Making The Case For Organic Interactions: What Can Multinationals Learn From Startups' Work Dynamics?

Do early-stage startups work differently than established multinationals?

After spending a number of years in both the corporate and the startup worlds, I've been reflecting on the fundamental difference between the two. I think this is a relevant question given that multinationals 1) have been revamping their work models with methodologies like agile and design thinking in an effort to develop key startups' attributes like resilience and adaptability, 2) are increasingly dependent on acquisitions of venture-backed companies to develop a competitive advantage, and 3) are facing more pressure than ever to constantly transform themselves in order to survive in an uncertain, ever-changing business landscape.

FAMGA's disclosed acquisitions of AI startups, 2020 (Source: CBInsights)

Making the case for organic interactions

When it comes to work dynamics, the main difference between an established multinational and an early-stage startup is that the former clearly defines their employees' tasks and scope of collaboration, while the latter doesn't. When joining an early-stage startup, we are making a conscious choice about what to work on and who to work with, often getting to know all key team members in advance. When taking on a corporate job, we rarely get a glimpse of what our daily environment will look like, and once we are settled in our ability to expand our network is often limited by the organization's hierarchical structure.

When it comes to work dynamics, the main difference between an established multinational and an early-stage startup is that the former clearly defines their employees' tasks and scope of collaboration, while the latter doesn't.

Although I have used the word employee when referring to people working in early-stage startups, they rarely perceive the startup as an employer, at least not in the traditional sense. They often have to get by without a paycheck, work overtime and face an incredible amount of pressure, yet you'll find they often have the highest levels of engagement and compromise. They might be tired, stressed or overwhelmed, but they aren't bored, unenthusiastic or unchallenged.

Top 20 reasons startups fail (Source: CBInsights)

There are two questions that will define our employee experience, either at an early-stage startup or at an established multinational:

1) Am I receiving the reward and recognition I think I deserve based on my perceived contribution to the organization?

2) Do I consider the level of reward and recognition my peers are receiving is well deserved?

If the answer is negative for only one of these questions, we might still have an overall positive perception of the organization. However, if the answer if negative for both of them we are likely to stop thinking of the company as a platform where we can fulfill our potential, and instead perceive it as a barrier or obstacle preventing us from achieving that very objective. It is in that process of disambiguation between employee and employer that disengagement originates.

In a way, this process of disambiguation is error-proofed in early-stage startups. This is because having a strong level of compromise is a sine qua non for the startup's first team members to effectively navigate the span of time from the moment the company receives its initial capital contribution until it finally begins generating revenue (also known as death valley, illustrated on the graph below). If that level of compromise is not present during that period, then the startup isn't likely to survive its first months of life, thus error-proofing any unproductive combination of team members' personalities.

Such level of compromise can only be achieved when the team's interactions arise organically and are driven by genuine interest rather than a hierarchical mandate. Note that the chart above by CBInsights highlights "not the right team" among top 3 reasons why startups fail, yet burnout occupies the 19th position despite being unfortunately common in early-stage startups.

Lifecycle of a venture (Source: UC Davis Center for Entrepreneurship)

But how can large organizations allow people to work on what they want with whom they want while running operations involving thousands of employees? The key lies in enabling and fostering organic interactions at scale, aligning the organization's formal decision-making processes with its informal reality through the consideration of social capital metrics.

The key lies in enabling and fostering organic interactions at scale, aligning the organization's formal decision-making processes with its informal reality through the consideration of social capital metrics.

Because we can't improve what we can't measure, in order to enable organic interactions at scale we must first find a way to visualize and analyze the informal interactions that exist within our organization while respecting employees' privacy.

Organizational networks as a framework to understand organic interactions

As more than 70% of companies now say they consider people analytics to be a high priority, decision-makers are increasingly getting access to the tools and resources they need to unveil hidden patterns in their internal collaboration dynamics and translate these insights into proactive actions that achieve sustainable value.

When it comes to understanding and enabling informal interactions across the organization, Organizational Network Analysis (ONA) provides companies with a powerful framework to monitor social capital metrics that complement human capital metrics across multiple processes. While human capital refers to employees' traits and skills, social capital refers to the relationships they build within the organization. By leveraging active and passive data sources such as online surveys and internal collaboration tools (e.g. Office 365), ONA can identify informal leaders and monitor burnout risk and productivity to create value for change management, leadership development and remote work management among other use cases. Actionable insights can be generated while respecting employees' privacy, e.g. by analyzing employee metadata without accessing the content of the communications or aggregating the results at team level without displaying identifiable information.

Visualization of an informal organizational network (Source: Cognitive Talent Solutions)

Mercer's?2020 Global Talent Trends Study?reported that 44% of companies say they use organizational network analysis and 38% apply intelligent “nudging” technology to help employees make better choices. With new AI engines acting as super-connectors and making automated introductions for 1:1 video meetings, it is a matter of time for AI-powered organizational network analysis to become a mainstream practice and enable multinationals to give their employees the collaborative freedom they need to realize their true potential.

Conclusion

As established multinationals face more pressure than ever to constantly transform themselves in order to survive in an uncertain, ever-changing business landscape, People Analytics methods like organizational network analysis (ONA) can enable them to replicate successful work dynamics from early-stage startups and enable organic interactions at scale. Only this way can they transition from hierarchical, siloed structures that limit their employees' collaboration to more agile, decentralized systems that provide them with the collaborative freedom they need to realize their true potential and maximize organizacional success.