First off – thanks to SAP for having me at the event as a blogger. In my day job, I no longer work on SAP technologies closely. But most of the clients I have are SAP shops and often talk to me about SAP given my heritage. Sapphirenow keynotes and executive meetings with the board members were enlightening for me. Here is my quick commentary.

Hana – done and dusted ?

As I walked into the show floor, the first thing that caught my attention was that there were hardly any signs for Hana. After the last 6 years of Hana overdose, I was pleasantly surprised to see this. I have always maintained that success of Hana will be when SAP does not explicitly talk about it, and it just becomes a part of everything they do. To be clear – Hana is not a part of everything they do quite yet – most of their customers are still not on Hana, but SAP probably feels confident that the tipping point on adoption has happened and momentum will keep building. Congrats to all my friends who worked tirelessly on it.

Indirect Access – SAP heard the community loud and clear !

Bill McDermott needs to be commended for taking on the indirect access issue heads on in his opening keynote. Without that, the whole empathy thing would have sounded hollow. Now the proof of the pudding is about SAP following through and changing the perception in the ecosystem. I heard it loud and clear from Bill in a private meeting that he is committed to making it happen and I trust him.

All hail SAP Leonardo – just don’t ask me what it exactly is 🙂

This Sapphire was all about SAP Leonardo ! Unfortunately, I cannot quite explain what it really is under the hood. From the slides I saw, it seemed like everything but the kitchen sink was thrown into this package – analytics, block chain, cloud…. . To be honest, it seemed like SAP did not have the time to think through a crisp message before the event. I am sure this will be a lot better by TechEd. I should say – while admittedly not being a marketer myself – I doubt SAP will keep this name “Leonardo” for long. It sounds like a cool internal project name , but I doubt this will catch widespread imagination. I heard this from several people on the show floor too, including several of SAP’s largest customers.

SAP loves partners !

The keynotes were totally partner friendly – We had senior execs from Dell, intel, Google, IBM et al show up on stage at various points. But it was rather light on customers. Perhaps this was a conscious decision given the customers would have wanted to talk about their existing (boring?) – mostly on premises –  systems, where as SAP wanted to position their future with cloud and AI and all that. I trust the balance will tilt towards customers as pilot projects for Leonardo get completed. While I have no idea of commercial impact, the Hana startup program was a good thing for Hana adoption. I was surprised at not seeing it being repurposed to get Leonardo a similar lift. Perhaps more at Teched?

What is next for UI and API in the machine to machine world?

SAP did an exceptionally good job with UI with Fiori etc – and Sam Yen and team should be incredibly proud of that. It was also an expensive effort to get SAP to look cool.  Now with Bernd and Hasso painting the vision of automation, ML etc – I would love to hear what is the next evolution of SAP UI. When most of the back office and factory processes are automated – there won’t be human beings keying in things into a beautiful screen for most of the processes. And for the few screens that do remain in future, screens cannot be static either – and will need to learn user behavior and adapt. Just looking at how many screens get used today and making decisions based on it might not be fit for future. I have a similar question on APIs. Future is mostly about machine to machine interactions and AI. The way APIs are designed are not fit for this kind of future – machines for example do not need anything more than a binary protocol to talk to each other. Sapphire is a business setting – so perhaps these things will find a place at next teched.

Time to rethink demos a bit ?

I will just make two points on keynote demos. Putting sensors in drills and showing how it works in real time on a live stage is something I applaud. What I thought was less impressive was the articulation of the business case. If I am renting a drill – the renting company cannot give that drill to anyone else. So – what is the point of charging per hole drilled ? What if I rent a drill, keep it for a week and drill 2 holes ? Its not a logical business model and it detracts the people watching it. I hope SAP just finds a real life customer case to demo next time.

From the beginning of time, SAP has demonstrated dashboards. I love dashboards – and the ones showed this time were visually impactful too – and maybe had great ML behind the scenes. It just did not show that way – I think its time SAP moved to some other way of making the message hit home.

SAP makes a much needed entry into AI

Hana brought the speed and technical simplification SAP needed. Now SAP is taking the next logical step to make applications smarter. SAP leaders I met here were all realistic on the effort and skills needed to make this happen. Ariba choosing Watson as its cognitive engine, and Google Tensorflow being endorsed in a big way by SAP are all signs that SAP clearly understands that AI is an ecosystem play.  In my mind – SAP can’t get the skills and IP needed to keep up with this market without a string of acquisitions. SAP has always had a strong services ecosystem around it for decades – but they historically have been low key on role of services in creating value. The future of applications might not all be pre packaged like how ERP traditionally worked – and the significant need for domain expertise etc is readily acknowledged now. 

The one aspect where I have some pushback for SAP is the inside out view that AI sits inside SAP systems. While there are valid scenarios like invoice and payment matching where all data sits inside SAP – it is not realistic that a company can model real world with just SAP data. Again – I think its just a matter of time for the vision to mature.

It was fantastic being here – and catching up with old friends and making new ones. It was also fascinating meeting several people I have known only on social media – thanks for introducing yourselves ! Till next year !

Every day I read at least one article that talks about Indian IT companies planning to hire several thousand people locally in US , and multiple articles about digital transformation disrupting multiple industries. What is abundantly clear to me is that jobs and careers as we have known them will get disrupted in an unpleasant way and society will have to pay a big price to pay . 

Global IT services business was built on “your mess for less” philosophy, not on advisory and consulting type business . 

If you, the client,need to spend a million dollars to process invoices or make collections  ,then  I – the service provider – have a combination of cheaper labor ,infrastructure  scale and technology that lets me do it for cheaper . So I will do it for cheaper and give you a better deal while still keeping a healthy margin for myself . And then I will keep optimizing my process and technology so that I make better margins – some of which I will pass on to you too . Now that you love the model for invoice processing , I can also make this work for you in say maintaining your ERP system . I will just hire more people , and use the learning from the invoice process work and keep making my business better while you have a healthy cut on your costs too . 

The differentiation became one of price charged by the provider – and hence buyers just needed to compare rate cards for the most part to make a determination . The last couple of decades went along this way – with minor differences . There came a point where low price alone wasn’t differentiating enough – so some kind of price + value add was needed to keep this motion going . That came in the way of pricing for outcomes , rebadging employees , adding some freebies like proof of concept work , etc . 

What didn’t change was that success was still tied largely to headcount – how many people could the customer take off their books , and how many the provider could add . Even if the contracts were outcome based , the costs were mostly head count based . This model could adapt to slow changing technology – but not for massive shifts in technology . For example – when BPM came along a decade ago, a ten member team probably got shifted to an eight member team . But when AI becomes mainstream – with visual recognition , pattern matching and so on – we may only need 2 people to do that job , or perhaps none ! The current model is not built for that kind of disruption – and companies and people will both have a price to pay .
I look at “we will hire thousands of employees in US” as mostly (not solely) a PR exercise . While protectionism will very slightly delay the inevitable – the job losses in US and elsewhere won’t be because India and China have cheaper labor . It will be because automation will take over routine repeatable jobs , and there just won’t be enough people with the skills needed for the higher end jobs given we have not prepared as a society and as an industry for such a tipping point . 

These higher end jobs unfortunately cannot be done in near future in India ( not sure about other countries but can’t be that different ) because the quality of the mass produced engineers are not anywhere close to the levels needed for taking these jobs on scale .  Revamping the education system world wide should be priority one – and this can’t be done via formal education system alone . 

It will be a rather weird world ahead of us till we hit the next equilibrium point . Job losses will not be because there aren’t enough jobs that need to be filled  – it will be because we don’t have enough people with the new skills . 

Automation will light up the industry candle on both ends – clients will automate a lot of their work , and service providers will automate a lot of their work . This affects all levels of current services organization hierarchy . For the most part , I expect the fundamental services business model to change . Just a few examples in no particular order to make the point

1. Services companies became large and profitable by optimizing large resource pools – like a thousand ETL programmers to support a hundred  projects . Large SI companies historically did not need too many people with multiple skills – so it was quite possible to be a top class ETL programmer without ever needing data modeling skills for example . When the need changes to micro specialization by client , and smaller and shorter projects , it no longer makes it possible to optimize this way . So I expect the large service providers to be more of general contractors and the actual skills to be provided by individuals and smaller companies . Some very niche skills alone might get housed in these larger SIs – and the differentiation will mostly be IP based 

2. Neither clients nor providers will need a lot of managers purely for administrative reasons like reporting , aggregating etc . I do expect project management to become even more important though . 

3. When robots take some of the the jobs of humans – HR will need skills to integrate human machine interactions . While it might sound funny today , I do expect people with “robot recruitment expert” in future 🙂 . Finance will need to deal with the newtradeoffs between balance sheet and P&L when robotics become mainstream. Similarly for a service provider differentiating primarily on IP – the CFO will start watching balance sheet a whole lot more than she does today . 

4. For near term at least – countries like India are better off transitioning to products from services . But in the mid to long term , there won’t be much of a difference between what is a product and what is a service . Again , the skill requirements and business model changes are significant and fairly disruptive 

5. The scope of services provided will fundamentally change . As clients automate their plants and further increase the complexity of their supply chain and talent mix – how many services companies can help them on the front line ? The “manufacturing vertical” of most services firms today deal with mostly back office functionality of manufacturing companies – that won’t be very useful in just a couple of years 

6. Consultants , Advisors and buyers’ agents will need a lot more depth than the high level digital transformation power points, and rate card benchmarks to earn their keep . The level of knowledge needed to implement actual robotics in a company is not the same as “here are the 7 steps” on a power point chart you can impress a senior exec with today.

You get the idea – it’s not just the line employees who will get impacted , the entire length and breadth of the services ecosystem will get disrupted . And given the high spending power of this segment in the society and economy at large – the impact will be wide spread . In other words, the time to act has probably passed us already and we better start at least now . 

The first step to change is acknowledging we have a problem – so let’s get that over with and start the creative destruction and the rebuild . The onus is on each of us as individuals to reskill ourselves for the needs of tomorrow . And the onus is on each leader to join hands with each other to collectively define and implement the future of our industry – we cannot solve this one company at a time. This is not the time for empty promises and bravado filled PR  and small ring fenced teams that do “innovation” – we need meaningful action at scale . Let’s go !

So I turned 42 – and woke up to 100s of birthday wishes from family and friends around the world. Its also the first time I have had my parents and my mother in law with us here for my birthday, and that makes it extra special.

As I settled down with my two fur kids to enjoy a cup of coffee, it dawned on me that 42 is my favorite number – thanks to Douglas Adams . When I first read the book, I had my fair share of theories (and fierce arguments with friends and strangers) on why is it the ultimate answer – including the binary representation and all.

Then I found out the truth a few years ago and then I liked 42 even more!

“The answer to this is very simple,” Adams said. “It was a joke. It had to be a number, an ordinary, smallish number, and I chose that one. Binary representations, base 13, Tibetan monks are all complete nonsense. I sat on my desk, stared in to the garden and thought 42 will do. I typed it out. End of story.”

The big lesson that 42 taught me was to not take the world around me – and myself – not too seriously . When we do take ourselves too seriously, the joke is invariably on us 🙂

There are three times every year that I look back and forward – New year, my birthday, and the day I review the business plan for my team at work.

Looking back, its amazing how perspectives change with time – and some times at alarmingly accelerated pace. Some totally random birthday thoughts here.

1. Growing old is not some kind of slow process – and not something that only happens to others. When I joined TCS out of B School, I was 24 and my boss was 30. We used to call him “The old man”. Joke is on me – I am the old man now 🙂
2. Not everything that is forced on you is bad. I hated being sent to learn Carnatic music as a kid. I never became a good singer, but the music theory stayed with me and now I appreciate the music a lot. I still tease my mom for forcing me to learn music and she teases me that all I seem to listen to is the music she forced me to learn. The irony is readily apparent to me as I drive my kiddo to her weekend activities, some of which she thinks we force on her 🙂
3. All I wanted as a young kid was to become a professional dog handler. I was pretty good at the craft – and had my fair share of wins in the ring. I still enjoy dog shows – but I enjoy spoiling my fur kids at home a lot more today than taking them through their paces at a show. From time to time I do feel the urge to take the leash and go into the ring just for fun – and I might still do that one of these days. I am not yet fully over the grief of losing my dear fur kid, Boss, but I am also convinced we will get a pup again in near future.
4. Life has a way of going in circles. After a degree in mechanical engineering and an MBA, I decided my calling was as a programmer. Then along the way, I got nudged into sales, management and all that I hated as a young developer. Turns out not all sellers and managers are horrible people like I initially thought 🙂 . And guess what – its my technical skills that help me the most as I build my current team.
5. While I try hard to keep my tech skills sharp, I have also realized that its way more fulfilling to be the one handing out the prizes than being the one gunning for those prizes. Bar none, the best part of my work life is to help my friends and colleagues succeed and be their number one cheer leader. And on personal front, the (more than) equivalent is being the chief cheerleader for my daughter.
6. I (think I) always knew that life is way too short to spend on unimportant things. What I did not realize very well till recently is that some (previously) small little things have become more important to me over time and some big ticket items from past don’t figure in my top priorities as much any more. I just need a little more conviction to say NO to weekend work phone calls – but I am planning on getting better at this really quick 🙂

Project management is hard as it is . There are three aspects of project management that I think need some extra attention when the project is about AI . Pro tip – if it’s your first time , consider buying a Costco membership for the BIG aspirin containers 🙂

The three aspects ( certainly not the only three) are

1. Estimation 

2. Change management 

3. Testing

It’s the very nature of AI based development that makes it harder than traditional project management . Let’s start by making a few assumptions 

Good engineers who can work well with AI are hard to find – but for this discussion I am going to assume skills are not an issue and somehow the PM formed a team with all the right skills. 

Since AI functionality is exposed as APIs in many cases , I will also make another “simplistic” assumption to make my point about PM that engineers don’t need to be big time mathematicians and statiticians to use the available APIs .  

Just so we are clear – you do need great skills , and you do need some experience with the math to do AI projects well . I am just making these assumptions to keep the focus on PM in this post . For good measure – I am going to use waterfall language here, but there is no difference if the project uses Agile 

Let’s start with estimation . The first step of the process is to define phases like say development and testing , and use some historical data to figure how much effort and time it takes . This is easy for many parts of development like web/mobile/DB etc since there are plenty of past projects to give realistic guidelines . Then we get to the AI part – let’s say just one model is in scope that uses Machine learning .

Now we run into the first issue – whether the model we chose is the right one for the app we are building . In many cases – we won’t know till we actually test it . And even if it works ok , it might not be valuable enough of an insight that it throws back to the app . So whatever we estimate – there is a good chance that while developing we will need to switch drastically to something else and that will invalidate prior estimates . This is on top of all the other things that can go wrong in the actual design of the app . 

Even if the model technically works – performance can become a dog . Not all models support parallel processing . In many cases , another statistical method might need to be considered after you realize performance is going to be a problem . Ergo – potential to rework is lurking throughout .

Then there is the second issue – which is that unlike traditional projects , source data can fundamentally change the fate of an AI project . Data is already a problem in traditional projects  – unclean data can increase ETL effort for example . But it’s kind of easy to estimate ETL effort after you profile source data . But even if data is technically clean – your model may not find it useful . You probably will miss data you need , or need data from additional sources and so on . And there is no good way to know this prior to putting the data through your model . It’s also not uncommon for your base application to change its paramarters significantly because your model needs different information eventually than what you originally designed . 

In other words – your dev and testing team can drive you nuts if you walk in as PM with “traditional” expectations. Now to be fair – it’s not because your engineers are willfully . They just have a harder time debugging when things go sideways – and a lot of things go sideways . The Costco membership helps with less expensive beer over weekends where you reflect your career choices  🙂

These two issues already make it hard to estimate and test an AI project . Now let’s look briefly at the change management aspect . 

Explaining what an ML model does in business terms is a non trivial challenge to begin with . Now to explain why the model doesn’t work as planned and why it needs to be reworked – without guarantees it will work the next time – makes it a much harder problem . Even when things work well – and if a business person asked you to explain why the model arrived at the result it did , its often hard to explain .

Good PMs educate their stake holders – AI projects can make them look like a tenured college professor 🙂 

So how does a PM mitigate these issues ? I will offer a few thoughts 

1. Start by educating yourself on fundamentals of AI . You can even create little chatbots etc with minimal to no coding . Get a feel for the actual work upfront before you take over the project . Make sure all your non technical people like Business Analysts do this too 

2. Set the expectation with the client on the nature of the AI projects before the project starts . You can do this with real examples using simple models that you can mock up in Excel and then help the client extrapolate what will happen if something goes wrong 

3. Insist on highest quality engineers . If you have to develop something from scratch today – developers need experience with multiple frameworks and know their trade offs . AI models add a layer of complexity on top and have their own trade offs . Having past experience won’t eliminate all the issues I called out – but it will help minimize the mistakes , and when things go wrong you will recover faster . 

4. Aim small , Miss small . Try hard to resist the temptation of trying to build a complex system in one go. Make small goals and course correct as you go . 

5. Double down on data quality . Encourage your ETL team and data scientists to keep looking at data , visualize them differently and identify problems as early as possible . 

6. Create a support system for you and your team . It’s a new field and everyone needs help from time to time . If you don’t plant your shade trees up front , you will face nothing but grief later 

I am very curious to hear your thoughts and comments !

The general idea of feedback in the context of management and leadership is like motherhood and apple pie. Feedback is good and without it, it is hard to improve. But that does not mean that we are good at giving and/or taking feedback .

Feedback becomes necessary mostly because, hard as we all try – it is really hard to be perfectly honest with ourselves to take corrective action in a time frame that matters. I am in my forties now – and I am perfectly honest about stuff that happened in my teens and twenties. I work hard on fixing some of those “issues” . I cannot say with similar conviction that I am honest about my strengths and weaknesses when I think about say the last 5 years. For that, I need to hear it from others.

People tend to underestimate or overestimate themselves. It is a lot easier to judge someone else ( and also say “who are you to judge me” )  🙂 On the work front – I have had very few managers who have consistently given me useful feedback . People ask me for professional guidance all the time too – and I am sure some of them think I suck at giving useful feedback as well.

Most feedback is pretty useless – generic questions get asked and generic answers are given. My two favorite examples of thoroughly useless feedback from managers to employees are

1. You need to improve your communication issues ( probably present in 90% of all performance appraisals for people in their first ten years at work)

2. You are doing great – keep doing what you are doing and good things will happen to you. ( Run, don’t walk – this comes from lazy managers who have no idea about your work or what they can do to help you get to the next level )

If you only seek or give feedback in long intervals of time, its generic nature is more or less guaranteed. A continuous process – can I make up a term “micro feedback” ? – is a lot more valuable. But for this to work – a few things need to fall in place

1. For feedback to work – there needs to be mutual trust. And trust takes time. So don’t expect consistent overnight results.
2. Aim small, miss small. Having a road map to your destination and being pragmatic about diversions along the way makes giving and receiving feedback a whole lot more valuable. Specific feedback is a whole lot easier if your goals are specific. I will go on to suggest – perhaps the most useful feedback you can get is to keep validating whether your roadmap itself is useful anymore, compared to just validating individual goals.
3. Person getting feedback needs to know that ultimate responsibility of acting on feedback is with them, and not the giver. Its your life and career – no one will take care of it like you !
4. Person giving the feedback should be comfortable in making the feedback specific or say clearly that they don’t have any good ideas this time. Ideally make introductions to someone else who can help if appropriate
5. Person asking for feedback should resist the urge to defend themselves to the extent they can. By all means ask clarifying questions though to understand the perspective that is offered to you. As I heard in the Netflix series “The crown” where the Queen mother tells Queen Elizabeth “It takes every ounce of energy not to say or do anything” 🙂 . I know – I have bit my tongue a lot when I have heard candid feedback, and almost every single time it has helped me realize how I was fooling myself.
6. Rather than defend, I prefer asking for feedback from multiple people and look at it as holistically as you can. If you ask only one person all the time – you are just inheriting their bias. One way that has worked for me in the past is to ask someone close to what I do for feedback ( lets say my immediate manager) , and another one who I know has experience in what I do ( say a senior executive in another company or another part of my company).
7. If you decide to not follow the advice you got – try to explain it to the person whom you got feedback from. This is best done if you both set up expectations upfront. This is harder than it sounds – but if you don’t do this, there is no sense in asking the same person again. They will tune out at some point. Over time, my own way of asking for feedback is to create a set of options and ask for advice on which one I should choose and why. I am always open to a new option that I might get from this exercise.
8. While persevering at something is laudable – the pragmatic way to look at it is that not everyone is good at everything. If after receiving feedback and acting on it for a while does not seem to make you good at it – you absolutely should question both the source of feedback, as well as your own ability to improve.
9. Always have a realistic plan B. Not all ideas pan out even if you do all the right things (including acting on feedback). If you don’t have a plan B, you are bound to be driven by fear of failure and that is not always a good motivator. Best time to develop a plan B is when plan A is working well.
10. Never be afraid to have a strong point of view. Your ability and necessity to take feedback should not be confused with the need to have your own strong opinions. Just be willing to stand corrected

In my couple of decades in corporate world – I have done stints at both large and small companies . I have also hired a lot of people over the years and watched their careers in those companies . These days when I hire – a lot of applicants tell me “it’s a really large company and that worries me” . So here is an attempt to provide some color commentary on this large company thing to help you think through .


Why do you want to work for a large company at all ? 

The truth is that while the company is large – YOU are probably going to be working in a team that is not that large . The better questions to ask is about the team you will be a part of . If you don’t like the team’s mission or the people in it – walk away and don’t look back .

Large companies mostly do things at larger scale . What they occasionally lack in speed , they make up in scale . Scale comes in many flavors and not everyone can deal with scale very well . For example – in last 5 years , I had to run portfolios that were an order of magnitude larger than previous ones . I had to unlearn and relearn a lot to make it work and it was not easy .  I have seen this work both ways – some people feel stifled at smaller companies because they want to change the world and they can’t find an opportunity to do so where they are . Some others at large companies beat their heads against a wall because they can’t move their ideas at the speed they want despite having access to vast resources . Choose wisely ! 

I often get asked “wouldn’t larger companies be really political?” . My answer “absolutely – but not any worse than smaller companies”. Politics is everywhere and you need to learn to live with it and navigate it . Also what is politics for you will be routine for someone else . Don’t sweat too much on that front . My own experience with small companies as an employee – which obviously is not a valid sample – is that favoritism and other political shenanigans are alive and well there , and more magnified because of smaller number of people . 

Another common question is “wouldn’t I be lost in this big ocean?” . And my answer is “yes – unless you show real results”. Large organizations are unwieldy to manage and hence get matrix management structures  . It is very easy to get lost in the system and it’s no fun to work that way . BUT – if you are good at what you do , and can show real results , the system favors you by design and you will get noticed quite quickly . If you are average – you will be the tree that fell in the forest that no one ever heard . So if you are not sure of your abilities to consistently deliver above average results , and if it’s important for you to get recognized – you should rethink the idea of joining a large company . 

Here is another one “I have heard the only way to succeed is to have a godfather in the higher ranks”. Well – having a god father certainly doesn’t hurt . But your real question is how do you get one ? Bosses like team members who make them successful . When you see a top executive giving special attention to someone – don’t just assume sinister things are at play or that the employee is sucking up . While those things all happen from time to time – the majority of cases , that employee had gone above and beyond in making their boss successful and is just getting noticed for good work . Also – sucking up to the boss is rarely a sustainable strategy . A VP of sales cannot “hide” a poor performing director of sales for very long. Unfortunately in very large teams where metrics are not clear – you may run into these bad scenarios . I have witnessed it a few times in large engineering and marketing teams . 

Large companies are often blamed as slow and bureaucratic . There is absolutely merit in that allegation . However , it has a good side too . Large companies are predictable in the sense that they rely on policies and procedures a lot . The policies themselves might be terrible and outdated – but you know what they are upfront . Also – if you run into problems like say a bad performance appraisal , or a commission dispute, you can be rest assured that there is a well defined process to rectify that and at least in my personal knowledge – it mostly favors employees . There are exceptions and those usually get the most publicity . 

One last point before this flight lands – the reason the large companies want to hire you usually is because they think there is something special about you that they value . They are not really looking for one more of what they already have usually . So find that out while you assess your future employer – if you think you have to morph into something you are not , this might not work out well for you or the company despite all the money and titles . I learned this lesson the hard way and hopefully you don’t have to 🙂

Since I grew up in ERP space , workflows are near and dear to my heart . I have set up a lot of workflows myself and I have been subject to the tyranny of bad workflows a lot too . Over time “collaboration” became a thing but classic workflows still largely rule our work life . The first time I directly set up a workflow was for a purchase order scenario in the late 90s – and I remember the client VP took me and a colleague to a fancy dinner to thank us . It solved the biggest pain for him in routine business and for two young consultants – that was like winning backstage tickets to a rock concert 🙂

So why do people use workflows ? The “useful” reason is that some decisions are usually complex and can’t be taken by one person – because of skills , legal and other reasons . There are many “useless” realms too . What is not talked about often in polite company is that lack of trust in fellow human beings is a big reason for the zillion workflows we all live with . 

I have several friends who specialize in workflow and collaboration systems – and they take great care of their clients in setting up the most efficient and effective workflows . What doesn’t always happen is that life changes often, but workflows don’t mostly change with it . And this can lead to comic and tragic and tragi-comic situations !

For example – Lets say there is an executive who runs a business, which has annual revenue that has a lot of zeros on the right side . But if she damages her phone and needs a new phone , she will need approval from her manager to get one . 

If a company trusts her to handle millions of dollars worth of business , shouldn’t she have an automatic approval for a phone ? Sure she does – and one call to the CIO can probably get this workflow fixed right away for her and everyone like her in the company . But it’s not just her – what if this is a non executive employee who has a critical job function like door to door delivery where the ability to reach a customer by phone  is paramount ? Sure he needs it too – and another call to IT (but this time from an upline manager in escalation mode ) can fix that problem quickly as well . But how many variations can happen in a workflow before it reaches the “this crap cannot be sustained” mode ? It takes very little time and I have lived through that nightmare a few times when I was a young consultant . And however carefully we craft the design of workflows – we won’t be able to predetermine all options that become necessary across enterprise as market evolves and business adapt to keep up .

It’s probably never going to get fixed completely – but machine learning can help solve a lot of these painful problems . Even if an automatic fix is probably hard, given legal and financial policies don’t move at the speed of innovation in STEM, we can make a tangible impact with meaningful insights .

The data about existing workflows is easy to get . That is enough information to get patterns for an algorithm to start on . Then it’s a matter of introducing other data sets and see what we can learn – like say weather , sales data , budgets etc . In our example of the executive – an algorithm that learns that there is a huge business impact if she loses her phone , it can trigger an order automatically . This is a much more sustainable way than a deterministic “if exec , then auto approve” rule . Why ? Say the same exec moves to a non P&L job and has a desktop where she has access all day while a new phone gets ordered . 

Humans cannot keep track of all the workflows that are set up over time  . No one needs an extra notification or email if they can help it . So machine learning can also be used to keep track of how the workflow landscape evolves over a period of time and suggest meaningful ideas to the workflow admin on options to optimize . 

If an hour a week gets saved for a given employee by eliminating useless workflows and making existing ones smarter , that is more than a week’s vacation that you can give that person at no extra cost in productivity . How cool will that be ?