One group consists of creative innovators with outstanding ideas and products, the other is embedded in a long-existing system based on a mostly very well oiled but conservatively running machinery room, a typical large corporate. It is indisputable and obvious that individual innovators do exist on either side of the fence, but experience and observations have shown that actual innovation is being lived tremendously differently — in particular in getting something ‘new’ done that will eventually become a game-changer. Both groups are trying to collaborate for the good of either side, but they’d fail in the majority of cases. When closer analyzing root causes of misinterpreted conversations, assumptions and joint misled projects between tech startups and corporates, one can identify certain patterns, which can be clustered into the following dimensions:
Perception: reality vs theory.
Decision: egalitarian vs hierarchical
Motivation: change vs continuity
Communication: low context vs high context
Undoubtedly, there are various more dimensions or different ways to have individual items be clustered together, however, these four have been observed to be taking a significant role when ‘things went wrong’ between tech startups and corporates. Thus, let’s have a closer look at them.
“REALITY IS MERELY AN ILLUSION, ALBEIT A VERY PRERSISTENT ONE.” - Albert Einstein
During the lifecycle of a tech startup from the time before the beginning to pitching to prospective customers, there are typically a few phases.
Creativity phase: —
Mostly an inherent motivation to start questioning the present, thinking about doing something differently and looking for greater innovation. Well known and successful creative innovators include for instance Steve Jobs, Elon Musk but also people like Galileo Galilei, Leonardo da Vinci and Albert Einstein
Solution exploration —
Identify a new (or slightly similar) use case based on leveraging (existing) technologies and methods, for instance Facebook based on web technologies, BitCoin based on distributed ledger technology, or simply a light bulb.
Proto-typing —
Creating a prototype in the area of information technology has become significantly easier and quicker than Da Vinci creating his flying machine that shows many similarities to today’s known choppers. It would take some more centuries until Brother’s Wright would set the foundation for aircrafts, which took them many years. In today’s world, a prototype can be created during a hackathon in often just 2 days.
Problem identification —
As fast as creativity leads to a prototype nowadays, it is missing the actual purpose: What problem are they solving, or are they even creating new ones? Machine-learning face recognition has created thousands of jobs, but in the context of the pandemic and government and intelligence driven analytics, big players like IBM and Google have been putting their efforts on ice. Remains one question: What has been the intention to work on this technology in the first place?
Customer identification —
Once a prototype is available, the problem identified and a nice story line developed comes in the next question mark: Who are the customers? It often shows up as “metal industry”, “finance industry”, “governments”, to name just some. So startups are pitching to their potential customers, outlining a problem statement they often don’t know if it’s even true or a priority and a proto-type that might or might not even be working for the customer. Most famous observation was offering banking products to a car insurer.
Proof of Concept —
The customer is putting trust into the innovative ideas and the team behind it, so they jointly work together on identifying a use case and develop a mostly costly proof of concept to see if the solution could solve gaps. They define some KPIs based on professional judgement and eventually help the startup scope, define, develop and validate their product.
Go-Live —
That’s the few cases where there is a product fitting to the customer’s needs, solving a specific problem, and financing is available.
1. Perception - reality vs. theory
Startups often design products and plan projects under the assumption that corporate customers will be able to (easily) obtain and work with relevant data. However, this is frequently not the case.
While corporate investments in technology have exploded in recent years, most large corporations are facing the consequences of decades of underinvestment in upgrading 1960s-style legacy systems as part of an overarching strategy.
Thus, time to market for new products with ideally standardized and centralized processes became a big hustle as corporates have to quickly make up for their decentralized and inconsistent approach.
While corporates are aware that their own data is a key asset, a key challenge for many C-level executives is to find the right balance between investing in new products, sales channels, and end-user experience, and investing in tackling legacy applications and centralizing technology and data.
Thus, startups should design their solutions to be data point agnostic and provide functionalities to run data transformation processes within their solutions.
Another key aspect startups are often misinterpreting is the response time of corporates. The typical working style in corporates is not like the one of the Googles in the world but rather like a public agency in many cases. And they’re constantly faced with fire fighting rather than using their time for strategic planning. This truly is the opposite of startups, where everyone works as long as they can stand as they’re part of a mission to achieve a common goal, and they would do everything to close the next (or even first) deal to best serve the client.
Both would need to learn to understand the other culture, but unfortunately startups will have to be the patient ones.
2. Decision - egalitarian vs. hierarchical
A major reason for dissatisfaction at startups is caused by endless delays, questionnaires, and ever-lasting contractual negotiations even for small projects. While there are some good reasons to have specific internal policies and processes in place, eg for procurement, legal, IT security, data privacy etc, particularly large corporates have become masters in overcomplicating even the simplest processes requiring the involvement of various different departments. Consequently, following internal processes and procedures can take weeks, sometimes months, and unfortunately there is little to no movement for simplification.
Startups need to learn to be more patient and not to get nervous too quickly, followed by sending reminders and making follow up calls. Based on observations, there are little chances that it might help.
That being said, one lesson learned is to provide demo accounts on SaaS solutions which speed up the process significantly.
3. Motivation - change vs. continuity
When comparing the motivation between startups and corporates, there are few dimensions where both parties oppose each other.
First to be ‘Learning vs. Doing’. While startups usually do not have enough subject matter expertise or knowledge about how large companies are operating, they need to learn and understand to better design and market their products, they basically live being entrepreneurs to change the status quo.The opposite in corporates, there’s the daily job from 9–5 with security, a good salary and a pension plan, thus often no motivation in changing the status quo. When picking a solution, their motivation is to find something long-lasting, provided by a trustworthy provider that is going to be able to deliver also in years from the moment of procurement — as they want to avoid to run through the same process all over again.
While startups often use closed deals as reputation and statement of credibility to start new deals and increase the value for financing rounds, corporates want to make sure that everything is up and running. Thus, smart startups will emphasize their credibility and sustainability, ideally along with the strategy of the prospective corporate customer (such as demonstrating their contribution to the UN’s Sustainable Development Goals, or SDGs) which most corporates have demonstrated their support for.
4. Communication - low context vs. high context
Startups and corporates are not only two different cultures, they even talk different languages. And unfortunately, most problems in their corporation are related to communication.
Picking two terminologies as an example will illustrate how varying the interpretation can be.
‘Digital Transformation’ is understood in corporates often as automating existing business processes using technology, i.e. RPA. A startup would rather say it’s about changing how things are working.
“AN AUTOMATED STUPID PROCESS IS STILL A STUPID PROCESS, IT’S JUST FASTER.”
‘Innovation’ is all about game-changers from a startup perspective, something like blockchain and AI. In a corporate world, I’ve observed that pretty much everything that’s new is innovation including access to emails on a smartphone. But differences in the languages are even more embedded in the cultures of both. For many decades, corporates have explicitly, but also implicitly, developed their own language, mostly to ensure their communication is clear and standardized. Most popular example is probably the usage of abbreviations, such as BoM, CXO or corporate structures such as COO-A3-Ops-LH3–1 — how else would a corporation be able to sometimes organize dozens and hundreds of thousands of employees. This has developed as a crucial part in the corporate’s culture, but makes it difficult on the other hand to be properly understood by tech startups who unusually use a more simple and direct language with significantly more technology-specific terms. Due to their long-standing developed communication culture, corporates usually assume that the other party does understand what they’re saying, on the other hand they’re too proud of asking startups questions on terminologies they are not familiar with. Thus, tech startups are communicating in a very low context, whilst corporates are typically on the other end: more high context.
Concluding with this short analysis, the best practice recommendation for startups to be successful is to better know and understand the corporate culture. It won’t change soon as it’s a big machinery, pretty much drifting towards an iceberg — but unlike the Titanic probably too big to sink. I will be discussing more concrete measures, tips and tricks in a follow up paper.
By Patrick Boscher from pabora.io (independent advisor and former global RegTech lead of the Allianz Group, one of the largest integrated financial services providers operating in over 70 countries). He’s now working as strategic advisor and board member to startups, also consulting large corporates and other institutions. He’s seen the relationship between startups and corporates from both sides of the fence.