Collaboration, on the heels of eventual competition in startups and businesses

A siloed approach to ‘secretively’ come up with path-breaking solutions may now be passé. It is manifest in the overriding complexity in the environment that has brought the big boys and startups to collaborate and complement each other in various ways.

Is 'co-opetition' a more inclusive term than collaboration, when seen through the lens of enterprise-startup relationship? While two entities (however different in structure and culture) can come together (collaborate for a common purpose), at some point, they also compete with each other and disrupt respective market shares.

Co-opetition as an idea is more realistic

For decades, the banking industry was reluctant to change, its disinclination almost taking legendary proportions, till the fintech startups came along and forced change upon them. To their dismay (at that time), the fintechs disrupted the banking world through digitisation and increased transparency to bring in a very high degree of personalisation. Talks abound that in the next three to five years, the banking industry could lose as much as 25 percent of their revenue to such new kids on the block.

Staring down at the prospect of layoffs and imminent restructuring, the reluctance soon dissipated and banks started to collaborate on new technologies with fintech startups and retain competitive advantage. Co-opetition as an idea is more realistic, shorn of peripheral cushioning, including suspicion, and not without good reason. When push comes to shove after the initial months, either party may harbour the feeling of being short-changed. For instance, startups’ obsession with intellectual property (read lines of code) can often get in the way and lead to barriers in communication. On the other hand, large enterprises are built over several decades, as customer outreach and capabilities take a very long time to mature.

How much push do you believe would be required to make them share expertise, business acumen and even office space with folks who are but a threat to their existing business model?

In reality, people have worked around these challenges adroitly and to say that the collaborative model has worked well is but stating the obvious. Those of you who have read the book, you will recollect, Louis Gerstner of IBM was able to 'Teach the Elephant to Dance' and so have many others after him. But, frankly, pachyderms are not known for their sense of rhythm or very large enterprises to display nimbleness. Those straddled with legacy systems find it difficult to bring in the desired degree of innovation. It is not just difficult but daunting and expensive as well. A far more workable a proposition would be to collaborate with startups that provide niche digital capabilities and work with them to integrate with the 'digital' way of doing things and offer composite solutions.

This integration-of-technology bit and catering to clients will soon be a given feature and will usher in the need for a high degree of differentiation. In industries where entry barriers are low, we are often in the midst of a red bloodied ocean. What is that one factor that will make consumers choose one company’s offerings over another? Perhaps startups are able to point towards the right answers, given their ability to customise solutions and expand the outreach at short notice.

Maturity is the key to a startup’s growth

'Me-too' following sees much fanfare when it comes to mushrooming of incubators and accelerators in India. Having a vision and helping Indian startups mature to the next level is critical to success and eventually scaling up of this model. Moreover, the large IT companies in India are in services and may be found deficient in product mindset. The gestation period is longer, and hence a lot of patience is required. On the other hand, there are way too many instances where startups obsess over technology that’s on display and pay little heed to the actual problem that they’re trying to solve.

The new-age technologies like machine learning and artificial intelligence require a huge volume of input data sets to make it work. The quality and volume of input data will be instrumental in arriving at the desired output. This is an area where enterprises can provide support.

Concluding thoughts

In the end, it is obvious that collaborative models work because entities bring in complementary skills. But, essentially, they are both trying to solve one very large problem for a specific customer base. Like everything else this model too will have a shelf life and will work well as long as the win-win component is in place for both parties.

That leaves room for competition in future. So what if the skills are complementary? If machines can learn from humans, is it then any stretch at all to believe that the latter can emulate their own kind, and be wanting to wear their shoes as well?