Why AI is more than cheap Prediction…its equally about self-organising dynamic workflows


I recently read the article The Simple Economics of Machine Intelligenceand the book Prediction Machines. It got me to think – the technology geek in me was of-course shocked at the over-simplification. And while I accept that the current state of AI is nowhere close to the myth of super intelligent machines, I still think that the reality is somewhere between the two extremes.

Five years ago, it was all about data and insights. Data mining algorithms were designed to determine patterns, correlations and convert data into insights and advanced machine learning algorithm predicted value (through ranking, relevance etc.) and recommended actions (using classification, context and value). Quality of Prediction was highly dependent on the richness and relevance of data and the buzz was about transforming data into informationand ultimately improving the decision making process. Better insights improved the context, and as the accuracy of predictions started to improve, it lead to more holistic perspectives– all this started slowly to make the difference, and the conversations gradually shifted to predictions.

If you sift through the AI hype, most of the products today sit in this space.  No wonder the economists are reframing AI as cheap prediction. They argue – the economic shift will centre around a drop in the cost of prediction (and an associated increase in the value of judgement) – which will in turn lower the cost of activities that were historically prediction oriented (like inventory managements, demand forecasting), as well as open up opportunities to use prediction as an input for things for which we never previously did (like navigation which now has moved out of the rule based programming framework to a machine learning based prediction framework that predicts human response to changing environmental data).

But limiting the view to predictions (and keeping actions triggered on judgement) out of the purview seems like an incomplete and a shortsighted view. It is like saying that we will replace bits and piecesof our existing processes by newer technology. It’s an oversimplification of AI and risks losing the bigger picture – the opportunity to reimagine business processes – not just with digital intelligence – but also through self-learning and self-organising workflows.

Of-course it is true that now prediction is becoming a commodity. It means that morecompanies can make betterdecisions, and fastertoo. But decisions do not necessarily translate into actions.

Some argue that automation of workflows is helping to achieve this. I hold a slightly different view.  Workflow automation has been practiced for decades now – and even I agree with reasonably good results.  But these workflows are not easy to setup. The design of a workflow is a slow, definitive process and requires careful planning and understanding of all possible scenarios and outcomes. This imposes a structure and boundary to the workflow and limits the flexibility. Changes to the workflow are slow to introduce and require both re-design and re-implementation followed by re-training. This often makes the workflows out-of-date in todays fast changing dynamics.

In the age of cheap prediction, new scenarios and outcomes are constantly discovered. Even if these predictions are fed into automated workflows the actions and outcomes are confined to a pre-determined set – the set that the designers anticipated as the range of scenarios and implementers programmed as rule based decision algorithms or multitude of if-then-else type statements. Conventional approach to workflow implementation and automation is simply too costly, low on productivity and offers limited flexibility to adapt to changing needs.

This is where I believe AI will have the biggest impact – the potential to take the predictions (or even simply data or insights) and change the workflow dynamically, the possibility to use the prediction to not just determine existing action paths but to learn new action paths and set them up automatically (by-passing the slow painful process of large IT projects with their 6-12 month cycles of consultancy, design, implementation, integration, staging and deployment).

AI has the potential to introduce this fantasy – the ability to dynamically create, manage and execute workflows – a paradigm shift into a constantly evolving and dynamic workplace. That is the vision that I am working towards and our early success makes me an optimist – our use of machine learning algorithms today allows us to correlate events with dynamic storyboards (thus setting up the full context), and with a simple application of prediction we are mapping intents to a multitude of actions. This is the first step away from the realm of data and insights into the world of real time actions and our machine learning driven framework is slowly evolving into a learning framework of self-organising and dynamic workflows and action paths.

AI will have a profound impact on the workplace – be it revenue or cost or customer experience – it will be more far reaching than the cheap prediction scenario envisions. The opportunity stands before us to turn business models upside down, to create new categories and overturn existing market structures. I can’t say if it will change everything, but it sure can change a lot of things. People and organisations that accept this and participate (beyond the hype paradigm), i.e. for whom AI is not simply a badge of innovation, will reign as future leaders.

And if you still do not believe me, take a moment to look at Amazon– AI is central to their workflow even today – some of this is highly visible (and an edge for the future), such as the autonomous Prime Air delivery drones; the Amazon Go convenience store that uses machine vision to eliminate checkout lines, but then there’s so much more behind the scenes – some to drive more traditional  predictions for process optimisations like demand forecasting, product search ranking, recommendations (product, deals), detecting anomalies and exception handling (e.g. frauds, asset management) and others that are innovating business models and personalising customer service… and many more that we are still to hear of!

This article was first published on @LinkedIn October 23, 2018.


When the workflow becomes a competitive advantage…

Is it a missed opportunity?

I find it fascinating that very few business leaders talks of workflows or processes in their business updates. It is rarely a subject of discussion in strategic planning and board meetings. The leaders are engaged (and often obsessed) with visions and milestones and the mantle of setting up the day-to-day working system (to realise those goals) is left for others to work out.

Even in today’s world where digitisation and transformation are buzz words, most leaders are following the hype curve and adopting the processes, tools, technologies that they see around them. Induction of new technology – digital, mobile, automation or AI – is seen as a checklist, something that has to be done to present as a badge of innovation. In most cases it seems to be viewed with the lens of cost reduction and the first instinct is to explore automation of existing workflows and processes; very few organisations invest in building it up as a competitive advantage or an opportunity to innovate customer experience.

And yet, every now and then we come across companies that surprise us with the power of process innovation. I was very excited when I read of an anecdote related by Hans Roslingin his book Factfulness– Hans tells us that he was invited by UNICEF to investigate a bid for a contract of malaria tablets to Angola – an unbelievably low bid by a small family business (Rivopharm) based on Lugano in the Swiss Alps. The bid caught everyone’s attention, as the price was lower than the cost of the raw materials.Hans visited the factory to inspect and came back highly impressed. They had achieved this impossible task of delivering a cheap solution through two unique strategies – first they had used technology to automate their process(which allowed them to manufacture and prepare the shipment in just 3 days); and second they had converted their payment terms into a business advantage– where UNICEF payment was received within four days, the raw material supplier gave them a credit of 30 days – and they simply routed the interest earned on 26 days to pass the benefit back and cut down the prices! This is a perfect case study to demonstrate the power of a process – any process – be it manufacturing, development, logistics or business.

If you are still not convinced and somewhat skeptical that this may work on a small scale but is not a practical approach for a large or complex business, then there is no better example today than Amazon– even without having the benefit of internal insights – I can say with conviction that they owe their success to process and technology. They have oriented themselves to let technology drive their processes(and not the other way around) – and that has made all the difference. Whether it is the use of technology, robots or AI – its central to their process. Some of this is highly visible (and edge for the future), such as the autonomous Prime Air delivery drones; the Amazon Go convenience store that uses machine vision to eliminate checkout lines, but others are like the hidden layers (of a neural network) that run their magic of optimisation and deliver predictions with the least cost solution – be it the machine learning algorithms that drive demand forecasting, product search ranking, recommendations (product, deals), fraud detection etc. or other automations that enable Amazon to improve core operations.

It has been seen – again and again – that the workflow (or process) is a leading indicator of success. And yet, it is always relegated to the side – why? Is it because it falls way behind in hype and glamour? It surely is a missed opportunity, as it seems to hold the edge for the future– especially with the growing applications of AI, predictions and automation.

The workflows in the future will need to be dynamic and adaptive to constantly changing inputs and predictions. But we have to be careful not to force fit new technologies into existing processes – that will only return incremental improvement. Instead, we should view technology and process innovation as a tool to create a step change.

Technology driven Innovative processes will turn out to be the biggest competitive advantage in the new era of AI driven automation. But a word of caution – even as we design and deploy these new processes, we should never forget that process is not a proxy for results… we should always stay vigilant – constantly reviewing the outcomes and adapting the processes…

This article was first published @LinkedIn on August 13, 2018.

Being curious – about everything!


An inspiration like no other… discern patterns where you never expect…

Having read many biographies and memoirs of successful business leaders, I started to feel that even though each journey was unique and distinct, their experiences, learning and even their approaches were often very similar… but the story of Leonardo da Vinci is an experience on a different plane… and its an experience worth going through.

Of-course I may be biased – with my own interest in seemingly disjoint disciplines (e.g. technology, neuroscience, psychology, design etc.), and my firm belief that bringing together ideas from different spheres not just adds a different perspective to thinking but also allows for innovation in unforeseen areas, I see here a perfect example… an example beyond my wildest imagination.

I have always known that Leonardo da Vinci was not just a painter but his interests and explorations spanned many areas beyond painting and sculpture and included interesting explorations in science and engineering (having earlier read about his flying machine and his (failed) attempts of flying like a bird, his many drawings of machines, etc.)… but I wasn’t much aware of his vast experiments in anatomy, geometry, hydraulics, geology, botany, fossils and fossil traces, birds, weaponry, waterways, bridges, architecture or theatre (there seems hardly a subject he did not study)… nor did I ever appreciate the extent of his study of nature and science…

But what intrigued me the most is that it’s not superhuman powers or divine endowments that make him so special, but his unquenchable curiosity and his obsessive passion, often coupled with inventive imaginationsimple skills that all of us have access to – if only we make an effort

What sets him apart is his ability to discern patterns not just from nature and apply them to science, but also to take his findings from experiments in science and applying them to arts… there are so many examples – from using the observations from water and fluid dynamics and applying them to figure out how the heart valve works or the blood flows in our bodies… from understanding the optics of light and reflections to functioning of the human eye to bring realism in his paintings…. from observing the structure of the wings of birds to design machines for flying… from using perspectives in landscapes to bring flat images to life… and many many-others… each of these correlations were arrived at only after hours and hours of obsessive study and often wild experimentation – an inspiration to all of us to pursue our interests with obsessive passion… his approach reflects his thinking of not accepting received wisdom but supplementing it by his own studies and carefully designed experiences – a lesson that we can all learn from.

I will admit that I have occasionally prided myself on being a successful proponent of driving innovation by bringing together ideas from different disciplines, but I now realise that my many attempts have not even scratched the surface – I cannot even fathom going to the extent that Leonardo da Vinci did – using anatomical studies and hours of hands on dissection to observe the muscles that move the lips and applying that learning to paint the memorable Mona Lisa smile! Not just unbelievable but amazing… I wish I could develop such a level of acute observation, obsessive study and experimentation, indefatigable curiosity and an unnatural degree of imagination

And if I can take one thing away from reading about his life, his pursuits, his passions and his approach, then I would like to slow down, and start looking at everything around me with new eyes, and maybe even stop awhile to cherish the infinite wonders around me – for if I do that I will also start seeing many more patterns and correlations that I overlook today…

I would highly recommend reading his biography, for it is not just an insight into Leonardo da Vinci, but his life is an inspiration like no otherit opens the mind to possibilities that most of us have long forgotten in our single-minded quest for ‘getting it done’

This note is a page from my diary – UnLearning, which records all those random thoughts (ideas and fears…) that make me live day-by-day.

This article was first published @LinkedIn on March 6, 2018.

Past Trends rarely lead to a new idea…

From Observing to Wondering… Design Thinking opens up a new way of looking at things!

from observing to wondering

I trained as an engineer. My experience (working with some cool designers – a few of them from frog design) quickly taught me to unlearn a few fundamental tenets of engineering practices, and instead embrace some contrasting methods from the radically different approach of design thinking. The first shift in my approach occurred in the mid-2000’s when I learnt the power of moving from a vertical (first) thinking to horizontal (first) thinking. The second shift started a few years thereafter, and it has taken me beyond the realm of conventional reasoning.

Over the years (in part due to my scientific training), I have learnt to combine deductive (top-down) reasoning i.e. applying theories and premises to specific instances, with inductive (bottom-up) reasoning, using observations to build hypothesis and theories. The focus has always been conformance to theories or hypothesis, and the goal is to discern patterns, connect dots and build correlations.

But, I have grown to believe that at times we have to go against the rational extrapolations of data and rely on anecdotal observations and instincts. Many-a-times I find that both deductive and inductive reasoning fail miserably.

In today’s era of data explosion, where we are constantly looking for past trends and data patterns, I have started to question the very goal of looking for conformance and patterns. I have started – instead – to search for those samples of data that break the pattern and wonder why? I know it disrupts conventional wisdom – but I find that it gives me space to think beyond accepted norms, anticipate new circumstances and look for new possibilities. It is quite possible that my interactions with designers has reinforced my own rebellious and contrarian attitude… and given me the confidence to break tradition and opt for design approach of ‘what if…?’ So what if I am currently starting with an incomplete set of observations… I can come up with not just one but several possible explanations. Of-course these are limited by the available information and are often based on conjecture and designed by my imaginative faculties. But it may be the starting point for a very different hypothesis, which can (of-course) be tested over time.

If I had any initial doubts, they soon eroded when I found support from a credible source – an American philosopher Charles Sanders Peirce (sometimes known as the father of pragmatism) has argued that no new idea could be proved deductively or inductively using past data and in his writings he introduces abductive reasoning, characterised as guessing and an inference to an explanatory hypothesis. He compares the different modes of inference and explains that deduction proves that something must be; induction shows that something actually is operative (it never proposes a new idea for its conclusion); abduction merely suggests that something may be (and seeks a new hypothesis to account for facts).

We have seen ourselves that we can hardly ever explain a new idea using past trends and data patterns… a new idea usually takes form when an observation does not fit into an existing model and we try to make sense of the ‘surprising’ observation by coming up with one or more explanations and slowly arriving at the best explanation.

We tend to forget that data is about past, emerging trends are about the future? And that’s where the problem with conventional thinking lies. Design thinking has extended my outlook with the what-if approach and trained me to move beyond simply observing to start imagining, wondering…

I love thinking about ideas freely… and observing them take shape!

This article is a page from my diary: UnLearning, more specifically an interesting section on ‘Embracing elements of Design Thinking’.

This article was first published at @LinkedIn on September 04, 2016.


What if sales transformation could become just another every-day project and not be a big managed initiative…?

Is this just a dream…? What if a CRM system automatically prompts me at the right time and guides me with a simple (not more than 2-3 lines) template, telling me what to do… I will always on top of the client journey! I will always know what to do, how to do and when to do…

on top of the client journey

Sales Transformation is more than deploying of new sales methodologies… its really about changing sales behaviour… and driving sustainable change… far bigger a challenge than most realise!

In recent client interactions, I have found many clients investing in expensive sales methodology trainings, setting up new workflows, building checks and balances… driving their teams to comply to newly deployed systems or processes…

There are many different sales methodologies in use – from the more widely known ones like Miller- Heiman’s Strategic Selling, SPIN Selling, Target Account Selling (TAS) etc., to a few that have their own niche following such as MEDDIC, The Challenger Sale (TCS, a successor to SPIN), Value Selling Framework, Solution Selling, SNAP Selling, Sandler, CustomerCentric Selling etc. All of these require the sales teams to adopt a structured approach to the sales cycle… expect them to get acquainted with complex decision matrices, question checklists and develop the capability to select from a vast suite of alternative scenarios…

Lots of investment in time, resources and processes is done by organisations (big and small), and yet the returns fail to match the expectations or the potential – and it always takes longer than projected! This is not from lack of commitment or motivation from the management, nor from selection of incomplete or ineffective methodologies – for each methodology has years of study, research and results that have led to its formulation and evolution. While, some methodologies focus on communication and messaging, others give more attention to important aspects of the sales process like discovery, qualification, decision criterion, decision process, and a few also cover account mapping, identifying champions and developing effective relationship maps.

The biggest stumbling block in effectively embedding these methodologies (or for that matter any new process or workflow) is the standard deployment approach – a few days of targeted intense workshop training (with tons of reference documentation to read and refer), followed by managed introduction of the new process, and setting up internal programs to drive adoption and compliance of the workflow through monitoring, tracking and regulating actions.

The reality is that however good the methodology and the supporting frameworks or worksheets, it is one thing to know what is required, a completely different thing to overcome inertia and an even a bigger ask to develop the capability to select from a vast range of options to put that into action. It is very natural to unintentionally fall back into the comfortable and tested mode of operation… and even the most effective of all monitoring and tracking is limited in effectiveness to simply managing and rewarding activity and very rarely get further down into quality or tangible results.

My personal experience has shown that the key to success is to try to embed the new methodologies into every day action. Do not simply rely on the quarterly, monthly or weekly reviews of plans, strategy or approach – big scary excel sheets, fancy blue sheets or other frameworks – as these are offline snapshot activities (and after all reviews!)… Instead enable every update, every action to automatically answer the questions and capture the information relevant to the stage of the client journey… Of-course this is easier said than done… but is achievable by supporting human intelligence with elements of collaborative intelligence and machine intelligence… support the user by giving him prompts – at the right time of the client journey – that guide him to the relevant questions… (Believe me – all sales users will love the fact that there is no need to remember the questions or the criterion or the complex matrices, or refer to offline reference tutorial to understand the definitions of confusing terminology or fill up documentation and worksheets!).

Remember that these prompts and questions cannot be big word documents or excel worksheets – that will turn off all of us… given our attention span 2-3 lines describing the question will work best… what if don’t even have to write down the answers and can instead capture the response as a short video? Well! We have our new template or worksheet – a short structured video! Where every question is just a segment in the video, and as the question is prompted we answer the question on video and move on to the next question… so a series of 3-4 questions gets recorded as a short structured video… just imagine the simplicity over all the documentation!

Is this doable? What does this require?

  • Take the client journey and map it to our own definitive stages – stages that we have identified for our custom workflow (say discovery, definition and scoping, qualification, positioning and validation, negotiation, closure etc.)
  • For each stage, building upon the methodology in use, list down the applicable questions and qualifiers (e.g. Is this an opportunity? Can we compete? Can we win? Is it worth winning?)
  • Map the questions into templates. [Each template is a set of 3-4 questions (2-3 lines max) which get encoded as segment descriptors for a structured video]
  • Identify triggers that mark the start or end of different stages and associate the templates with the triggers.
  • Integrate the triggers into the sales workflow (as simple service extensions on the CRM)
  • The occurrence of an event or a trigger will result in an automatic notification to the user, feeding him with the prompt and appropriate template

So, what does it deliver? As a user, I don’t need to refer to offline documentation or associations or supporting apps and toolkits, I simply engage with the defined process of updating client interactions in the CRM system. The process automatically activates on changed triggers or new events and notifies me (the user) on specific action, prompting me with the appropriate template to ensure full coverage of the questions to progress the opportunity or reduce the risk.

The notification acts as a natural check and reminder to engage with the process and the prompts drive the quality of client conversations… The methodology gets embedded as a way of working and requires no offline effort from the user. The underlying goal of building sales capability and moving the organisation towards consultative selling gets gradually into the social fabric of the organisation.

The improved effectiveness of sales and better understanding of client needs opens up new opportunities for accelerating business.

This is not just a concept today but tested in the field through SmartVideoNotesshort, structured and searchable video bites that are embedded into client CRM systems.

SmartVideoNotes (from humanLearning) are being used by global organisations today to drive sales transformation and embed new sales behaviour through use of customized client journey templates and associations. It is also being used for digital transformation to embed technology and innovation into everyday actions.

This article was published @LinkedIn on August 05, 2016.

Leading Innovation – Making Ideas Happen


Success of Innovation is generally associated with Ideas. There is little doubt that ideas are core to innovation,  but we have also seen time-and-again that many good ideas and innovations lose their way – in fact only a small fraction eventually end up living to the original promise.

Innovation is the talk-of-the-industry. It is probably the most overused term in business today – and yet, everyone has there own interpretation of what innovation means and how it can be introduced. The focus is mostly on Idea Generation – some emphasize on systemizing innovation processes while others look to inculcate innovation thru specialized focus groups. Innovation is rarely measurable and disappointingly it seems to have become an end in itself – it is no longer about transforming businesses, changing lives or creating a new world. Innovation seems to be losing its meaning.

In reality, innovation is not just about ideas – an idea is just the beginning of the innovation process and the real work starts after that. This is not to say that defining the idea is not important – it is at the core of the innovation process and the relevance of innovation is tied to its response to real problems and addressing the pain points. However the potential of the idea needs to be deterministic and fully measurable – the path and the journey itself in most cases of true innovation would always be unknown. The difference between success and failure usually lies in how this unknown and unchartered journey is undertaken – how the idea is translated into a goal and (more importantly) the commitment that is made to take it forward.  

And this is where leadership comes in and leaves its mark. For innovation to drive achievement, it cannot be simply built around creativity, but has to be realized through careful planning, painstaking execution, constant vigilance, periodic adjustments and diligent pursuit. All examples of successful innovation have been driven by strong leaders who have been instrumental in changing the impact of various actions down the path of success. There is no better example than Steve Jobs – the ideas in themselves were not his own nor were these new inventions – it was the journey that made all the difference!

There is no doubt that the basic qualities of a good leader – i.e. clarity of goal, adaptability to change, swift & decisive action, calculated risk taking and the courage to override obstacles – are still important. But these form only the minimum requirements to drive innovation, and are not necessarily sufficient to ensure successful impact. Leading innovation requires additional capabilities that help traverse the lifecycle of change.

(1) Passion to make a difference – the inspiration to leave a lasting impact.  

(2) Perseverance & Courage to pursue, to keep going despite all odds & contradictions, to always look for alternatives.

(3) Conviction to challenge conventional wisdom, manage inertia and resistance to change.

(4) Focus to drive towards the goal – ignoring newer attractions and distractions, staying on course, constantly improving usability, simplifying the experience and using technology as a means and not as the goal.

(5) Pursuit of Perfection – willingness to admit mistakes, change course even late in the game and striving always for the best possible.

We just need to look around to see how many innovations have been lost on the way – some have been stopped, others modified beyond recognition, while the majority have simply been replaced by newer initiatives. In most of these instances, it has rarely been the idea at fault, seldom a scarcity of resources – and mostly always a lack of conviction or a faltering commitment.

There is no doubt that success in innovation is far more influenced by leadership than any other element – even more than creative ideas, smart resources or unconventional out-of-the-box thinking!

This article was written on February 09, 2012.

Reverse Innovation Lessons for Developed Economies


Reverse innovation stories in emerging markets highlight the untapped potential of innovating operating and business practices in developed markets.

The more I read about “reverse innovation” and the opportunity this creative method of rethinking products and services has opened up for the developed world, the more I see how important operating and business processes are. What’s interesting is that in the telecom industry the best examples (and the most successful ones) are coming out of emerging economies. This shouldn’t be a surprise as innovation in the emerging world has been an outcome of the prevailing business pressures that left little option but to change conventional thinking.

We have all seen that most of the subscriber growth in the developing world has come from bottom-of-the pyramid consumers that generate marginal ARPUs (average revenue per user). It’s no wonder then that in most instances, innovation in the emerging market has been in the form of new business models that profitably target these consumers with low disposable incomes and stimulate the usage of basic and value-added telecommunication services.

This kind of innovation started by changing very basic paradigms. Companies redefined core values and questioned competitive drivers. We also saw operators such as Bharti moving away from physically owning the entire front-and-back-end infrastrcuture to outsourcing many non-core activities in an attempt to turn capital expenditures into operational costs and manage cash flow better. They started with Network Sharing in order to optimize on-network op-ex and cap-ex costs (often driven by the need to extend coverage into remote areas). They then added Network Outsourcing and later IT Outsourcing to bring down fixed costs and improve performance. These moves have also helped Bharti divert internal resources into customer management and business evolution.

These innovations are not just focussed on operations. There are many examples around advanced business practices as well. Dynamic pricing has been introduced in South Africa by the African telecommunications company MTN (the plan is called MTN Zone). It uses realtime network loading to offer time-limited discounts on voice and sms in specific geographical areas. This has increased usage minutes, stabilized pre-paid ARPU, and helped to smooth traffic profile for higher network efficiency. Concepts like sachet-pricing, which has been introduced in a few markets including Africa, India, and Latin America, enable customers to purchase data access in small increments (1-hour, 1-day, 3-days, etc), thereby providing a flexible alternative to the standard flat or usage-based pricing models. Another high-profile example is the One Network launch from Zain that allows customers to roam across 16 markets using their home SIM card at local call rates.

And there are many more such examples.

What is probably not commonly known is that most emerging market mobile operators have been able to record EBIDTA margins that are, on average, higher than the margins in developed economies—proof that there is value in taking a different approach to backend requirements.

With the developed world now faced with similar challenges on costs and margins, there is a case for learning from the innovation in emerging markets and building on it further to target the untapped potential of innovation in both operating and business practices.

This article first appeared on Aricent Connect on 20 August 2011 (http://blog.aricent.com/blog/reverse-innovation-lessons-developed-economies)