On Jan 1, 2019 I decided to get out of Facebook, WhatsApp groups and Twitter for a month. I stayed on LinkedIn and this blog though. I came back on Feb 1.
The reason to leave was three fold – one, there were a lot of things going on in social media on social and political issues that I felt compelled to respond to , and in the process had to pick up a lot of negativity . I tried to unfollow a few people to see if that was enough – and it was not nearly enough. Two, I started feeling its taking a disproportionate share of my free time which I could use for other things like reading books that I bought over holiday season but did not get around to finishing, or take my dogs for a long walk, or go out with my family. And last but not least – privacy concerns !
Lesson – It’s about freeing the phonefirst
I thoroughly enjoyed the experience of staying away from (most of) social media . The first few days was difficult and I had to dig deep to not open the apps on my phone. I took the easy route and just deleted FB and Twitter on phone, and logged out of the accounts on browser on my Mac. That is all that it took – the need to look at my phone frequently went away fairly went away. And the stats on iPhone confirmed my screen time came down drastically. I started becoming more present in real life conversations – and started doing more phone calls . And what else improved ? Battery life on my iPhone ! That was like winning the lottery 🙂
What did I miss ?
I missed out on several “breaking news” type things – including a few where I really could have said “I told you so !” to some folks (and probably would have felt really good about it too ). That was the greatest temptation to “cheat” and take a quick look back at my FB and twitter feeds. I missed photos and videos of dogs – which is the best part of FB for me.
Watch it, don’t just read it !
My biggest “find” in this time was Youtube. I generally used Youtube to find old songs I love, and occasionally clips from dog shows I could not attend . I found out there are a lot of other things on youtube that I had previously not spent time checking out. The two sets of videos I love the most now are David Rubenstein’s interviews and the ones from Stanford Business School.
Back on the grid as a changed man 🙂
On Feb 1, I logged back on twitter and FB from my Macbook. I also joined back just one of the Whatsapp groups I exited – to keep up with some dear friends. I Interestingly I did not find the urge to keep scrolling to find what all I missed. I just responded to a few friends who had left messages and announced that I am back on both platforms. I think the FB algos have eased off on me and I see much less offensive content now. I also did myself a favor and decided to not re-install the apps on my phone. So now I get to look at both only twice a day or so – which is perfect. And my ability to not get drawn into useless discussions seem to have improved a tad.
Some of you might know that I have a zero inbox policy on email. A side effect of that habit is that I cannot ignore notifications on any app. So I turned off notifications on everything but email and slack. If I have any regret – it is that I did not do that years ago.
What next ?
I do think both FB and twitter still serve some useful purposes for me – which is why I did not totally delete my accounts. But I now know that if I decide later to delete the accounts in both – I won’t miss them nearly as much as I thought as recently as two months ago. And I think I will probably take sabbaticals from both more times during the year going forward.
In 2015, when I was given the chance to lead a services portfolio that included AI, I was quite excited for two reasons. One – it perfectly suited my geeky interests (especially my passion for math) and two – it seemed to me that I was back in 2000 with ERP, with every company wanting to do something significant with AI . Four years of working closely with assorted clients on AI projects, building teams, and getting asked similar questions all the time – I think it is probably time to step back and share a few learnings from the initiatives that went exceedingly well and those that did not get anywhere.
To keep the blog to a reasonable length, I am going to make some groupings and generalizations and I do have a worry that some nuance will get lost as a result. So if something needs to be challenged – pls do so in the comments, and I am happy to explain what is on my mind in some more detail.
While there is a lot of debate about AGI, potential job losses and so on – I don’t think that is what is hurting adoption at the moment in commercial companies. So I am ignoring a discussion on those kinds of topics here.
As always – all this is strictly my own personal opinions, and not that of my employer.
1. Adoption has not exactly matched what ERP did in the 2000s
AI adoption no doubt has increased significantly – but certainly nowhere near what we saw when ERP exploded twenty years ago. There are many reasons – fragmented market, lot more hype and scare tactics thanks to social media being a thing now, AI initiatives being considered a science fiction type project etc. Also, I think the academic influence on this field is a bit of a double edged sword. On one hand – its a young field in many ways and it needs a lot of R&D. On the other hand – we also need a strong focus on applications and not just the theory. Till that balance happens – I don’t think adoption will catch fire. I also think going after low hanging fruit alone without a road map of how AI will influence the business has played a part in adoption not being where we all expected it to be.
2. “Fire and forget” does not work at all
In a large part because of ignorance, a lot of initiatives did not understand that as data changes over time – so will the quality of output of that AI makes out of it. So you will see great results and declare a Proof of Concept a success, and then a while later you start wondering why the decisions look so bad. A lot of attention need to be given to the life cycle aspects and only a minority of projects do so now.
3. Focus on business is key
There are plenty of courses on AI and they are inexpensive and generally of high quality. And yet – awareness of what problems AI can actually solve is still very low amongst business and IT executives. Pardon me for saying this – but anyone who says things like “use AI to re-imagine xyz” should be stopped and questioned on specifics before allowing to continue their pitches. We should be way past the point where we should be talking at such a high level . The conversation should be about what unsolved problems can we solve now with AI, what extra benefits can the business get with an approach that uses AI to already solved problems etc.
4. Focus on IT is ALSO key
AI is not all business oriented either – it needs a LOT of IT aspects to be taken care of if you need it to be widely adopted in a company. This is the lack of nuance that gets me worked up with a lot of commentary on the topic . To make it sound fancy, a lot of people say things like “AI is not an IT initiative, its a business initiative”. Newsflash – they are plain wrong ! . It is both and it needs different things to fall in place for business and IT. A lot of care and thought needs to go into making sure that you have a process (and tech components and people) in place to manage the whole life cycle of AI in a company – and it is non-trivial. It is absolutely possible to to do a bunch of pilot projects without such a platform approach – but it will get to a tipping point very fast where the lack of a platform approach will hurt.
5. No Good Data, No Good AI
This is true for all things in IT – but I have seen that AI will expose your lack of discipline in data much more than most other initiatives. You can create very smart solutions in a POC mode with AI techniques and they may solve very complex problems. Then when you decide to deploy at scale, you realize that your data is not in a form that can allow for an enterprise wide deployment . I have lost count of how many times I have come across this even for departmental level roll outs. It was no different in late 90s with ERP – a good part of the coding I have done in my life was to write transformation routines to fit data into something ERP could use. It’s astonishing that even today we have not largely solved the problem with data. Pro tip – include budget to get data into shape when you estimate AI projects !
6. Quality of service
There is a whole array of things like performance, security, ethics, CI/CD etc that get ignored in POCs that get declared as a success. And then when we try to take it into production – it hits a sequence of walls and AI gets labelled as “only good for POC”. While it might be too early to say “best practices” – there are sensible tried and true approaches to be used with AI that you should consider from the start and include in the scope with time and budget. If it does to get into production, what is the whole point anyway ?
7. API, custom build, commercial vs open source platforms etc
Like it or not – almost every company will end up with a mix of all these while going through their AI projects. Open source based custom builds will probably be the largest component eventually – but there are so many good reusable commercial products around now that it makes very little sense to custom build everything from scratch. Companies that have no ground rules established on how to approach this typically end up wasting a lot of time and money.
8. Developers have a big role, along with data scientists
Thanks to a lot of dashboard based demos – a lot of companies start on AI thinking that all they need is a team of data scientists and may be some visualization experts. The reality is that high value usually comes from integrating AI functionality into applications that run the business. That needs API work, data wrangling , DevOps integration etc which need engineers . I know several projects that under estimated this in a big way which led to stoppage of work till they could find more budget.
9. Ecosystem of talent
AI is the poster child of the war on talent. It is hard to source, recruit and retain talent in this field. A big part of the planning for large scale AI work needs to consider the risk of finding and keeping the right talent. This will need a combination of working with academic institutions, consulting companies, free lancers, recruiting and training in-house talent and so on. Its a LOT of work and generally under estimated by orders of magnitude. You generally don’t need an army of people like what we needed when ERP became hot. But you need high quality – and everyone else needs them too. And since AI generally needs more care and feeding throughout its life cycle – thats an added layer of thoughtfulness that needs to happen !
10. Education
I cannot over emphasize the need for training and education – and a lot of cross pollination across the organizations. There are a LOT of different ways of solving problems with AI techniques. If the team is not aware of it, and can’t debate and try out – you probably won’t maximize the effectiveness of what AI can do for you. Similarly it needs a bit of education to understand how to interpret what AI tells you. I often do a level set on basic ideas of curve fitting, probability etc for my clients to make sure they are not misinterpreting me.
11. Change Management
I have lived through the ups and downs on how change management is perceived by my clients from my ERP days. It used to be the first thing to be cut off the budget and almost in every single case there was a big price to pay. AI – in many cases – is significantly disruptive for how business and IT operates daily. And when the humans involved in it are not proactively prepared for change – the resistance/fear/anger etc usually leads to suboptimal use of AI and sometimes total failure. In any case the top-down approach from ERP days is not useful in AI context – simply because the whole process is a series of experiments and not very deterministic like an SAP implementation.
12. Communication
In almost every other context I would have clubbed it under change management. But comms needs a special mention when it comes to AI. Within the team, and across the org – there is always uncertainty about AI and the best way to get things done, and what benefits are expected and so on. It’s just the nature of the beast. If you don’t spend the time and effort ( and money ) to communicate clearly – it can single handedly make it impossible to have a successful AI initiative.
My intro to philanthropy came via a short lesson on economics from my late grand father when I was in high school. He was a retired professor of history – and interestingly had similar depth of knowledge in economics and political science. And for the record – he is the biggest hero in my life !
One day, when I was back home from school and finished several cookies from a new packet that was on the table , he asked me “why didn’t you eat the four that are remaining?”. I told him I was full and don’t need any more . He asked me if I am too full to finish my glass of chocolate milk , and I told him that I was indeed planning to finish the glass of milk . That was a planned teaching moment and I learned the idea of diminishing marginal utility.
That discussion eventually led to a discussion on whether money/wealth also followed diminishing marginal utility . Logically, if it did – then all the rich people should be able to help all the poor people and world will be such a happy place, right ?
My instinct though was that it didn’t apply to money – who would really say no to more money ? . We were solid middle class – Dad was an engineer , Grand Dad lived on his small pension , and my mom and grand mom were home makers at the time . We were living comfortably but very far from even dreaming of money having diminishing marginal utility.
This is where I got my first lesson on philanthropy – money is not the only thing you can distribute that has value to someone . Your time and your wisdom are perhaps even more valuable ! Later in life, I heard first hand from several very successful people how my grandfather helped them get over their struggles with his time, his advice and his willingness to get them connected to influential people from his network. And my dad – his only son – takes after him.
Grandfather was a learned man and had a terrific network. What if someone doesn’t have money , a lot of education or a big network ? My mom proved that it doesn’t matter either . From her I learned – If you have a mindset to help, then no obstacle is big enough to stop you!
My mom finished high school and married my dad – never attended college. She doesn’t speak much English. But to help raise me and my sister, she ran several small businesses over time and made it work . And throughout the time – she helped the less fortunate people in her life get a leg up .
I have lost count of how many orphans she helped find jobs , get married , get loans etc . She could absolutely convince total strangers to help someone she knew and they didn’t know – and some of them were friends of my dad from other countries who would come home for dinner when they were in India. She never felt any embarrassment asking people to help others and in general no one she asked felt awkward and most of them did what they could . How I wish I had that kind of confidence and conviction ! She never bothered about credit – most of the time the people who got the leg up didn’t even know my mom was behind it .
So when is the right time to start giving ? For the longest time I have believed one should start at the earliest – like from the first pay check itself if possible , and proportionately increase it as we progress in life . And a dear friend of mine gave me an alternate perspective this past weekend – there is no one-size fits all method to do the right thing!
He and I come from similar modest backgrounds . He started his own business and I chose the safer route of getting employed by someone. While I don’t deny I did reasonably well – he totally outdid himself and is very wealthy now and I am very proud of him . I knew that giving back was very important to him from childhood . So I was shocked when he told me that he has stopped making big donations.
It’s not because he stopped believing in it – he just realized that he can compound his wealth better than many others and thus solve harder problems in the world later in life with a lot more money he can put to use. I asked him how he came to this conclusion – and he said “From an interview of Warren Buffet that I watched on TV few years ago, which made me open a spreadsheet and come to the same conclusion”.
To be clear – He is nowhere as rich as Buffet . But he is good at math and is ruthlessly logical in decision making . He still makes small donations – which is still considerable in size in my opinion – but he spends a lot of time studying the problems and choosing where he can make the most impact and how . I have a strong feeling it won’t take that long for him to start solving big ticket problems.
I asked him if that’s how I should think about it too – and he said something like “Man , there are so many problems all around us that we can all help in some way . So you can do it whatever way you choose and the world will still benefit “.
And Vijay Says... 