experience as ‘a person’s emotions and attitudes about using a particular product, system or service. It includes the practical, experiential, affective, meaningful and valuable aspects of human-computer interaction and product ownership. Additionally, it includes a person’s perceptions of system aspects such as utility, ease of use and efficiency”.

Consumable Experience

Ok, I admit it. I stole this term from friend Paul Greenberg. He coined in back in 2016 in one of his great articles. A consumable experience is essentially an individual experience. Individual experiences occur whenever a customer interacts with a product or a brand. And individual experiences can get staged, i.e. designed and then promoted, as Joseph Pine and James Gilmore already put it already back in 1998. They say that “an experience occurs when a company intentionally uses services as the stage, and goods as props, to engage individual customers in a way that creates a memorable event”. A consumable experience needs to be in line with the company messaging and positioning, be consistent with its products and services. There, e.g. is no need for fanciness at Ryan Air. The brand is all about getting from A to B cheap. This way, a consumable experience contributes to the overall customer experience.

Customer Experience

All the above types of experience contribute to the customer experience. The customer experience is where this all culminates. Wikipedia defines it as “the product of an interaction between an organization and a customer over the duration of their relationship”. Forrester Research defines customer experience as “how customers perceive their interactions with your company”. Paul Greenberg says that customer experience is “how a customer feels about a company over time”. Over time is the keyword here. So, while a consumable experience is a single experience, customer experience is kind of the sum total of all experiences, weighed over time.

Experience is Perception – but where is the value?

What one can derive from all this is that experience is all about perception. An experience is a result – an outcome – on the customer side. Customer experience is solely in the realm of the customer and hence something that can get only influenced, or partly managed by businesses. Businesses can control their messages. It is hard for a company to influence how I got up in the morning or whether or not I had a good day with my colleagues, partner, kids. The vehicle for this influence is the unity of different individual experiences. Which in turn can partially be ‘supplied’ by software. I use the term supplied in quotes as the company and its software can only create engagements, which result in experiences. While it is very plausible that customers prefer making business with companies they feel good about, the question remains what the actual value or the return on investment of customer experience is. While improved sentiment and a higher NPS might be the measurable outcomes, one could ask whether or how this can get attributed to a customer experience initiative. And more importantly: How does the value that a better experience creates for the customer translate to value for the company itself? What are the economic outcomes? There are lots of studies that tell us that customers – especially consumers – are willing to pay more for a good experience. Here is one from Bain, one from Dimension Data, another one from Super Office, American Express, another one from Microsoft, or a study by the MIT together with Capgemini Consulting. Forrester Research already in 2016 concluded in a study that ‘Customer Experience Drives Revenue Growth’. But again? How can a business calculate the return of a customer experience initiative - its return on experience? How to quantify it in hard currency?

Here is a framework

In order to do this it is crucial to be able to link the pay-out of an initiative to a change in customer experience. This makes it necessary to

list and chart the journeys that are most important for your customers,
identify KPIs that show the status and change of customer experience along these journey
link them to a monetary value for the company

The first step into this exercise is the identification of the customer journeys that are most relevant for your customer. There is no need – yet – to dive into the importance of the touch points, this will come later. There is also no need to look into the ones that are less important for the customers, even if you look at them as being important. Focus on customer needs and desires. Then, identify a number of, say 5 KPIs that are measurable, important for you, change with customer experience, and have a link to revenue or profitability, depending on your goals. These KPIs could be customer churn, share of wallet, customer lifetime value, cost to serve a customer, cost of sales per customer, number of customer issues, defaulting on mortgages, referrals, or many more. Which ones are important depends heavily on the industry you are working in. It doesn’t matter if you find only 3 KPIs, but build a model, or at least a working hypothesis, that links them to the customer journeys, or parts thereof. Do some simple surveys. Within the limits of data privacy, link what customers say about their experience to what they actually do. SAP calls this linking experience data with operational data. Doing this you can segment the (anonymized) customer base into 3 to five groups, along with their scores within the chosen KPIs. As these have a link to value, this segmentation shows where value lies, between which segments particular gaps are and hence, where the road to improvement is. Link initiatives to the chosen KPIs via a business case that clearly shows the expected change in KPIs and choose the ones with the highest lift proposition relative to their implementation cost. Linking the benefit to the cost enables you to do two things: Staying nimble, means maintaining the ability to act small while thinking big, and to establish a prioritization of initiatives based upon customer outcomes. Track the outcomes, i.e. the changes to the KPIs after implementing the initiatives.

The Demo, the 7P of Planning, and Customer Experience

Sat, 07 Sep 2019 06:44:52 -0400

Being a consultant being called into or asked to do a product demo is inevitable. A demo is one of the most powerful tools that product/solution vendors and their partners have in their arsenal to convince prospects. The demo is a key part of the customer journey that the buyer of enterprise software takes. A good experience in this step will establish the trust that is necessary to go any further with a vendor and/or implementation partner. A great demo can make an underdog a winner while a poor demo can make the frontrunner an outright loser. Well, and sometimes the underdog’s killer demo scores them only the second place in a winner-takes-it-all world. I have seen and done that on both sides of the table, given good and bad demos, sat as a customer or trusted adviser, attending bad to great demos. And it is always amazing to see and participate. One thing is for sure: If you get into the make or break position of a competitive demo in a short list, you better remember

The 7 P of Planning

Proper Prior Planning Prevents Piss Poor Performance. Know thy customer; and know her well.

Who is part of the buying center?
Who decides?
Who influences who?
Who has the money?
What are their likes and dislikes?
What are their interests?
What do they want to achieve?
What do they expect to see?
How can I surprise/wow them?
Who is my friend?

These are only some of the more important questions that you need to get answers for before the demo. Other ones include answering how important this deal is for me? And related to it how much effort you would like to invest into it. Who is the competition? Is it another partner working with the same software that you are working with? Or is it a competing vendor? With most of the questions above clarified and given that the customer is important enough to warrant some investment it is time to prepare the demo. More often than not there are a number of functions and features that the customer wants to see, and usually, they are given as that, functions, and features. Often the demo requirements are prepared from the point of view of what customer employees are doing now, sometimes even to an extent that they are just translating their current way of working into the world of the new system that they want to implement. Expect a check list that helps your audience in making sure that the relevant points got covered and to what extent. Expect this check list being mainly organized by the sequence of functions and features that have been asked for. Means, you are cornered. Are you?

How to make your demo a success?

This is the demo dilemma that places you squarely between a rock and a hard spot, as a demo needs to have a flow, you want to tell a convincing story, and then users of your solution surely do things different than users of most other solutions. And then your solution can do a few things that make it shine over the competition, doesn’t it? You also do not want to do a ‘hard sell’ but gain trust to become an advisor. It is the long-term relationship that matters. Technically, the most important part of the demo is translating the required functions and features into a story with a good flow that covers the required topics. Coverage should happen in a sequence that mainly covers the sequence of appearance of the features in the demo requirements document. Be sure to also plan to show some topics that are not necessarily asked for but that are eye-openers for the audience. You have a friend in the organization? Talk to him/her and make sure to get a deeper understanding of what is really wanted, how processes currently look like and what the pain points or unmet needs, especially of the most important people in the audience, are. Be sure you listen and then use the advice. It is not really necessary to explicitly mention the pain points but just demoing a solution for an unmentioned pain point gives you credits and helps establishing trust. You show that your solution can support more than the imminent pain points, that you know your stuff – and, what is more, your customer’s business. Last, but not least: Be sure you have the right person to demo the solution. This person needs to know the customer’s industry, ideally the customer, and speak the customer’s language – as in language and jargon. There is no point in having a German with poor English skills demo to an English-speaking audience – or vice versa. In addition, demoing is almost like acting. It is a performance! So, make sure that the presenter can perform and speak vividly in front of an audience. There isn’t a much better possibility to put an audience asleep than a droning on presentation. Doing a few dry runs helps in this regard.

To sum it up

As you guess by here, delivering a killer demo is not really rocket science, but the application of a simple recipe.

Know your customer
Know your stuff
Get the right intelligence
Prepare
Tell a story
Perform

A killer demo is also a surefire way to deliver a mind-blowing customer experience. Does it guarantee a win? No. But it brings you a lot closer to it by establishing the necessary trust into software and implementation partner. Now, you say that this is a lot of effort for a demo? It is. But then this is the only way to go for bigger deals, or deals that you just want to win. Having a good infrastructure surely helps to contain the necessary efforts, but it still is effort. Are there less expensive ways? Yes, there are. Ask me for ideas if you are interested …