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A well-communicated message can make the difference between inspiring a workforce and making it sigh whenever Monday morning comes around. Take one small example, how do you name a document? Everyone has their own way.
Some people use personal conventions, some randomly pick a name, while others might follow a standardised procedure they’ve brought in from another organisation. It’s a small thing but what a document is called can make all the difference.
Who hasn’t tried seven different ways of searching for that one important document needed for the Board meeting that starts in five minutes? But here’s where it gets interesting. What if there was technology smart enough to name documents for us based on criteria we define, and therefore automatically standardising the process across your whole organisation? AI has the potential to structure the unstructured without the work becoming reductively homogenised.
I have spent much of my working career building software products, marketing them or taking them to market. I worked in the USA helping to build an automotive electronic document management (EDM) solution as a SaaS product before the term SaaS was common term. It was designed to manage certain types of documents (APQP & ISO 9000 to be precise, Source Quality-One) that the automotive supply chain shared it was at this stage I got my first introduction to lean and agile thinking.
For example, when a tier 4 supplier, such as a steel producer would update a document, this would then update a tier 3 producer, like an engine maker, and so on up the chain. The documents were independent but connected through a backend database, and this idea and the advantages this interconnection has always stayed with me.
As someone who has worked in many different industries (CRM, EDM, Automotive, Banking, Travel, API’s, Video Transcoding), it’s this cross-industry perspective that gives me a solid purview that often allows me to see opportunities where others might not. It’s from this background that my 10x productivity hypothesis was born.
Tomorrow’s Technology Today
Many people don’t realise just how much AI technology is already in our daily lives. Just take one of the commonest uses of AI today, rough planning. For the smartphone generation who have always had maps built into their phones, they might be unaware just how differently we used to do things. Getting from A to B in a car without the use of a disembodied voice telling us to turn around, used to be a semi-painful/humorous activity that could (in my experience anyway) result in arguments, accusations and general grumpiness.
But then came along companies like TomTom who utilised AI to plan your journey for you; goodbye maps. You still had autonomy over your journey, but also a tool that responded to and updated your plan accordingly. And then as smartphone technology improved, the need for a separate tool decreased; goodbye TomTom, (TomTom still trades today in fairness). This streamlined the process further, but the initial premise of AI doing the work still remained.
People Supported with AI
I recently got to see a Knowledge worker-based AI tool in action. It was an expert system (a form of AI popular in the 1980s that has become more powerful due to recent AI advances) called AIC from a consulting company called Rialto Consultancy. This AIC carried out sentiment analysis and degree of alignment analysis between leadership teams, which then went on to create a set of deliverables that helped to overcome the usual 70% failure rate seen in change programs in larger organisations. (Source: McKinsey). This system was built as a result of research undertaken by Harvard professors, Thomas Schelling and Chris Argyris one of which has received a Nobel prize for his work.
In effect, the expert system drives increased performance and the likelihood of a successful change program, though better orchestration of the individuals involved, (Source: Bain & Co.) lining up the detractors and getting them on side. Because change relies on human beings, therefore the human condition can drive success and/or failure in equal measures.
However, a tool like AIC shows how having all the data and insight overseen by AI, can increase the chance of success significantly. By establishing a protocol that ensures every contributor is receiving the same information, this information, therefore, becomes accessible to everyone, leaving any egos in the room free to concentrate on other things. One thing is for sure, in the future more decisions will be made by algorithms and humans working together which will aid leaderships teams and Boards to make better strategies and/or successful growth approaches.
But if after the recent A-Level fiasco in the UK, has you wary about the use of algorithms, don’t be, the problems occur when algorithms are left without human oversight. Properly managed, AI working with knowledge workers can free up workers to concentrate on problem-solving work and thus increase productivity.
The Future of AI
Anyone who has spent any time looking at the open AI API GPT-3 will have seen first-hand how powerful this technology has become. GPT-3 however, has been built mostly using open-source data sets that have plagued the AI space by introducing bias and a number of high profile and embarrassing blunders. Nevertheless, as demonstrated above, AI can successfully be used for a number of knowledge working tasks, with the breadth of these tasks increasing with every new innovation. And if applied the to the challenge of 10x productivity I believe we could see a significant step-change in productivity.
However, in order for people to interact effectively with the technology, we will need AI that is explainable (XAI) so decision-making processes are accessible and transparent. We need to look into the “black box” (the algorithm) and understand how and why a classification was made. The range or algorithms and applications for AI is staggering. If you’re interested in keeping up to date with what going on with AI, I would encourage you to check out AI Forum which is a useful resource for the latest research and news.
From More of the Same… To a New Breed of Solution
We are starting to see a new breed of productivity apps but in some cases, they are taking us way from the prize of increased productivity rather than helping to deliver it. Instant message-based tools like Slack and MS Teams hold great promise but that they take us out of our state of flow and do nothing to help structure the real work. They facilitate the communication, yes; they help support the connections and the messages I discussed in the last essay, yes; but they are yet another distraction from the real work.
So, to help with all these interruptions, a huge range of easy to use project management tools have now grown up in recent years. These move away from the MS Project style (mostly waterfall-based) bloatware, to tools with superior user experiences like Jira, Monday and Asana.
However, in many cases, they are little more than shared task lists with the ability to attach the documents (the work). Essentially, the project management tools are gathering all the information together in folders (tasks) and files (subtasks), but the method of transmission of the work, the document, is still the limiting factor as it remains unconnected and therefore open to duplication, wasted time, etc.
I am interested to see how documents and modern machine learning algorithms might forge a whole new type of productivity application. We are starting to see hints of the future with Microsoft 365 PowerPoint with its ability to auto design slides. If you’ve not seen it, I recommend you should because it demonstrates a small glimpse of the future. Another example is Google mail with its auto-complete capability that can finish sentences for you.
So, if your appetite has been whetted, here are some productivity tools that are starting down this road all addressing small discreet elements of the 10x productivity challenge. You may have heard of companies like, Grammarly an AI based spell checker that can help you create different types of writing.
But actually, the companies closest to delivering a 10x productivity are probably companies you have not heard of, like Notion who pitch themselves as an all in one workspace for notes, tasks and wikis, or Coda who pitch themselves as a new document for teams, and who recently secured $80M from famous Google venture capitalist, Kleiner Perkins.
And AirTable who claim you can organise anything with a spreadsheet-like database, who also recently secured $185M Series D round of VC investment. whilst other companies like Monkey Learn and Agolo offer tools for language classification and text summarisation, all of which hold promise for the development of self-organising documents. Even Microsoft with Microsoft Fluid are starting to recognise the traditional paradigm is broken.
The Future of Productivity
Every organisation wants to be successful and maximise productivity. And every person in every organisation wants to work on things that matter, that make a difference, and that one day might just help to get them promoted. Yet so much work inside organisations are non-value-adding, and some are even value-destroying.
The law of diminishing returns means as an organisation gets bigger it becomes harder for everyone to start on the same page, meaning 10X productivity slips further and further away for start-ups who are focused on delivering the founder vision this transaction can be particularly difficult as teams grow and grow.
So how is 10x productivity actually achievable? It’s finding the correct balance between the four kinds of knowledge work, Reactionary, Planning, Procedural, Problem Solving, the Why, Who, How and What that demands our focus, and the application of AI. That’s where tangible and accessible 10X productivity lies. At least that’s my hypotheses.
Without clarity on every level, everyone’s ability to understand how they contribute to the big picture is challenged along with their ability to truly be productive. And with working from home, these challenges have never been more significant, keeping a team motivate and focused and well informed is not easy.
So, unfortunately, at the moment you can’t buy an off the shelf solution to deliver true 10x productivity, but I believe in the not too distant future we will see this kind of innovation more and more. I hope my observations can help you address productivity in your own work environments and maybe by using some of the tools and ideas I have shared in the previously essays you can begin to organise your own 10x productivity workspace. I’d love to hear your views and how you address productivity in your own organisations, so let’s keep the conversation going.
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