Tag: AI

Getting Sh*t done in a distracted world 

Could it be that human attention is the most scarce resource of our time?
We are surrounded by distraction, inside and outside of work. But for today’s knowledge workers that can be particularly damaging to productivity.

In this short blog I review some of the most interesting thoughts I’ve read recently on how to beat distraction, increase focus and maximise productivity. Highlights include

Dave Allen’s Getting Things Done (GTD) method

Cal Newport’s advocacy of Deep Work

Tiago Forte’s concept of Small Batch Productivity

Knowledge Work & Productivity 

Knowledge work comprises a range of roles in the modern economy but broadly can be thought to consist of things such as: generating insights from data, applying experience and expertise, stakeholder communication and managing, teaching others.
In the eras of manual work, innovations such as tools, organised labour, the production line and batch production all dramatically increased productivity. We’re only at the start of trying to figure out what the equivalents are for knowledge work, although there’s already some great thinking out there on this point. What’s clear is that the difference between low and high productivity in knowledge work can be many times the difference in manual work.
A distraction when we’re doing a manual job (digging a hole say) is bad enough, but one that interrupts a diffuse flow of partly formed thoughts, logic, ideas and a flow of not yet formed connections –  possibly never to be recovered – can be really damaging to productivity as a knowledge worker. Carl Richards neatly illustrates how the time spent on distractions can add up, in this blog and how we can find a lot of time if we have the discipline to reallocate and focus on what really matters. Gloria Mark has done work to attempt to quantify the impact of switching – finding that information professionals work in 12 different spheres per day, switching between them every 10 minutes on average and suffering interruptions every 2 minutes. Work resumed after an interruption on average 23 minutes later. Here’s a cool infographic from inc. that captures some of the stats… but don’t let that distract you!

Find out how to find focus

“People think focus means saying yes to the thing you’ve got to focus on. But that’s not what it means at all. It means saying no to the hundred other good ideas that there are. You have to pick carefully. I’m actually as proud of the things we haven’t done as the things I have done. Innovation is saying no to 1,000 things.” ( Steve Jobs. Apple Worldwide Developers’ Conference, 1997)

Not all distractions are bad of course, the 21st century office is set up to facilitate interactions and collaboration, and of course one person’s collaboration is another’s distraction depending on viewpoint and timing. However most of us are also vulnerable to distraction from apps and notifications (from email, social media, messengers etc) all promising us that small hit of dopamine we get when we seek and find an uncertain reward. We become physically addicted to the trigger / uncertain reward dynamic that these apps employ (this is brilliantly articulated by Nir Ayal in his book Hooked). That little red circle with a white number inside it generates a powerful hold over us! I’m not arguing that social media is a bad thing in itself, clearly it can be powerful in connecting people and satisfying our innate desire for social connections, it can even be powerful in a work context. However what I am trying to say is that when it comes to getting things done, viewed through the strict lens of productivity it is something that must be carefully managed to avoid damaging our productivity.

How to fight back

The good news is we can use some of these behaviour insights to our advantage to design systems around us to utilise the same reward loops in a more positive way, as well as to plug holes in some of the shortcoming in our natural mental systems such as storage and timely recollection, and to create timely nudges. Something as simple as a to do list is a good start – the brain responds to earned markers of success with dopamine, even if they are as simple as placing a tick in a box (check out this blog from trello).
Even retaining some of the bad distractions as rewards to allow us the will power to create a period of focus followed by unfocus/reward can be productivity enhancing. After all, we aren’t robots and trying to work like them isn’t the solution.
The need for a system to buttress our own internal cognitive processes and functions is one of the main insights of Dave Allen’s GTD (getting things done) methodology. A system to log and categorise tasks, projects, ideas. A system that is reviewed regularly & maintained as current, and brings focus onto the right tasks and projects to accomplish both the urgent and the important through time. It helps by recognising that the brain is an excellent problem solving device but a lousy storage device – we need things around us to help with the storage and recall point!
I’d challenge any knowledge worker to put in place a few of the GTD ideas and not report a noticeable improvement in productivity.
In fact, I’d go as far as to say that if you are a knowledge worker in the 21st century and you are not employing a system around you to help overcome distraction, focus and beat mental shortcomings, it’s like trying to farm the land with your bare hands. It might work for a while, but ultimately it unproductive and you’ll get left behind.
The nature of these systems vary, of course, something as simple as a physical notebook could be one example, personally I am a huge fan of trello. Other examples include evernote, google drive, OneNote etc.

Going one step further

Cal Newport highlights what he calls Deep Work “work performed in a state of distraction free concentration that pushes our cognitive abilities to the limit”
And the benefit of deep work? Well, in an era of AI and automation Cal argues persuasively that any task that can be accomplished with a shallow or brief level of focus can and will be automated. We need to go to deep work to stay relevant and generate real value. Deep work is what will be valued.

A different perspective

On the other hand, Tiago Forte argues against deep work and in favor of what he calls “Small Batch Productivity” or intermediate packet delivery (getting something – however small – shipped out for comment/feedback at the end of each short block of work). The idea behind this is to make sure you aren’t asking yourself to return mid-stream to a task after a break – as this is bad for productivity. On the flip side imposing little deadlines and getting things out early for feedback and be very beneficial.

Overall it’s about finding what works best for you, of course. But don’t assume things are just fine as they are because you don’t see any chronic issues – I reckon most knowledge workers could increase their productivity by at least 50% by implementing a few of the thoughts discussed above, but most don’t even know they are doing anything wrong!

We’re just really at the start of discovering how to knowledge-work most effectively, the issue is many of our structures and institutions are designed around principles from the 19th or 20th century that were optimal for manual-intensive work, but are far from optimal today. We should stop trying to adapt ourselves to outdated processes & structures and find what works best for what we need to achieve today.


One minute guide to real-world AI implementation 

McKinsey just published an excellent and comprehensive paper covering how Artificial Intelligence (AI) can deliver real value for business.


The only issue – at 80 pages it’s a lot to read.

A lot of the use cases focus on retail, energy and education, one angle I find particularly are the read-across of these examples into service based and business-to-business environments. There are definitely some relevant points that could map to a services/B2B worlds: for example the automation of admin tasks for teaches, more targeted sales and marketing and more personalised customer service.

Here’s my take on the key points from the document:

1. No shortcuts: first data & digital, then AI

AI becomes impactful when it has access to large amounts of high-quality data and is integrated into automated work processes. AI is not a shortcut to these digital foundations. Rather, it is a powerful extension of them.

The firs thing firm’s need to do is come up with a real business case for AI that relates to the firm’s strategy, this requires separating the hype and buzz around AI from its actual capabilities in a specific, real-world context. It includes a realistic view of AI’s capabilities and an honest accounting of its limitations, which requires at least a high-level grasp of how AI works and how it differs from conventional technological approaches.
Each new generation of tech builds on the previous one – this suggests AI can deliver significant competitive advantages, but only for firms that are fully committed to it. Take any ingredient away—a strong digital starting point, serious adoption of AI, or a proactive strategic posture—and profit margins are much less impressive. This is consistent with McKinsey findings in the broader digital space.
Technology is a tool and in itself does not deliver competitiveness improvements.

2. Areas to focus on to create real value: project, produce, promote or provide 

To fulfil the expectations being heaped upon it, AI will need to deliver economic applications that significantly reduce costs, increase revenue, and enhance asset utilization.

Mckinsey categorized the ways in which AI can create value in four areas:(1) enabling companies to better project and forecast to anticipate demand, optimize R&D, and improve sourcing; (2) increasing companies’ ability to produce goods and services at lower cost and higher quality; (3) helping promote offerings at the right price, with the right message, and to the right target customers; and (4) allowing them to provide rich, personal, and convenient
user experiences

3. Data ecosystem & staff culture to the fore 

Firms must conduct sensible analysis of what the most valuable AI use cases are. They should also build out the supporting digital assets and capabilities. Indeed, the core elements of a successful AI transformation are the same as those for data and analytics generally. This includes building the data ecosystem, adopting the right techniques and tools, integrating technology into workplace processes, and adopting an open, collaborative culture while reskilling the workforce

4. Take a portfolio approach focused on use cases in short, medium and long term, be lean, fail fast & learn

A portfolio-based approach to AI adoption cases, looking at use cases over a one- to five-year horizon, can be helpful.

In the immediate future, McKinsey suggest a focus on use cases where there are proven technology solutions today that can be adopted at scale, such as robotic process automation and some applications of machine learning. Further out, identify use cases where a technology is emerging but not yet proven at scale. Over the longer term, McKinsey’s view is to pick one or two high-impact but unproven use cases and partner with academia or other third parties to innovate, gaining a potential first-mover advantage in the future. Across all horizons, a “test and learn” approach can help validate the business case, conducting time-limited experiments to see what really works and then scaling up successes. Fast, agile approaches are important.

5. Don’t be a hammer in search of a nail … 

To ensure a focus on the most valuable use cases, AI initiatives should be assessed and co-led by both business and technical leaders. Given the significant advancements in AI technologies in recent years, there is a tendency to compartmentalize accountability for AI with functional leaders in IT, digital, or innovation. This can result in a “hammer in search of a nail” outcome, or technologies being rolled out without compelling use cases. The orientation should be the opposite: business led and value focused. This business-led approach follows successful adoption approaches in other digital waves such as mobile, social, and analytics.

McKinsey graphics on AI: