Month: December 2019

Building effective teams

~ Shashank ~ Leave a comment

Effective teams are at the core of any organization’s success. Ask any successful leader tasked with achieving the impossible to pick the top 5 things that contributed the most to their success and chances are that they will attribute a large portion of their success to their teams. Ask the same managers about the most challenging tasks they faced in their journey and chances are they will call out their team building efforts as one of them. So, despite the widespread wisdom of the effectiveness and importance of effective teams, why is it so difficult to build one?

As a member of some very effective teams and also some not so effective ones, I struggled with this puzzle for several years. I secretly dreaded the day when I will be called upon to create a team of my own before embarking on a deal-clinching record-smashing type of endeavour. Blaming the poor team dynamics for missed targets and lost opportunities is an easy way out of an organizational quagmire for most leaders. I took mental notes on getting out of tricky situations by observing my leaders and peers, and their modus-operandi. This was before the time when I started harbouring desires of starting a small company. Owning and running the operations of my own company did not offer me the cushion of learning from my own mistakes. I had to observe and learn from the mistakes of my peers.

What follows is a distillation of some very personal and unorthodox ideas on what makes a team tick. Careful, these are not ideas to build a great team but effective teams. At this juncture, I find it important to state the difference between the two. In my humble opinion, effective teams are excellent at achieving targets and meeting goals. If you need to grow your business by venturing in new territories, you need effective teams. This team will do all the necessary market analysis, competitor research and product development to meet the deadline for a perfect launch. But if you want to put the first man on Mars or dive to the yet unexplored depths of our deepest oceans, you need a great team. Great teams are driven by something more than just goals and deadlines. They are hungry for making a mark and leaving a legacy. But more on great teams in some other post. Let’s focus on effective teams for now.

In the following paragraphs I’ve tried to be honest about my experiences in effective teams and my analysis of what worked and not. As is with most things on internet these days, I am sure that my views might not agree with several others’, but please consider this not as a commentary or critique on any previous work but my sole opinion and should be consumed as such.

  • Effective teams need effective leaders – I cannot stress enough the importance of leaders in taking teams to great heights. I believe that effective leadership is the single most important thing that makes teams achieve extraordinary results. On several occasions I have witnessed from close quarters how brilliant individual contributors flaked when led by ineffective leaders. In my most recent paid engagement with a client, a team was managed by a very under-confident and ineffective project manager. The result was that the members were perennially dissatisfied by his approach to things and could not concentrate on their work. This had a rippling effect on the team’s feedback from important stakeholders. Miraculously, when the project manager left to pursue his own ambitions, and the team came under the aegis of a better leader, the team found its confidence back. Unshackled by earlier hurdles, I saw the team quickly rise from the ashes. After over a year of mismanagement, the team was able to turn things around within 6 months and was soon in the good books of the executive leadership.
  • Diversity not just for the sake of being diverse – Diversity in teams can be a powerful factory of ideas and resources. It is difficult to over-emphasize the value and perspectives that diverse participants bring to brainstorming sessions and to the understanding of a problem. However, it is also of vital importance to not fall in the diversity trap for earning brownie points in management meetings. But is diversity only limited to parameters like race or geographical origin? In keeping with the times, organizations spend considerable resources in building diverse teams but the element of diversity is only limited to gender, race or social background. Subtler traits like education specialization, economic background, technology specialization, age groups etc. can also provide the diversity that can be a significant force in teams. Seemingly unrelated and less relevant sources of diversity can also bring new ideas that give dimensions to problems that were previously unexplored.
  • Informal structures create flexible teams – I have been a huge proponent of creating informal structures within teams from the very onset. Informal structures refer to the invisible and undocumented channels of information and power flow within the team, not restricted to the prescribed or official channels. The flow of information over lunch tables and coffee breaks cannot be replaced by email clients and collaboration platforms. The sense of ‘brotherhood’ that compels members to go the extra step to pull their brethren out of a tough one cannot be replaced by overtime and all-nighters. Creating these informal channels is the tough part and there is no one formula that works. In my experience, a lot of it flows from the top down. A rigid structure at the top and adherence to official channels by leadership will only promote adoption of formal structures. Geographically distributed teams that do not get to meet often also tend to adopt more official channels in their interactions. On the other hand, a leader that promotes informal team gatherings and uses these gatherings to disseminate information fosters growth of informal team dynamics that last longer than official structures do.

My ideas are evolving and like most things, are an interpretation of my understanding of the complex human society that has evolved over centuries. It is imperative that I will have different ideas in the future about effective teams and will keep adding to this blog. In the meanwhile, I would also like to learn from our readers on their ideas of building effective teams.

What does AI mean for Insurers

Featured ~ Shashank ~ Leave a comment

From Vicky, the humanoid in 90’s kids show Small Wonder to Ava, the sentient, Turing test cracking and starkly human looking robot in Ex-Machina, we have always fantasized a world with Artificially Intelligent computers. While the former example was a lighter take on what AI could look like in everyday world, the latter was a more serious attempt to rethink the same concept. But between these two stark contrasts, AI is being effectively used by businesses today to perform several tasks that would have required humans to sweat tirelessly for weeks and sometimes even months.

On a high level, the term Artificially Intelligent can be attributed to any computer or machine that replicates a human behavior. With that broad a definition, even an IVR system is intelligent. After all it tries to do what a human would have done i.e. try to ascertain the purpose of your call and connect you to the right department. However, given the advances that the field has made in the last decade and for the purpose of this article, I will refrain from using such a broad brush to paint the industry practices. The widely accepted and more refined definition of artificial intelligence goes as below.

A system’s ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation.

An average Joe’s life is surrounded by intelligent machines. For example, the YouTube recommendations that pull you into a never-ending spiral of binge that by the time you look at the clock it is already 3am or the innocuous spam filter in your email that quietly works in the background without you ever noticing it. More complex AI algorithms are being employed by businesses to predict stock movements, presence of cancer, real time face recognition etc.

While other businesses have made significant advances in adopting AI in everyday tasks, the insurance industry is still seriously lagging behind in making any meaningful mark in the discipline. In my modest 7 years experience working for large insurers, I have not come across any path breaking implementation of machine learning, deep neural networks or other relevant concepts in real life. Some carriers have realized that they are late to the party and hence are rushing to acquire the necessary skills and teams to build a data analytics and AI practice but their progress is still in nascent stages. Lack of a holistic strategy is what keeps the players from making game changing progress, something that AI is absolutely capable of delivering.

I have tried to list some use cases that are radical but totally worth exploring for establishing the next generation of Insurance products and services.

  • Custom risk assessment for accurate underwriting – Since the early days of life insurance, most insurance companies have relied on four parameters for a customer’s risk assessment – Age, Gender, Smoker Status and Occupation. These parameters determine your basic mortality risk, which is sometimes peppered with a few more historical parameters like a health assessment and lifestyle preferences questionnaire. In our opinion, these methods of determining insurance risk is outdated and needs a massive overhaul. Life insurance risk assessment can incorporate so many relevant data points available from wearable devices, lifestyle expenditure patterns and social media feeds for a more accurate and customized profile. Just because I am a 40 years old smoker who works in sales doesn’t mean that the level of risk I pose is the same as another 40 year smoker salesman who regularly runs half marathons, shops at organic stores and is a regular at his gym.
  • Remote surveillance after a natural disaster – Imagine the plight of property owners who have been hit by a massive hurricane like Dorian or Katrina. In the midst of all the chaos and loss suffered by the owners, they also have to deal with insurance providers who are not able to reach the premises due to infrastructure collapse. It takes weeks and sometimes even months to get the infrastructure up and running before any kind of inspection can be performed on the property. Some insurers have started using aerial drone footage to do preliminary assessment of the destruction, but it is still limited to mostly manual identification of broken roofs and fallen trees. Deep neural networks are capable enough to identify damaged houses, vehicles, swimming pools
  • Intelligent telematics – several insurers have started to invest in solutions and devices that allow them to monitor customer’s driving styles. Though most of these programs are in nascent stages, their fundamental premise is far from leveraging the true capabilities of intelligent telematics. My auto current insurer offers a ‘program’, which when enrolled in, gives me 5% discount on my insurance premium and also rates me on my driving every time I take out my car. However, the system is far from being ‘intelligent’ as the insurer claims it to be. I understand the ‘under-the-hood’ working algorithm that powers the program. Rather than assessing my driving style and marrying it with the driving context, the program only uses an algebraic formula to give me points on my speed, cornering, acceleration and breaking. The cumulative score I get for every driving session reflects the discount that I will be entitled to at the end of the year. Instead of such half-baked algebraic telematics, the new age object recognizing algorithms that power self-driving vehicles can be used to better assess a driver on the road. Things like how long after the front car’s break light went off did the driver apply breaks is a far better representative of the risk drivers pose than a driver doing 5 kmph on a highway.
  • Intelligent digital assistant – An insurer provided digital assistant, akin to Google’s assistant or Apple’s Siri, can smoothen out many kinks in customer interactions for which they have to call the contact center. Simple pieces of information like my next premium due date or tasks like updating my address should not require me to pull out my policy documents that are lying in a pile in storage or call a contact center, only to be put on hold for 30 min (true story). Add to that the fact that most life insurance policies require customers to make payments once a year, makes it even more unlikely that customers will reach out. No wonder, majority of adults do not relate with their carriers in the same way they relate to other e-commerce partners like Uber or Amazon. An intelligent digital assistant that is available 24×7 and is intelligent enough to answer basic financial questions will make the customers feel connected with the carriers. Empowering the customers with technology will not only result in a recurring relationship but also impact the bottom line of the company.
  • Pay as you use insurance – Life, auto and several other types of insurance are typically renewed after a certain period of time. My life insurance contract is renewed every year when I pay my premiums and my car insurance every 6 months. It doesn’t matter if my car spent 2 months in winter sitting in my garage because I prefer to take an Uber when it snows. I still pay the same amount of premium for my car insurance. On the other extreme, it doesn’t matter if I suddenly decide to take a long road trip across the country to see my college friends. Though futuristic, usage-based insurance products are possible with the amount of data and connected devices that are available. Insurers can develop, with the amount of data that is available with them, models to accurately predict increase or decrease in risks for departure from regular trends in customers’ lives.

I have been working with Insurers for the better part of the last decade and have seen a recent uptick in technology exploration and adoption. A lot of my clients have shown interest in building an AI lab that gives them immediate result on the balance sheet. My recommendations to all my clients have been the same – AI is not something that you can install in a year or two and keep upgrading every 6 months thereafter. It is a continuous journey that requires a fundamental overhaul of how we perceive data, leverage it and keep improving our models for better accuracy and relevance.