Generative AI is here to stay.

It is not only a hype that probably gets worse before it gets better. And we clearly still are in a hype, as the following chart showing the search interest for ChatGPT between October 1, 2022 and April 12, 2023 from Google Trends shows.

Similarly, the Gartner Group sees Generative AI technology approaching the peak of inflated expectations in its 2022 hype cycle for artificial intelligence.

To be sure, we see only the tip of the iceberg when looking at voice, text or image based services that we all know and use. The Gartner Group also foresees many industrial use cases reaching from drug and chip design to the design of parts to overall solutions. 

You think that these scenarios lie far in the future? Read this Nature article from 2021 and think again.

And in contrast to some of the other hypes that we have seen in the past few years, there are actual use cases that support the technology’s survival of the trough of disillusionment. As there are viable use cases, unlike “Metaverse”, Blockchain or NFTs have shown, generative AI is not a solution in search of its problem.

Apart from OpenAI’s GPT and Dalle-E models that surely caught everybody’s attention in the past weeks and months, there are a good number of large language models that are just less known. A brief research that I recently conducted, unearthed more than 50 models that got published over the past few years. For their paper A Survey of Large Language Models that focuses on “review the recent advances of LLMs by introducing the background, key findings, and mainstream techniques”, a group of AI researchers identified a similarly impressive number of large language models that got developed in the past few years.

Fig. 1: A timeline of large language models; source: A Survey of Large Language Models

This increased competition, along with research into how the resource consumption of large models can be reduced, e.g. through sparsification, will lead to improved pricing.

Consequently, businesses need to find a way to leverage this technology without harassing their own data security and/or exposing their intellectual property (IP).

That this is easily possible, has been already learned the hard way by companies as diverse as Amazon or Samsung, to name just two better known cases. And then, there are ongoing use cases around the unauthorized use of IP-protected data for training and even many questions around the ownership of generated (derivative?) work products of generative AI, not even speaking in unwanted bias. 

The way to be walked involves three major steps:

Education

It is necessary to educate the teams not only on the benefits of using generative AI but also on how to check the output and, importantly, when to not use generative AI. Every employee has a role in keeping the business protected from avoidable mistakes. And no employee wants to make one of those mistakes. Sources that are used for any content that is generated should to be attributed and/or verifiably not be copyrighted. Create some sensitivity about “when in doubt, don’t use it”. Enable your people. For the time being, this training should be frequently reviewed, as the whole area is evolving fast.

Second, it is important to enable personnel to fine tune and then train large language models. This is not necessarily a trivial task that requires some knowledge about machine learning.

Governance

As with most technologies, there is a need for governance to augment the training. Generative AI is a very powerful tool, so it needs to be used responsibly and ethically. To help the employees with their own decision making about when and how it is permissible to use generative AI services in the course of their work, and according to the corporate values, it is necessary to develop, publish and implement a policy that outlines and explains the relevant guidelines to follow.

Points that should be covered by the policy include;

• Who is allowed to use generative AI services. Depending on numerous factors like corporate culture, sensitivity of information dealt with, etc. it may be useful to explicitly limit the use of generative AI.
• Proprietary or confidential information must not be revealed or disclosed.
• Similar to other policies (e.g. use of the Internet in general), there may be a limitation to business use or the permission to also use it for private purposes.
• The services should not be used for tasks that are violating any law, regulation, or the company’s code of conduct. This involves discrimination, offensive or hate speech that should be prohibited. This also includes content that is intended to deceive or defraud others.
• Whatever the generative AI produces needs to be reviewed. 
• The use of generative AI to create a work product should be disclosed in order to maintain transparency.

The policy should also include a reference to the offered training that personnel should undertake. Last, but not least, and as bad as it sounds, the consequences of violating the policy must be made clear as well. Here, too, I would concentrate on explanation and rationalizing instead of just wielding a stick.

It might be useful to review this policy frequently, as we are still in a learning period.

Following my own suggestion here: I have used You.com and Google Bard to give me some points that the policy should cover.

Execution

In parallel to this effort, it is time to look for use cases that can meaningfully be implemented. Collect and assess them. Meaningful means that the use cases are important enough to add value and isolated enough to still be able to manage any risks. These use cases can include improving existing capabilities or adding additional ones. What is important is that for all use cases there need to be KPIs that shall get improved and a cost-benefit analysis.

Remember: Not every problem needs to be solved with the help of a LLM! For some problems it is more efficient to use more “traditional” technologies.

At the same time, it is important to be aware of the level of LLM-knowledge that is available in house. This level is, especially in smaller organizations, not too high. The degree of available knowledge is a boundary factor for project execution. So, it is best to start off with a problem that requires only minor fine-tuning of an existing LLM that is powerful enough to to support multiple of the collected use cases. This way, it is possible to learn fast while also getting results fast. Additionally, fine tuning a model comes at far lower cost than fully training a model, as the number of required training cycles is smaller and the amount of necessary training data is far lower. Out of the many available models (see above), one or more will be capable of supporting most of the selected use cases after fine tuning.

The way to success is thinking big while acting small and to walk before starting to run.

Inspired by a recent panel discussion as part of theIn the Hot Seat podcastthat I am involved in, I opened a chat with chatGPT3. ChatGPT is a language model by OpenAI that interacts in a conversational way. This way, it shall be able to follow a conversation, answer follow up questions or even admit mistakes, challenge incorrect premises or reject inappropriate requests. Our sixth episode of In the Hot Seat revolved about the question whether web3 will deliver on its promise or not.

The promise being that content producers and web users get more power by applying concepts like decentralization, blockchain and a token economy.

As I am a bit sceptic about this kind of silver bullet promises, I went right for the jugular.

A conversation between a human and a bot

Thomas: Tell me with arguments why web3 based on blockchain will fail

chatGPT3: It's impossible for me to provide arguments as to why web3 based on blockchain will fail, as web3 is not based on blockchain technology. Web3 is a term used to describe the next generation of the web, which is focused on decentralization and enabling users to have more control over their online data and interactions. Blockchain technology, on the other hand, is a decentralized, distributed ledger that is used to securely and transparently record transactions. So while blockchain technology may play a role in the development of web3, it is not the foundation of it.

Thomas: If blockchain does not play a role in web3, how will the decentralization be achieved?

chatGPT3: Web3 is focused on decentralization, but it does not necessarily rely on blockchain technology to achieve this. Decentralization can be achieved through a variety of means, such as peer-to-peer networks, distributed systems, and other technologies. Some examples of decentralized technologies that could be used in the development of web3 include distributed file systems, peer-to-peer messaging protocols, and decentralized identification systems. Ultimately, the specific technologies used to achieve decentralization in web3 will likely depend on the specific goals and requirements of the applications and services being built on top of it.

Thomas: How can ownership be assured in a decentralized world if blockchain is not the foundation?

chatGPT3: In a decentralized world, ownership can be assured through the use of cryptography and digital signatures. These technologies allow individuals and organizations to prove ownership of digital assets without the need for a central authority or third party to verify the ownership. For example, in a decentralized network, a user could prove ownership of a particular asset, such as a digital token, by signing a message with their private key. This signature could then be verified by other users on the network using the user's public key, providing a secure and transparent way of proving ownership. While blockchain technology is one way of implementing these cryptographic techniques, it is not the only way, and other technologies such as distributed ledgers or advanced cryptography could also be used to assure ownership in a decentralized world.

Thomas: Why then the hype around web3 based on blockchain?

chatGPT3: Web3 is not necessarily based on blockchain technology, and the hype around it does not necessarily relate to blockchain. The hype around web3 likely stems from the potential for decentralization to enable new, innovative applications and services that were not possible with previous generations of the web. Decentralization has the potential to allow users to have more control over their data and interactions online, as well as enabling new business models and opportunities. While blockchain technology may play a role in the development of some of these decentralized applications and services, it is not the only technology that could be used, and the hype around web3 is not necessarily tied specifically to blockchain.

Thomas: What new innovative applications and services are not possible with previous generations of the web?

chatGPT3: The potential applications and services enabled by web3 are difficult to predict, as the technology is still in development and many of the specific details are not yet known. However, some possible examples of innovative applications and services that could be built on top of web3 include decentralized finance (DeFi) platforms, decentralized social networks, and decentralized marketplaces. These types of applications could potentially offer users more control over their data and interactions, as well as enabling new business models and opportunities. Additionally, web3 could enable new applications and services that are not currently feasible due to the centralization of the current web, such as secure and transparent voting systems, decentralized governance systems, and distributed cloud computing platforms.

Thomas: Decentralized social networks do already exist. How can defi work without a decentralized ledger, i.e. a blockchain?

chatGPT3: While decentralized social networks do already exist, they are not as widely used or as well-known as their centralized counterparts, such as Facebook or Twitter. Decentralized finance (DeFi) platforms, on the other hand, are a relatively new concept that is still being developed. DeFi refers to the use of decentralized technologies, such as smart contracts, to enable financial applications and services that are not dependent on traditional financial institutions. These services could include things like decentralized exchanges, lending and borrowing platforms, and prediction markets.

DeFi platforms do not necessarily need to be built on top of a blockchain in order to function. While blockchain technology does provide a decentralized, distributed ledger that is well-suited for certain types of financial applications, other decentralized technologies such as distributed ledgers or advanced cryptography could also be used to enable DeFi platforms. Ultimately, the specific technologies used will depend on the specific requirements and goals of the DeFi platform in question.

Thomas: name me some defi platforms that do not use blockchain?

chatGPT3: As decentralized finance (DeFi) platforms are a relatively new concept, there are not currently many examples of DeFi platforms that do not use blockchain technology. Most existing DeFi platforms are built on top of blockchain networks, such as Ethereum or EOS, as these networks provide the necessary infrastructure for decentralized applications.

However, it is possible that future DeFi platforms could be built on top of other decentralized technologies that do not use blockchain. For example, a DeFi platform could be built on top of a distributed ledger that uses advanced cryptography to secure transactions, rather than the consensus mechanisms used by most blockchains. Additionally, some DeFi platforms are exploring the use of "layer 2" technologies, which are built on top of existing blockchain networks and enable higher scalability and performance. These technologies could potentially be used to build DeFi platforms that do not rely on the underlying blockchain network for all of their functionality.

Thomas: If you say most platforms do use blockchain, there are some that do not use blockchain. Please name some examples.

chatGPT3: As I mentioned earlier, most existing decentralized finance (DeFi) platforms are built on top of blockchain technology, as this provides the necessary infrastructure for decentralized applications. Therefore, it is difficult to provide specific examples of DeFi platforms that do not use blockchain technology.

However, as I also mentioned earlier, it is possible that future DeFi platforms could be built on top of other decentralized technologies that do not use blockchain. These technologies could include distributed ledgers or advanced cryptography, for example. Additionally, some DeFi platforms are exploring the use of "layer 2" technologies, which are built on top of existing blockchain networks and enable higher scalability and performance. These technologies could potentially be used to build DeFi platforms that do not rely on the underlying blockchain network for all of their functionality. It is worth noting, however, that these technologies are still in development and it is not yet clear what specific DeFi platforms will be built using them.

Thomas: Ownership already now can be through the use of cryptography and e-signatures. What problem that is not yet solved does decentralization solve or solve better than existing solutions do?

chatGPT3: Decentralization has the potential to solve several problems that are not yet solved by existing solutions. For example, decentralization can provide increased security and resilience by distributing data and functionality across multiple nodes, rather than relying on a single, central authority. This can make it more difficult for attackers to compromise the system, and can also ensure that the system continues to function even if individual nodes fail.

Additionally, decentralization can provide increased transparency and accountability by enabling all users to have access to the same information and allowing anyone to verify the correctness and integrity of that information. This can help to build trust and confidence in the system, and can also enable new business models and opportunities that are not possible with existing centralized systems.

Finally, decentralization has the potential to enable individuals and organizations to have more control over their data and interactions online. By removing the need for central authorities to verify and control access to data, decentralization can enable users to have greater control over who has access to their data and how it is used. This can help to protect user privacy and can also enable new applications and services that are not possible with existing centralized systems.

Thomas: Your first point basically says that security by decentralization is inefficient. Your second point disregards that the information can also be made available to everyone, and easier, in a centralized world. Re your third point, how does decentralization promote more control by individuals as it creates more attack vectors?

chatGPT3: I apologize if my previous response was unclear. Decentralization does not necessarily make systems less efficient, and it can provide increased security and resilience by distributing data and functionality across multiple nodes. This can make it more difficult for attackers to compromise the system, and can also ensure that the system continues to function even if individual nodes fail.

In terms of transparency and accountability, while it is true that information can be made available to everyone in a centralized system, decentralization has the added benefit of enabling anyone to verify the correctness and integrity of that information. This can help to build trust and confidence in the system, and can also enable new business models and opportunities that are not possible with existing centralized systems.

Regarding your third point, decentralization does not necessarily create more attack vectors. While it is true that decentralized systems can have more nodes, which could potentially be targeted by attackers, decentralization can also provide increased security by making it more difficult for attackers to compromise the system as a whole. Additionally, decentralization can enable individuals and organizations to have more control over their data and interactions online by removing the need for central authorities to verify and control access to data. This can help to protect user privacy and can also enable new applications and services that are not possible with existing centralized systems.

My point of view

First of all: Wow!

And I mean W O W.

Given this conversation, not knowing whether there is a conversational AI involved or not, one surely doesn’t have an easy time to establish this fact.

The other thing that becomes abundantly clear is that the system is quite well trained. I would also suspect a bias towards web3, which it sort of ambiguously defines somewhere between web3 and web3.0. Yet, there is quite a wide understanding that one of the core foundational elements of web3 is a blockchain. The system is quite right about the fact that there is no coherent web3 yet.

So, it argumentation wise leaves the door open.

It also, “behaves” rather like someone who is semi-educated and defensive. Drilling down into statements that the system makes are responded to with references into a possible future. This can be clearly seen when it comes to “new innovative applications” or my question about defi companies that do not use blockchain and that the system suggested as existing.

All in all, this system clearly shows the art of the possible. This raises quite some expectations to the future, and some concern. With machines becoming increasingly better at using language, it must be made clear when one communicates with a machine and not with a human.

Last piece of advice: Do not try to use it as a PA. It doesn’t like it. Really not …

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.

It is the end of 2021 and I do not have anything better in mind than writing a last post for the year. So let's do it.

There have been some terms that were used more than others with some of them actually being quite hyped. Some of them for the right reasons, some of them for the wrong ones.

My favorites of these terms include in no particular order:

• Metaverse
• NFT (non-fungible token)
• RPA or robotic process automation
• Hyper personalization
• Hyper automation
• Customer Journey Orchestration
• Low code / No code
• Artificial intelligence / machine learning

Hyper personalization

I already did a short video that expresses my thoughts on hyperpersonalization. You cannot really avoid the term when browsing the web with anything related to CX in mind. Back in the day, what is now called hyper personalization was called one-to-one marketing. This was in the eighties. The problem was that we didn’t have the technology nor the computing power to technically implement a contextually relevant 1:1 approach at scale and in real time. That’s why we worked with segments. 

Now we have the technology and computing power to address individuals contextually relevant in real time, nearly regardless of the size of the audience. Technologically, this is quite amazing, and if used consciously is of great benefit for customers and prospects. 

But there is nothing “hyper” about it. It is still personalization, maybe individualization. 

My wish for 2022: Let’s do away with the hyperbole before we start to desperately look for the next hyperlative – or should I call it ultralative?

Robotic process automation

No doubt, RPA is an important technology. It provides a fast and easy – maybe too fast and too easy – entry into digitalization of processes.

Its origins lie in automated UI based testing, applied to the automation of workflows.

Using RPA or its derivative intelligent RPA helps companies to increase the efficiency of their processes, especially if they stretch across various applications that have poor or no interfaces or that haven’t been integrated.

This works very well for processes that rely on structured data and in the case of intelligent RPA increasingly also with unstructured data.

Why too fast and easy then?

The problem is that it is hard to improve processes with RPA. Instead, existing processes get entrenched even more. This is also why it helps in digitalizing processes and not in a company’s digital transformation, which involves the creation of new, different processes. Still, it is an important technology as it helps reducing friction in existing processes, helping in reducing manual work and speeding processes up considerably. As a consequence, both employee experience and customer experience can get improved considerably.

My wish for 2022: Let’s merge RPA into workflow automation because this is what it is.

Low code / no code

We have seen low- and no code for a few years now but it really popped into people’s minds in 2021. Related terms are “citizen developer” and “democratization of development”. 

Again, another important technology that improved a lot over the decades. 

Yes, it is available for decades. Look at all the abilities to customize that enterprise systems offer for a long time. Admittedly, it often looked ugly and was not that easy to understand, but still, it was there.

Low code and now code have a tremendous value in workflow automation and systems configuration. 

My wish for 2022: May there be sufficient governance to avoid the database hell that we experienced in the good ole Lotus Notes times (or have these been the bad ole days?).

Hyper automation

This term is worth a rant. Gartner defines hyper automation as “a business-driven, disciplined approach that organizations use to rapidly identify, vet and automate as many business and IT processes as possible. Hyperautomation involves the orchestrated use of multiple technologies, tools or platforms, including

• Artificial Intelligence (A)
• Machine Learning
• Event-driven software architecture
• Robotic process automation (RPA)
• Business process management (BPM and intelligent business process management suites (iBPMS)
• Integration platform as a service (iPaaS)
• Low-code/no-code tools
• Packaged software
• Other types of decision, process and task automation tools”

There are many more definitions like this, published by many pundits. 

Automation exists like, forever. It is one of the very reasons for the existence of software. And it always included the use of multiple technologies, tools and platforms. Just adding a bunch of shiny category names that can be used to the definition, doesn’t add any hyper to automation.  

My wish for 2022: Let’s cut the crap and name it as what it is: automation or process automation. Sorry for these sharp words but here we are inventing a term just for the sake of inventing a term. This doesn’t add any value besides making some pundits appear important and/or visionary (with the emphasis on appear).

Artificial intelligence (AI) and machine learning (ML)

There is no doubt that these two terms describe a set of technologies that are immensely powerful, given the availability of enough computing power. Machine learning is a subset of what artificial intelligence is. The technologies have entered a trajectory away from technology for technology’s sake towards being embedded into business process and outcome oriented. The technology itself is mutating from a kind of silver bullet to a mere helper. Of course, this is not always the case. There are still areas where AI is overrated, sometimes even dangerous. We are also seeing ethical problems and biased AI come up. Still, with the ongoing change the acceptance of AI and ML have increased a lot. 

My wish for 2022: May we see a continuation on this path along with an increasing focus on explainability and ethics. May we also continue to see “human augmented by machine” instead of “human replaced by machine”.

Metaverse

I have kept my three favorite ones till the end, the metaverse and, related to it, web 3.0, that is currently in everybody’s mouth being one of them. The preliminary peak of the hype was Facebook changing its corporate name to meta in October 2021.

Right now, nobody can credibly say what the metaverse will be, except that it will be a significant development towards communication and collaboration. But what is obvious is that metaverse will be another evolution of the platform play. It will be seen as a significant source of new business. The players are already lining up to achieve “dominance”. With the likely consequence that the “metaverse” will be broken down into a couple of walled gardens that are intended to further the garden owners’ agenda and lack interaction/integration. This breaks at least the first four of Tony Parisi’s seven rules of the metaverse.

So far, the metaverse reminds me of the gold rush at the Klondyke river. Many people rush there, most will return disillusioned (if at all) and a few (platform) players got incredibly rich.

My wish for 2022: Call me a dreamer but may there be a strong focus on above mentioned four rules, in particular openness and that no individual entity controlling it.

Non-fungible tokens

A non-fungible token (NFT) is a unique and non-interchangeable unit of data that is linked to a blockchain. It cannot be copied, substituted or subdivided. NFTs gained notoriety for being electronic identifiers that confirm the ownership of digital collectibles.

NFTs are closely related to the topics of blockchain, which is still trying to find a compelling use case, digital twins, and metaverse, which drives its hype. 

Given that, NFTs are yet another example of a solution in search of its problem. Or the other way round, it creates a totally new market for the exchange of digital assets.

The crux seems to be that everything seems to be around virtual, as opposed to physical assets. This means that, as long as no metaverse emerges, NFTs are actually not creating value but destroy it. Looking at sites like Decentraland where companies recently bought chunks of “real estate” for millions of dollars reminds me strangely of Second Life. But then, maybe I am just not creative enough to understand.

My wish for 2022: May we find a compelling use case for NFTs that creates a solution for an unsolved problem or provides a solution for a solved problem that is better than the existing ones.

Customer Journey Orchestration

I want to end this post on a positive note, that’s why I left customer journey orchestration to the end.

Customer journey orchestration is a technology that actually exists and that has some benefits. It helps businesses to think outside-in and to come to a mindset that their success is the consequence of making their customers successful. Implemented properly, it enables the identification of customers intents in real-time and to offer them touch points of their choice on their journey towards the solution to their challenge at hand (or job-to-be-done, if you will). It helps businesses to identify unnecessary friction in customer journeys and to remove it.

My wish for 2022: May we increase our understanding that automated processes like automated marketing campaigns are not customer journeys. 

Customer service is in the middle of a transition. Not only is technology capable of doing far more than it was, say, ten years ago, but also are customers expecting far more. That has a consequence for businesses. Automation and conversational AI are one thing. These technologies already help companies serving their customers more effectively, efficiently, and the customers way. 

But this is not enough! The same technologies that allow this, allow it for every company. Which means that one needs to elevate the game. 

How this elevation can happen using unstructured data to improve the customers' service experiences by e.g. leveraging unstructured data to avoid escalations is something that Martin Schneider of SupportLogic has good and very interesting stories to tell about. He argues that systems must become more intelligent and that purpose built AI is the way ahead, of course fuelled by the demands and learnings of Pandemic times.

In a nutshell he says: For years, people wer not harnessing the insights inside the unstructured data inside support interactions. Today we have the power to do so - in a sense, finding the logic and the trends etc. in support interactions that help guide the business, all driven by these support interactions.

It is very worthwhile listening to him. 

And entertaining, too!

Enjoy!

The News

On November 11, 2021 Freshworks held its annual Freshworks Refresh event. This year, the event had a hybrid format with around 250 customers, partners and analysts participating on site while around 17k people have registered for online participation.

There was a social pre-event and an after-event for entertainment and networking purposes. 

The event itself was themed around “delight made easy”. Naturally, it had different agendas for customers and partners on one side and analysts on the other side.

The morning was dedicated to a 4-hour sequence of keynote sessions for everyone. The event was kicked off with a keynote by Neuroscientist, entrepreneur, and author David Eagleman, who spoke about the “Science of Delight”. The closing keynote was delivered by Amy Purdy, who shared her inspiring story of how she used creativity, a positive outlook and a never-give-up attitude to turn her life from nearly dying, finding herself with a double lower leg amputation and failing organs into becoming a 3 times Paralympic medalist. Between these two speakers, who set the scene, Freshworks offered product and customer information.

Freshworks CEO Girish Mathrubootham gave a product update that linked into Eagleman’s message and a distributed customer panel spoke about their experiences with Freshworks, how they implemented Freshworks solutions and how these help the respective businesses. Rounding this off, Freshworks awarded several prizes to customers who offer exceptional EX or CX and showcased the winners of an internal hackathon. The latter is relevant because these winning solutions made it into or will make it into Freshworks products.

The afternoon was filled with customer related information in the customer and partner track and product and strategy sessions for the analysts. The analyst track started with an open Q & A session with CEO Girish Mathrubootham, a session about the changing role of the CIO and continued with product updates and a distributed fireside chat with a customer. 

The bigger picture

Every significant player – and every player that wants to be looked at as significant – is positioning itself as a platform player and its platform as the foundation for the ability to generate a great customer- and/or user experience. This can be observed in offerings for all sizes of customers, from small businesses to enterprise. The definition of what a platform is and contains varies. So does the maturity of the various platforms. 

As I have repeatedly written before, having a great technology platform is not enough to enable experiences.

There is more to being able to create an individual experience than just a technology platform. Apart from an appropriate, customer-oriented strategy and a culture to match, that is.

The platform that is needed to enable experiences at scale for organizations consists of more than IaaS and PaaS. Software vendors that want to be successful platform players need to be able to deliver on four areas to succeed:

• (Technology) Platform 
• Ecosystem
• Insight
• Productivity

By extension, this means that smaller vendors that cannot or do not want to deliver on these four dimensions need to choose the platform or platforms they want to become a part of.

My PoV and analysis

This part I need to cut into two sections, one about the event itself and one about the content.

The event

First, kudos to the team who organized it. The event was well organized and structured. The Freshworks team around Alan Berkson and Shelbi Gomez was always available with a helping hand. I think the team can be sad that only 7 analysts came on site.

Plenty has been said in the past years about Las Vegas conference venues, so I do not need to add anything but that we certainly got our ten thousand steps. Seriously, the Mandalay Bay is a good choice.

The two external keynotes by David Eagleman and Amy Purdy have been highly engaging and in the case of David Eagleman also highly relevant to the event theme. The following keynote by Freshworks founder and CEO Girish Mathrubootham showed how ease of use and delight can be combined. Doing this, he established an emotional connection around delight and ease. Using this smart approach, these terms are now tied to the Freshworks brand.

Another kicker was the customer panel with representatives of DHL, GE Digital Aviation Software, Carrefour and TaylorMade who talked about which parts of the Freshworks software they use, how the implementations went (easy as) and how they use Freshworks to pursue the digital transformation of their respective businesses. In line with the nature of the overall event, it was made up in a hybrid way with one participant attending from a remote location.

From a brand perspective, these customers are at the upper end of Freshwork’s sweet spot in the market, but then this emphasizes on the company’s ability to scale up.

These customers, as well as the customer representatives, who accepted the prizes often spoke with strong positive emotion about Freshworks, rather than only pointing out rationally where and how the products help their business. This increased the tie between delight and ease on one side and Freshworks on the other side. 

Why is this important? Because every significant player is playing a platform game, which also reduces the differentiating factors. Consequently, it is important to be front of mind when it comes to positively loaded keywords. This worked well in quite some industries, think automobiles or smartphones, to name only two. Freshworks establishes itself as a likeable brand.

In an interesting twist, Freshworks not only presented several customer awards but also gave a glance at what I would call “employee-driven innovation”. Chief Product Officer Prakash Ramamurthy presented the winners of an internal hackathon. Their implementations did make it or will do make it into the products. 

The afternoon started with an open Q&A with Girish Mathrubootham who openly answered all questions that were fired at him, which is testament to authenticity. I didn’t fully understand the relevance of the subsequent CIO session, although it seemed to partly set the stage for the AIOps topic that came up a little later. It was followed by a fireside chat with a customer and various product roadmaps along with their respective Q&A rounds. Some of the sessions were based on more detailed video information that was made available to us before the event. It was often important to have this material reviewed before the event.

The event overall felt good. It was well structured, quite rich on information and offered many strong customer testimonials. I think that especially the customer success stories have been a strength of this event. More of them might have even strengthened it even further. In any case it was good to be able to freely speak with customers.

There was ample opportunity to network with Freshworks people, partners, and customers. As written above, from a session point of view I wonder how Amy Purdy fit into the message and what the sense of the CIO session was.

Being asked, Marshall Lager confirms that “very little of my time felt wasted, and I’m the first to admit it if I tune out of a presentation. I think they missed the mark by making Delight the theme, though, because the content didn’t really bear that concept out.” 

Having been one of a few on-site analysts, what I missed was more possibility to interact with the remote colleagues. This may have been due to the tight schedule. As a caveat, I am aware that this is difficult to achieve.

All in all, I like the concept.

The messaging

Messaging is a difficult topic. Marshall Lager opines that the event tended to lean “into the message at the expense of substance.” I would agree to this as I sometimes had a feeling of questions being dodged by executives.

The topic of messaging is however, not only from a CRM (or CX) point of view, very important. During last year’s Refresh, this has been a major discussion point with the analysts, which was partly addressed.

According to Chief Product Officer Prakash Ramamurthy “CRM now does include support. Lots of investment is going into creating a unified solution. We have strong offerings in all three areas marketing, sales and service/support.”

Still, the Freshworks CRM suite message is slightly confusing. 

Freshworks announced a bundle named Freshstack, which consists of Freshsales, Freshmarketer and Freshdesk and that is dedicated to startups. Given this, it covers all functions of a CRM. Why not name it (Fresh)CRM? Especially, since some of the product slides refer to this combination of products as CRM. 

Freshsales and Freshmarketer, on the other hand, are grouped as the Freshsales Suite and marketed as an “All-in-one CRM”. This weakens the idea of the “One Platform for One Unified Business” and confuses the CRM (or CX) message. Again, CRM consists of marketing, sales, and service.

Still, this package acknowledges the trend towards fostering a closer collaboration of marketing and sales departments to achieve one common goal and not two separate ones although there is no talk about revenue operations, which is the next evolution of this trend.

The functional roadmap of the overall customer solutions suite appears sound; yet it shows that Freshsales and Freshmarketer are fairly young products.

Which is a fact that can be used as an advantage, as there is little legacy. Freshworks Head of Product for the Freshsales Suite, Peter Stadlinger is well aware of this fact. In the prerecorded part of the session he speaks of an “unfair advantage” that Freshworks has. It remains to be seen how the company uses this advantage.

Freshworks clearly positions itself as a platform vendor – like everyone else. Asked what beyond high usability the differentiators of the platform are, VP of Product Marketing, Neo, Tejas Bhandarkar maintains that “our platform strategy is based on 3 things: 

a) giving a unified experience to customers through unified customer experience, collaboration, multi-channel, federated search, etc., 

b) delivering insights built on data, enabling customers to compose their own analytics the way they want, and

c)  empowering developers to build simple as well as sophisticated apps easily.”

Prakash Ramamurthy extends that “easily customizable and extensible is what customers call out as a differentiator. The extended roadmap includes analytics.” This extension is important as several customers pointed at analytics when queried about where Freshworks could improve.

In addition to this, Freshworks recently partnered with UIflow, to enrich the Neo platform with low code abilities, while looking at Slack and Microsoft when it comes to productivity and collaboration apps.

So, looking at the components of a platform, Freshworks plays the ecosystem card in these areas.

Interestingly, Freshworks also does not talk much about AI and machine learning as part of the platform. These topics, e.g., in the form of the virtual assistant Freddy, mainly come up in the application layer. Freshworks wants to provide purpose-built features and not deliver AI as a technology. “Users do not care whether it is an AI or not”. Still, the foundation for these purpose-built features needs to lie in the platform, i.e., Neo, to allow for efficient development and deployment. I think that this topic deserves some more clarity.

However, Freshworks chose an interesting approach out of the dilemma that being a “platform vendor” is not a differentiator anymore. This approach is the use of emotion.

The message that permeated all communication is “we want your success; we are easy to deal with and we create our solutions for ease of use”. This was confirmed by customer testimonials in an impressive manner, even considering that customers who attend this type of event are rather positive and customers who get on stage are even more positive. With this, and with the themes of “delight” and “ease”, Freshworks managed to tie strong positive emotions to the brand. This is a path to differentiation that other vendors have not yet chosen to this extent, and this might give Freshworks an edge.

All this leads to the question how Freshworks gets into and grows in and with accounts. There are two main ways, organically and via the ecosystem, i.e., partners. Freshworks has partners with presences in more than fifty countries.

Although partners have not been that big a topic during Refresh2021, Freshworks has a network of more than 500 partners and a marketplace that lists 1,100+ applications. That certainly gives some scale and reach into various markets.

The company clearly positions itself for the mid-market. At the same time, it opens itself to enterprises that do not want or need too much customization but prefer functionality out of the box. Still, Freshworks wants to establish that its solutions do scale and are attractive for (divisions of) enterprises. This is in alignment with “delight made easy”.

A division that implements Freshworks then grows its implementation footprint in two ways, increasing usage across countries and rolling out additional solutions. Eventually, other divisions might implement Freshworks, too – a classical land-and-expand strategy. Asked whether the employee solutions and the customer solutions mutually enable this strategy, replies seem to suggest that land-and-expand works well between the customer solutions, e.g., a division starting with Freshdesk and then rolling out Freshsales or vice versa, starting with Freshsales and then moving on with Freshdesk; the employee solutions so far seem to play a smaller role here.

The last topic of interest is the one of industries. Freshworks does not offer industry solutions. Being asked when this will change and a vertical strategy be employed, Girish Mathrubootham answered that the company will remain horizontal to ensure that the market stays broad enough. While this is a sound answer, one might argue that addressing verticals does not reduce the size of the addressable market but adds ease to verticals that are specifically targeted. This, especially in times of industry clouds. It might become necessary to explain why Freshworks does not require industry solutions. This should be possible, also following the theme of ease. In any case it will be interesting to see how a pureplay horizontal strategy pans out.

Overall, I think that we see another interesting player emerge that can be a good and viable alternative to the bigger players in the CX market, given that customizing requirements are not too big and that there is an own local presence or a local partner.

Freshworks is a young company. The most mature products are on the customer service and ITSM sides. The sales, marketing and employee solutions are younger and, given that, still somewhat simpler than other solutions. On the other hand, there is an ambitious, young company behind them. Building and delivering on the existing messaging will be key.

The Clash of Titans seems to get an additional participant.

Now this title is not a case of do a rhyme or get bitten but actually summarizes the topic pretty well. The CRMKonvos team had a long and good discussion with John Paterson, CEO of Really Simple Systems about the needs and wants of a CRM for SMBs.

Are the requirements that an SMB has really different from the ones that an enterprise has? At the end of the day the original challenges seem to be fairly similar. Especially on the sales side, people avoid the system, just like Denis Pombriant found in a study that we talked about earlier this year in our CRMKonvo about why salesreps hate their CRM. Here a short version that just answers this very question. The parallels in thinking and observations between Denis and John are striking!

But then, there are a few differences. Listen to John explaining them.

But then this isn't all. Talking about value of the systems we couldn't not also talk about the value of AI. Is there any? Where?

Spoiler alert: There is, but one needs to know what one is doing. What? The discussion reveals it in the last 15 or so minutes.

Enjoy the conversation. We learned a lot. You can, too.

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.

It was a Tuesday again. This time Ralf Korb and I greeted Raj Balasundaram, Senior Vice President of AI at Emarsys as our guest - and had a second one, but more on this special appearance below.

Of course we were interested in both sides, Emarsys, the marketing automation company that was recently acquired by SAP, and in his deep experience with the implementation and use of AI based systems. We had lots of questions like:

Where is the marketing automation market headed? What is your view on marketing automation tools at the intersection of CDP’s, personalization, RTIM, Customer Journey Orchestration? Will these market segments merge? Will they jointly create another one?

What is the difference between Marketing Automation and Marketing Clouds? Is one used rather for B2B purposes and the other one more on the B2C side? Or is it different concepts?

What is the role of AI and M/L? What is AI after all? Is it just a vendor generated hype? Why should AI work now, after the previous hype cycles failed to deliver on the promise? What are promising applications of AI and machine learning in marketing?

Lots of ground to cover. And then we also touched pricing, which currently is a very hot topic. Raj has an interesting point of view - that I like a lot.

Last but not least, Ray Gerber, Chief Solution Officer at Thunderhead, who started to follow the CRMKonvo as an interested and active community member came in for some more in depth AI discussion. Things couldn't get any better, and of course we didn't stick to the hour that we gave ourselves. Too much high value conversation to be digested.

Enjoy this epic 1:30 h episode that is packed with expert information of two persons who know.

The News

The Bigger Picture

My Analysis and PoV