The journey of growth that an entrepreneur goes through is like the journey Khaleesi undertook with her dragon eggs – from incubating the dragon eggs, to believing in the idea of dragons flying again over Westeros to helping them grow and finally seeing them to their maturity. Just as she faced unique challenges in her journey, similarly an entrepreneur has his/her set of struggles basis which wave of business cycle they are riding. And each phase should be handled with care, because the parenting technique one adopts for a toddler would be no way like a teenager.
Phase 1 – Jumping in the fireIs your idea good enough for you to quit your job? Is the idea good enough for you to get someone else to quit their job and join you?An entrepreneur must be a passionate believer in what they are doing and must often be THE primary proponent for the idea. Startups are brutally difficult and founders must be tough from get go to weather the storm. If a founder can’t even convince themselves to quit, it’s probably never going to get off the ground.
Phase 2 – Is the idea good enough? Building the Market IntelThe idea phase is an emotional rollercoaster for any entrepreneur. One minute you may be convinced that you have the next million-dollar idea, and the next minute those self-doubts creep in, and you will start to feel like a failure before you’ve even put anything into action. To douse the fire of self-doubt one should spend a significant amount of time into the market research, collecting data about primary & secondary audiences. This helps in answering key market variables – is the idea based on a compelling value proposition, is the market timing correct and is the market size big enough for the idea. And most importantly, at the end of this stage one should know, who would pay for the product & service & why?
Phase 3 – Business PlanningWhile assembling the building blocks of the business, battling feelings of loneliness and vulnerability are likely going to be the order of the day. The key to surviving this phase is to appreciate the concept of delayed gratification. For now, you must focus on putting in the groundwork that will facilitate that success. What is essential in this phase of the journey is to create the actual business plan with key company milestones for the next 2-3 years and identifying the core team. The business plan should be able to answer the following questions:
- Can you find a scalable way to acquire customers?
- Can you then monetize those customers at a significantly higher level than your cost of acquisition?
Phase 4 – Building the productA phase full of mixed emotions. Have you simply proven that you can get a few customers, or have you proven that there is a real business to be built here?
Essentially, here the startups are starting to prove the business concept, but it may not feel all that good because the end goal is still a long way off. This is the stage of building the MVP or Minimum Viable Product to test the business idea. This is an opportunity for learning about deficiencies in the product, critical features, and, most importantly, to get real-world feedback from someone not on the core team. In many cases, the entrepreneur may learn that the potential customer uses the product in ways that they did not expect.
Phase 5 – ValidationThis phase is perhaps the most challenging mentally and physically, because the entrepreneur will be working around the clock with very little letup. This is the validation or proof of concept stage, where the entrepreneur must show maximum value to all stakeholders starting from its current customers, its employees to current angel (if any) & potential investors. On one side, there is the struggle to find the right product strategy & brand positioning that would allow to attract potential Series A/B venture investment, and on the other side, there is a continuous pressure to show some running profits and ensure customer delight. Incidentally, most of the startups lose their plot during this stage of business.
Phase 6 – Scaling up and growth pathSo, is it smooth sailing from here on out? Business unfortunately doesn’t work like that, and always there will be challenges on the horizon. Finding successful financing, maintaining the cash flow till the company achieves its key milestones, hiring key resources, marketing the product in the target markets to key audience, and accelerating the quarter on quarter revenues are just some challenges a startup has to deal with. Once the startup has achieved a critical mass of customers, they enter the growth stage in which they can diversify the business through possible acquisitions of smaller companies or can enter newer markets by raising more venture fund. Fundamentally, there is no fixed time duration to this stage as most of the startups want to remain in the startup mode for a long timeBut although some of those challenges would make anyone think was it all worth it, the entrepreneur has earned his/her battle scars by then and is obviously far tougher because of it.
Artificial intelligence (AI) has created a lot of buzz for years, but the coming years hears more about discussions centered around machine learning (ML) – an approach to AI that focuses on data sieving, data interpretation and learning and taking informed action based on the analytics using algorithms.
In other words, ML is the motor that drives data science.
Since a Machine Learning system can assess new data, its behavior and performance while operating unsupervised, enterprises across all industries are keen to experiment and implement this progressive approach for its ventures. Many businesses have built in-house data science departments or are inviting external teams to guide them in solving real business issues through the smart use of data.
But the real value of machine learning is the ability to make decisions based on what it has learned from the past, how it has been trained using datasets and intelligence and not what it is currently exploring and evaluating.
Though the anticipated commercial impact of these efforts has been discussed extensively but the realistic challenges of applying these technologies to real business can be often overlooked.
So to avoid such costly mistakes, it is necessary to ask following simple yet important questions before embarking any machine learning project-
Is there really a requirement? –
Ask yourself if you are adopting the new technology just for adoption’s sake or is there really a requirement. Often, you’ll find there isn’t any need of the same.
‘Machine learning is valuable only for use cases that benefit from dynamic learning – and there are not many of those’ – David Linthicum’
Vendors do create a need for ML cloud services, selling in the name of good fit for applications which shouldn’t use it at all. As a result, the technology seems to be misused and over-applied, diverting valuable resources from projects that can actually drive growth.
Do you need a CAIO (Chief AI officer)?
– Implementing ML requires a good understanding of the technology and a strong business vision. Though anyone from the company (CIO – Chief Information officer, CDO – Chief Data officer, and CTO- Chief Technical officer) can own the overall implementation but a CAIO is the one, who can view an organization’s potential projects that can be scaled and positioned for ML adoption and is responsible for setting a roadmap that ensures the AI integration is in line with the company’s overall strategy. He/she ensures that the right product is selected that meets the business goals and the required resources are readily available.
Are your employees scared of robot colleagues?
– The biggest challenge faced by companies is that their ‘employee’s fear change’ thinking it will make their jobs irrelevant. So the need is to empower employees and management and encourage them to engage with the machines. The hidden fact is people are unaware of the integration problems. Even if a system is developed to perform individual tasks, a person cannot be removed completely from the process – since there are interaction issues such as coordination and communication between people and AI systems. So take up a transparent approach of how machine learning will be integrated, what it means for jobs, and train people to engage with their new robot colleagues.
Is your organization mature in data capabilities?
– Machine learning requires quality data – lots of it. Organizations planning to adopt ML, need to work on their data capabilities first. Since if the data is not clean and extensive, you are not going to be successful in your machine learning efforts. Incorrect and inaccurate data results in erroneous solutions, bad decision making and potentially bad AI implementation. Apart from that, the data needs to be secure with regulations that hold back organizations from using personal/ sensitive data to train their algorithms especially in healthcare and finance industries.
Can you attract the essential Talent? –
While people can be trained on machine learning implementation, it still requires someone with a data science or machine learning background to cater to implementation hiccups. But unfortunately, trained and experience AI and ML professionals are thin on the ground. With more than 500% rise in the number of jobs in AI, less than 30% have the required experience. So, the organizations need to make themselves an attractive proposition with interesting challenges and competitive salaries to ensure more talent flows into their pipeline.