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Activant is a research-led global investment firm that partners with high-growth companies.

We partner with founders who have won their initial battles and are ready for the next challenge.

We typically invest when two things come together. First, the company has the potential to achieve escape velocity and transform an entire industry. Second, we believe the founders can lead the company through hyper growth, and our support can significantly increase the probability of this success. 

The critical time window to escape competitors and transform an industry is not open for long.

That’s why after we invest, the real work begins.

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Founders who aspire to build something bigger than themselves chose to partner with us at their most critical point.

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After graduating as valedictorian from the Rotterdam School of Management, he joined Amsterdam-based fund HPE Growth. There, he caught the bug of the rapidly evolving startup ecosystem in Europe and completed several growth-stage transactions.

Josh then joined a consumer startup in the automotive sector as a Founder’s Associate, focused on finance & strategy. As one of the first employees, he gained a first-hand understanding for the entrepreneurial challenges of building intelligent operations.

At Activant, Josh aims to combine his learnings from both the investor and operator side and is excited about building out Activant’s European presence from Berlin.

"I am fascinated by the holistic nature of entrepreneurship - every fiber of a business is interconnected. As investors, we have the unique privilege to spend our days trying to deeply understand markets and companies in order to help founders manage this incredible complexity."

HPE Growth is a pan-European private equity firm focused on early-mid growth capital investments in technology companies throughout Europe.

F.A.T. International is an independent consumer brand and communication platform that operates self-owned and -managed hospitality locations and events, produces its own merchandise while also acting as an alternative communication channel for established brands across the automotive and luxury space. Past products include co-branded luxury timepieces manufactured by Porsche Design, and event partners include Audi, Lamborghini, Porsche and BMW-Mini, among others.

“Sorry, I didn’t understand. Please select option 1 to ...” We have all suffered the frustration of self-service automation systems used by large banks, telcos, airlines, and hotels. Caught in a loop of misunderstandings and inflexible options, it can feel like the service provider has never encountered the issue we want to resolve. Why do we have to type “one’’ for English or “dos” for Spanish when voice recognition technology is effective and freely available? It simply makes no sense and indicates an opportunity for change. Conversational AI is likely the solution.

Introduction to enterprise conversational AI Platforms 

An enterprise conversational AI platform allows users to design enterprise-to-customer communications across voice and digital channels. This would allow human customer service agents to be augmented or entirely replaced, a potentially significant cost saving that means business owners are willing to explore and invest in conversational AI solutions. For example, the procurement manager at a large Swiss wholesaler suggested that the company planned to reduce its customer service headcount by 40% because 

 both freed up agent time by triaging inbound customer requests and improved overall productivity through its ability to quickly resolve client queries like a “super-agent.” 

We estimate that conversational AI platforms have the potential to automate as much as 50% of all customer service calls, generating value up to $40 billion in the US and $100 billion globally

Given this potential to leverage technology and create value, the market is unsurprisingly competitive and well-funded. Established incumbents such as Zendesk are developing in-house AI tools targeted at customer service, and investors have lined up behind myriad start-ups: India-based 

 has raised more than $600 million, and US-based 

 have each raised more than $100 million in funding. Lesser funded but equally notable players include Replicant, PolyAI, Parloa, Observe, Forethought, Hiver, Freshdesk, Tidio, Balto, Kustomer, and Invoca. There is no shortage of competitors in the market.

Our research highlighted three factors providing the impetus for growth that we believe will be sustained for years to come:

 The demands on customer service agents increased meaningfully with the onset of Covid-19 as both call complexity and frequency increased

 Recent research highlights that consumers are unlikely to change these new 

 Machine-led conversational AI interactions are the quickest and most effective route for institutions to drive customer engagement, and widespread implementation has led to 

 Business leaders recognize the potential for increased upsell and cross-sell because of faster issue resolution and higher customer satisfaction.

Also, generative AI models have introduced the ability to engage in more complex “multi-turn” conversations and provide human-like logic which broadens the scope for understanding user intent.

Increasing Labor Costs in Customer Service: 

Customer service professionals resigned en masse during and after the pandemic. The following period of coordinated stimulative monetary and fiscal policy has driven a significant increase in labor costs, limiting returns and incentivizing further 

When we studied the market, it became clear that the solutions offered differentiated themselves on the degree of software orchestration (from simply understanding the issue to independent tech-enabled resolutions) and whether a human or software agent resolves the issue. Our Activant Market Map segments the market on this basis.

Conversational AI 

Winning in a crowded landscape is never easy but we think that vertically focused players offering voice solutions that leverage naturalistic speech and understanding will differentiate themselves.

Most customer service still takes place via voice/telephone and the share of voice-related service increased during Covid-19, indicating a strong user preference for the 

 Experts confirm that voice will remain the primary channel of customer service and that legacy voice automation solutions (Intelligent Voice Recognition (IVR) and self-service automation) frustrate customers, who largely express a preference for human interaction.

Natural-sounding voices are a major selling point when enterprises consider purchasing an automation solution, and naturalistic dialogue (where the AI dynamically responds to naturally phrased human input as opposed to presenting an ordered list of viable options) is valued by customers and 

Future automation solutions should, therefore, be highly naturalistic.

Vertical specialization increases model accuracy and results in the creation of data moats wherein domain-specific user intents can be learned and modeled by the software agent, thus increasing the number of context-specific tasks a solution would be able to solve (which a generalized solution would not be able to).  

, for example, focuses on the manufacturing and financial services sectors and this gives them an advantage in acquiring new business as they increasingly develop domain-specific intents. User engagement can also be improved by 

using proprietary models in conjunction with open-source models

 to improve response accuracy, extract more intent from human input, and empower software agents to complete tasks in business systems of record. For example, 

 uses a mix of models to ensure accuracy and reduce response latency, 

 leverages OpenAI’s GPT3.5 alongside their in-house NLU model, and 

 makes use of third-party tools to enhance the naturalistic aspect of their voice models.

The importance of these differentiators will increase over time as we believe that switching costs will decrease. One enterprise customer described how it took six weeks to switch their solution, with the biggest inhibitors being the need to build integrations into ERP and CRM systems as well as training human agents in the system. However, they were open to switching again in the future if a better product became available. We believe that switching costs will decrease over time as companies develop better pre-trained models (likely limited by vertical) and can more quickly achieve higher containment rates.

The solutions that we find the most attractive are “Automated Virtual Agents” (software solutions which are capable of independently understanding an issue, deducing a path to its resolution, and solving the issue without the intervention of a human agent) as shown in the Activant Thesis Map. There does, however, exist some overlap between firms that address the customer service end-market by augmenting the abilities of human agents while automating some portion of the agent’s work (Agent in the Loop) and Automated Virtual Agents.

We have identified a few interesting players based on the work we have done in the space:

 has an enterprise-grade product that performs well in terms of naturalistic language, issue resolution and routing accuracy. A customer interviewed cited 50% containment rates using PolyAI – an 8x increase over the containment rates that they previously achieved with another leading product. They believed that the product could replace as many as 40% of the current human agents as it can handle complex use cases and multi-turn conversations with a long context window.

 has recently emerged from stealth and is a surprisingly compelling example of an enterprise-ready product. Sierra’s agents are quick to deploy and integrate deeply into a business’ systems of record which allows them to act on customer issues such as updating customer information or updating a subscription. Sierra has overcome its lack of vertical specialization by orchestrating across several knowledge sources and AI models (both open-source and proprietary) to address different elements of the overall issue resolution workflow without losing context.

 appears to have strong traction within its focused industry verticals of financial institutions, municipalities (Nordics) and utilities (UK). The company considers itself to be “middleware AI” and is confident enough in its product that it guarantees containment rates with a money-back guarantee if targets are missed.

 offers a platform that learns from conversational data and knowledge-base articles, allowing it to deliver a more naturalistic solution. It also integrates into existing customer service processes, increasing efficiency without disrupting workflows. Forethought provides data schema and shares models directly with customers for observability. Customers claim a superior customer experience.

i all offer enterprise-grade platforms that effectively manage the orchestration of AI agents, integration with ERP & CRM suites, as well as providing “easy-to-use, out-of-the-box” functionality for business users.

There is no doubt that this is an interesting market worthy of serious consideration for investment. The rate of AI research and product developments is impacting this market, and competition is intense –so intense that industry experts suggest that as many as half of the players could disappear over the next 12 months. Picking winners is always difficult, and in a rapidly changing world, it sometimes feels impossible! If our work resonates or you have a different perspective, please 

The information contained herein is provided for informational purposes only and should not be construed as investment advice. The opinions, views, forecasts, performance, estimates, etc. expressed herein are subject to change without notice. Certain statements contained herein reflect the subjective views and opinions of Activant. Past performance is not indicative of future results. No representation is made that any investment will or is likely to achieve its objectives. All investments involve risk and may result in loss. This newsletter does not constitute an offer to sell or a solicitation of an offer to buy any security. Activant does not provide tax or legal advice and you are encouraged to seek the advice of a tax or legal professional regarding your individual circumstances.

This content may not under any circumstances be relied upon when making a decision to invest in any fund or investment, including those managed by Activant. Certain information contained in here has been obtained from third-party sources, including from portfolio companies of funds managed by Activant. While taken from sources believed to be reliable, Activant has not independently verified such information and makes no representations about the current or enduring accuracy of the information or its appropriateness for a given situation.

Activant does not solicit or make its services available to the public. The content provided herein may include information regarding past and/or present portfolio companies or investments managed by Activant, its affiliates and/or personnel. References to specific companies are for illustrative purposes only and do not necessarily reflect Activant investments. It should not be assumed that investments made in the future will have similar characteristics. Please see “full list of investments” at https://activantcapital.com/companies/ for a full list of investments. Any portfolio companies discussed herein should not be assumed to have been profitable. Certain information herein constitutes “forward-looking statements.” All forward-looking statements represent only the intent and belief of Activant as of the date such statements were made. None of Activant or any of its affiliates (i) assumes any responsibility for the accuracy and completeness of any forward-looking statements or (ii) undertakes any obligation to disseminate any updates or revisions to any forward-looking statement contained herein to reflect any change in their expectation with regard thereto or any change in events, conditions or circumstances on which any such statement is based. Due to various risks and uncertainties, actual events or results may differ materially from those reflected or contemplated in such forward-looking statements.

A shift in the customer support landscape

Conversational AI and the Future of Customer Service

In 1908, Ford unveiled the Model T, a four-cylinder, 20-horsepower car that cost $850. Production was painfully slow, with factories only able to make ~32,000 per year by 1910.

However, Ford cracked the code on something no one else had solved: part interchangeability.

 This innovation, followed by the introduction of the assembly line in 1913, which broke tasks down into smaller, specialized jobs, cut the average task cycle time for a Ford assembler by an order of magnitude, from 514 to 1.9 minutes.

The result? Just a few years later, Ford had increased the output of Model Ts by a whopping 23x and cut its price by more than half - a tectonic shift that single-handedly launched the automotive age.

Ford's methods continued to define manufacturing, with future waves building on the system. Lean manufacturing, for example, honed in on waste and inefficiency and gained popularity in the 1950s via its success at Toyota.

 In the 80s came Six Sigma, which brought an obsessive focus on quality - with managers using statistical analysis to greatly reduce variation and defects.

But recently, we’ve hit a wall. The progress Ford began over a hundred years ago has largely stalled:

even if a factory has 21st-century machinery, it’s often still managed using 20th-century methods.

We believe that’s all about to change - and not a moment too soon. In this piece, we explore the transformative potential of industrial intelligence, and how it's (finally) poised to drive the next wave of efficiency and sustainability in manufacturing.

Industrial Intelligence is the sub-sector of Industrial software that leverages enterprise data to add an 

to industrial operations. It’s the application of new technologies, like

 modern data infrastructures, analytics, machine learning, and digital twins 

to the industrial sector. Industrial Intelligence means unlocking all the operational data that these organizations generate and leveraging it to drive efficiency, increase returns, and meet sustainability targets.

At this point, you must be asking yourself – how have the world’s leading manufacturers not innovated on their technology infrastructure already? Let’s dive in.

Like many “old economy” sectors, the industrial space has largely been left behind by the past decade of software innovation. Here’s why:

Buying cycles are on par with government procurement (read: glacial). 

Industrial firms often require lengthy proof of concept programs before making a buying decision. Even if ROI is proven, there’s still a bidding process, which further harms success rates. Only a few can stomach the wait - or the risk.

Niche manufacturers have less incentive to innovate. 

If you’re the manufacturer of a particular spring used in a seatbelt retractor for a Porsche 911, odds are you have very little competition. Without competition, the status quo is fine.

Complex manufacturing environments need complex implementations. 

Every customer has a unique combination of legacy infrastructure and future needs. Combine that with a byzantine and often incompatible manufacturing hardware landscape, and you quickly realize why adding a software layer is so difficult.

is an oft-repeated mantra, and for good reason. On average, switching off just a single BMW factory for only one day could cost ~$12mn.

remains a challenge. Telling a tenured production manager who “has been doing this for 30 years” that there might be a better way is never easy, especially when historic “buzzy” areas of manufacturing innovation failed to prove value until now, like 3D printing in the early 2010s.

Taken together, it's no surprise industrial software has come up short - with real costs to companies and consumers alike. 

 Why, you ask? For starters, the companies developing it are not software-natives. Of the top 15 incumbents, 

 - (Dassault, Autodesk, PTC & Ansys). The rest (Siemens, etc.) are hardware players at their core, with software contributing less than 10% of their revenue.

The quality of experience we’ve come to expect from consumer and now business software is hard to come by in the industrial sector:

Nevertheless, many incumbents realize the value of diversifying to recurring, high-margin software earnings in what is historically a cyclical sector - and they have the pockets to go shopping. Rather than develop products organically, many larger players are building out vertical-specific software offerings via acquisitions. Since 2021, for example, Siemens spent ~$2b on buying predictive maintenance tools, while Emerson and Schneider acquired industrial software giants AspenTech and Aveva for ~$11b and ~$10b, respectively.

 This inorganic approach of legacy vendors creates two major pain points in the industry:

For instance, Rockwell’s Design software suite consists of seven different sub-products, among which two were acquired (Emulate3D & Arena) and one accessed via partnership (FactoryTalk Twin Studio).

Complex offerings make plug-and-play extremely difficult. 

Instead, they require significant customization to roll out. Incumbents advertise this “flexibility” as a “feature”, but most operators hate the need to call their service provider for every small change (and the ensuing bill).

 industrial software needs to solve industrial-scale issues

. For one, analyze immense volumes of data: manufacturing plants generate twice as much data as any other vertical.

Per week, a typical smart factory generates ~5PB

 (that’s petabytes, or one million gigabytes).

Throw that at your average legacy software, and more issues quickly come into focus:

Complex infrastructure is fertile ground for data siloes. 

Across the board, experts mention that data often remains stuck on the production-line level. This leads to incomplete (meta-) data, duplicate processes, and barriers to collaboration. Most importantly, however, it hides operational insights - or in other words, better ways to run the factory. In a survey of 1,022 respondents, 

68% reported that data siloes have a negative impact on their work

. Those workers were found to spend 11.6 hours per week searching for the information they needed.

Engineers lack the tools to move right into analysis. 

Data engineers still spend around 40% of their time on data quality – time spent not solving problems.

 We routinely hear of engineers still exporting data from faulty machines to Excel, only to spend days wrestling with it.

Once you get to the analysis, doing it manually is grossly insufficient. 

Manufacturing processes generate immense volumes of data. Finding issues can be like searching for a needle in a haystack – that’s why human analysis will always be too late to be truly impactful. 

Robert Göbel, Co-founder & Managing Director, Spread.AI

In short, there is a massive opportunity to challenge incumbents’ complexity and unlock the insights hidden in operational data - by offering software that is quicker to deploy, easier to customize, cloud-native, and collaborative.

With improved intelligence tools, manufacturers can reduce downtime, improve quality, and increase throughput – aka increase profitability. And crucially, the technical underpinnings for this shift have finally fallen into place.

In the past decade, we have seen three key technologies reach maturity: namely, our ability to collect, store, and analyze data on an industrial scale.

Let’s start with data collection. In 2004, an average IoT sensor cost $1.30. By 2020, the cost had fallen by almost 4x to $0.38.

 IoT has benefitted from the relentless innovation in semiconductor technology & connectivity protocols, driving down costs and improving performance. We don’t expect this trend to slow anytime soon - if anything, just the opposite. 

As IoT sensors multiply, so too will the volume of operational data available to manufacturers.

 are supporting the infrastructure of this significant tailwind with their MQTT platform, which allows customers to send and analyze data from IoT devices in real-time and even manipulate data in motion to ensure integrity and quality across deployments.

Or consider storage. Just years ago, these new flows of data would’ve overwhelmed the average data center. But since 2010, the cost of cloud storage has roughly halved, and the

 number of industrial firms who have completed a shift to the cloud has risen from 13% to 64%.

Now, it is finally feasible (and cost-effective) for manufacturers to store this data for later use.

improvements in database technology open the door for effective analysis. 

 are key to handling the mass of time-series data produced by industrial firms, especially 

. Moreover, as industries increasingly embrace electrification and software-driven "smart" products, graph databases have become instrumental in understanding the interrelationships between mechanics, electronics, and software.

Finally, data analysis has also benefited from rapid advances in processing technology like the 

data warehouse, which has greatly reduced both latency and cost

. A prominent example is Snowflake, which has recently introduced a vertically designed 

Taken together, these technologies finally make possible a “single pane of glass” through which industrial organizations can monitor and analyze their operations in real-time. 

This is the value of true industrial intelligence – it makes data accessible, paving the way for value creation via analytics, machine learning, and digital twins.

Many industrials are facing a steep knowledge cliff: 75% of machine operators in the US are over the age of 45, yet the average 18-year-old does not consider manufacturing as their first-choice career.

 As staff retire and companies lose critical expertise, intelligent software will have to step in. 

The burden of running a factory must shift from accumulated experience to mathematical models

And in a Western world short on skills, the demand for reshoring just adds fuel to the fire. In the US, reshoring-linked jobs are skyrocketing – up 6.5x from 2012 to 2022.

Add geopolitical tensions, the lingering effects of Covid-19 or the 

, and you see why solutions are so desperately needed. 

Estimates are that organizations will need to spend ~$60b to rebuild supply chains in the coming years

Critically, software can also play a key role in our journey to net-zero

. Until recently, for example, Unilever’s Indaiatuba plant in Brazil (which makes powder detergent) was their largest greenhouse gas emitter. By applying machine learning to the spray-drying process, however, the company was able to reduce GHG emissions by 96%.

In fact, the average company applying these technologies sees improvements not just in energy use (-24%), but also in productivity (+52%), labor costs (-32%), and manufacturing costs (-26%).

The scale of the opportunity, both ecologically and economically, is massive. 

In our analysis, we assume that the global manufacturing sector could save ~25% of its COGS, in line with the examples seen above. The resulting resource savings would be equal to the entire GDP of France.

Moreover, savings are only one side of the equation. Efficiency gains are a huge resource unlock, and it's 

impossible to put a number on the upside potential inherent in giving engineers more time to focus on building great products. Our estimate: It will be big, given the sheer size of this industry. 

Our View on Industrial Software

So, when we look across the whole Industrial Software landscape, which companies do we see as the most innovative and most likely to see durable growth through the next decade? It is those companies that are working to add the

 to industrial operations: Data Operations, Operational Machine Learning and Digital Twins. We’ll highlight a few of these, together with one segment that is mature but showing bright spots of innovation – Simulation.

In our view, Digital Twins are the most innovative segment of this exciting market. They combine (some or all of) 3D rendering, physical simulations, and real-time operational data to provide a digital representation of physical products and their assembly lines. In other words, they are considered a “holy grail” in industrial intelligence, as 

they bring together 1) a data platform, 2) analytics and machine learning, and 3) automation and operationalization capabilities

. As such, this segment is growing rapidly and is expected to reach $46b by 2027, up from $13b today (a 36% CAGR).

, whose graph database-based engineering intelligence software provides a holistic view of manufactured products to power use cases across the engineering lifecycle. For example, Spread supports auto OEMs on the shift toward EVs with an understanding of how mechanics, electronics, and software interact.

As this segment matures, agile start-ups will face incumbents like Siemens & PTC, whose advantages include owning existing hardware, extensive consulting teams, and recognizable brands. However, most of the users that we’ve spoken to have questioned whether these incumbent products meet the mark. We think that the door is now open to challengers who can build exceptional technology.

Today, companies either provide pre-trained models to perform specific analyses, or no-code / intuitive interfaces that allow process engineers and other operational personnel to train and deploy machine learning models for specific needs. Players in the sector tend to specialize: 

 targets drug and pharma vendors, for example.

The application of ML/AI to highly specialized use cases can drive significant ROI for customers, as we’ve seen in the case of Unilever’s Indaiatuba plant. As such, these companies can see a quick time to value and become deeply integrated in their customer’s workflows.

 Competition is typically lower as vendors focus on their respective verticals. Market opportunity is a key consideration, however

: we see the total Industrial MLOps & ML-as-a-Service market reaching $28.7b by 2028.

 Niche vendors may need to make the leap to other industries to allow for breakout potential or cut across industrial verticals like 

. Where Axionray leverages natural language processing, Instrumental leans heavily on image data. Both are tapping masses of unstructured data to detect quality issues long before a human could and they’ve seen strong adoption from large enterprises like PEM, Boeing, Meta & Cisco – a testament to the value they can create.

Simulations help engineers close the gap between design and reality and see how their designs will - or won’t - behave in millions of real-world scenarios. This can reduce or even eliminate the need for costly physical testing. Simulation is a mature market, but there is innovation occurring at the edges. Cloud-native simulation is making it more accessible and cost-effective to run simulations, eliminating the need for expensive on-premises workstations. Leading the charge in this innovation are 

, located in Berlin. Dive is particularly noteworthy for its pioneering particle-based simulation, opening entirely new use cases – like understanding washing systems, liquid cooling, and drivetrain lubrication.

For too many industrial companies, data is still largely inaccessible and disconnected. In fact, most companies spend more time finding and manipulating data, rather than extracting any value from it. Data Ops solves for this by systematically and effectively managing data so that it can be contextualized and used for decision-making - fast. In parallel with IoT, the market is taking off and is projected to reach $11b by 2028, growing at a 23% CAGR.

, which offers a data operations and contextualization platform designed to power digital twins for industrial applications.

realized, some companies are making a strategic shift. By moving away from selling software and towards building modern factories from the ground up, these companies are no longer constrained by legacy and fragmented technology – they can start afresh using a software-centric blueprint. This can massively expand the TAM too – Industrial Software is a large market, but global manufacturing is a $16 trillion market!

, from Germany, uses their proprietary FactoryOS to orchestrate a connected factory and become the central data layer – driving constant improvement and automation to increase the quality & throughput of the parts they produce. Both 

 follow a similar approach and are also using their proprietary software to orchestrate and automate precision manufacturing operations, hopefully becoming the backbone of this industry in the US.

The North Star: Driving Return on Investment (ROI)

Industrial Intelligence start-ups hold significant potential to deliver substantial value to their customers. Nevertheless, breaking into the industrial market remains a formidable challenge. 

To succeed, companies must not only build complex, high-quality technology but also pair that with a robust go-to-market approach

. Recall that many of the challenges in this market are structural, and if technology is not the problem, then 

 building more technology will not necessarily solve it.

their north star - but when it comes to the Industrial sector, “we’ll make your business more efficient” just won’t cut it. In our view, the most successful challengers are laser-focused on defining a very specific use case for their product. This provides the basis to demonstrate tangible, quantifiable ROI. Finally, we see the best companies aligning their go-to-market approach and technology roadmap with these targeted use cases, making 

 the flywheel that drives the whole business.

Driving ROI

The industrial sector is the backbone of our modern society. The proliferation of IoT sensors, the ever-decreasing cost of storage, and the introduction of vertical-specific AI use cases serve as the catalyst for this massive technical shift. This transformation is occurring just as society is so deeply in need of software-driven solutions, wrestling with skills gaps, reshoring needs, and climate goals.

case for this market has never been stronger. Despite their immense potential, the penetration of these modern technologies remains low. There is enormous upside to be captured. That’s why we’re fired up by the incredible founders we have met who are building the next era of software to level up industrial firms and transform this $16 trillion industry.

If you’re building in this market, please get in touch!

[2] The Machine that Changed the World, The Story of Lean Production – Toyota’s Secret Weapon in the Global Car Wars That is Revolutionizing World Industry, 1990

[4] Marshall Advanced Manufacturing Center, 

Lean Manufacturing Made Toyota the Success Story it is Today

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