#203: The False Promise of the 'Quick Win'

Beware the lure of the quick ROI in your innovation project. Also, the future of Pillows, and Jobs.

The false promise of the quick win.

Image by Andrew W Grabham from Pixabay

Here's pattern we see often - a client has a plan for a new innovative product  - there's excitement, workshops are conducted, a big vision is crafted, markets are sized and opportunities are calibrated. The clients are enthused, we are all gung ho about the project. More workshops are conducted. Then somewhere around the third workshop, the discussion turns to cost. It turns out that what the team wants will cost £600K, but there's no budget for this and the project doesn't belong to the 3-year roadmap of the firm. What can be done? Often, the conversation then becomes one of 'what quick win can we deliver for £75K?'. The actual figures may vary, but the orders of magnitude are surprisingly similar.

If you've been in this position, you'll know that this is natural, and there's nothing wrong with wanting to start small - as most innovation experts will advise you. But there's a dangerous and hidden peril in the thinking around the quick win - it is, in fact, quite antithetical to innovation thinking. To illustrate this, let's remind ourselves of the recent SpaceX launch. The rocket exploded seconds after launch. To the world, this might have represented a colossal failure for a multi billion pound project. But SpaceX and Elon Musk, who described the event as a "rapid unscheduled disassembly before stage separation", have insisted that this was a success. The rocket launched successfully, even though it then exploded. And in the broader context of the ultimate aim - space exploration and sending humans to Mars, this is needs to be seen as a 'successful failure'. In fact many experts have argued that this approach in the long term will get us to Mars faster than the traditional NASA approach of avoiding such ‘successful failures’. x§

This is the key difference that is hard to grasp for those of us used to working under the traditional ROI expectations in large corporations. The key measure in companies tends to be financial return at every stage. The key measure in successful product innovation is often maximising knowledge and learning in the early stages of the project. This is the point made eloquently by Peter Thiel in his book Zero To One, and a point understood intrinsically by a lot of good innovators and entrepreneurs. To use another analogy, imagine that 2 people are dropped off at a remote location in unfriendly terrain and given the task of getting back to civilisation. The first person makes a beeline for what she can see as a path and goes as far and as fast as possible on that path. The second person spends the first few hours exploring the terrain in all directions, identifying the highest point and climbing up to create a visual map of the terrain. She realises that the obvious path actually leads to nowhere but a watering hole in the forest, but that there are 3 other potential ways that appear to be better. It's a clumsy analogy but you get the idea - the quick win may offer a false economy and may end up being not much more than that quick win.

This is borne out by what I've seen in many a company - a quick win project results in a somewhat usable solution that does the job - at which point no further funding is available and years later, people are still using that make shift tool or solution, locked into the code, or the limited knowledge base because only 2 people know how to operate the platform or change the code. This is the worst case scenario of course, because it also significantly impedes future work.

These two approaches look similar - the quick win and the fast fail. But the quick win is aimed at showing financial ROI at the very early stage of investment, and it takes a linear view of evolution and scale. The fast fail or the learning driven approach focuses on knowledge ROI and if the learnings are ploughed back, can deliver much more accelerated growth over time, also leaving scope for pivots into new areas based on the learning. Of course, this assumes that further funding is available in both scenarios. For quick win projects, the problem is that often the promise of financial ROI is hard to deliver at very small scale, and usually, the decisions you make to make the project financially viable at small scale are the very ones that will prevent the same project from scaling well. But the quick win remains one of the most alluring mirages in innovation.

Other Writing

Pillow Talk: A (Possible) Glimpse of the Future

I wrote this piece in 2014, as a tongue in cheek look at the future 20 years ahead. It came following a fun brainstorming exercise we did. It popped up again and I thought I’d share it with you.

It’s the year 2034. 

Google and Apple are now the leading brands globally, in healthcare, apparel, furniture, banking, construction, transportation and waste management, among others. 

This week marked the launch of the next generation of the AFKAP (the Artefact formerly known as the pillow) from both companies. As with most things, Apple and Google followed very different paths… (read on here)

The Future of Work

Also this week, I wrote a piece for NASSCOM (The National Association of Software & Service Companies) in India. In this one I was exploring the impact of accelerating technology change on jobs and careers, and what happens when every company is a tech company and significant shifts happen many times within a single career. (Read it here)

Further Reading

Artificial Intelligence: Once upon a time Alan Turing suggested the test of how well a machine could hold a conversation and whether it could pass itself off as human. The idea behind the Turing Test was predicated on the principle that AI needed to prove it could be as 'good' as a human, at conversation. We might be at a point where a future Turing-like test will explore whether the answer was too good to be a human answer. In this piece, Walter Isaacson explores the past and future of human computer interaction. (WSJ)

AI and Enterprise Architecture: This is a very interesting take on how AI will impact IT strategy The author McCreary posits that understanding the new architectural patterns emerging from AI, and redrawing your IT environment to ensure you have descriptive machine readable APIs for LLMs to retrieve and present information against queries are just some of the things you'll need to do. This is as big a shift as graphical user interfaces, or cloud computing. (Medium)

AI Impact Analysis: Tyler Cohen argues that AI experts aren't necessarily the right people to think about the impact of AI on the economy, or how to construct the checks and balances of rolling out AI effectively and safely. Who can construct a true cost-benefit analysis of AI? Who can construct the right policy to help with the transitional impact of AI driven change on people and the labour market? Clearly a broader set of skills is required, not just the maths of AI. (Bloomberg)

AI & Loneliness: In this blog, Galloway suggests that AI will exacerbate loneliness which is a killer. I agree with a lot of what he says, especially about rejection being a critical learning experience during childhood. I don't know whether I agree that AI necessarily makes us more lonely - that's a matter of design and not a foregone conclusion. But Galloway makes some great points and his writing is clear!

AI Deployment: Meanwhile, Grammarly (emails), Expedia (Travel plan), Snapchat (Social buddy) are all using AI and language driven bots in their businesses.

AI & Valuation: AI drives stock performance - what's common to .com, ICOs, and AI - yes, the very mention of them in your business plan and products seems to drive up your valuation. Is this the peak of inflated expectations? Of course Google search volumes is a good litmus test for investment and valuation activity as well, as much as their appearance in earnings calls.

Anti-ESG: For any progressive idea, there is an opposite and harmful idea. This piece talks about the emerging anti-ESG sentiment which works across politics and business in America, led by states like Texas and some influential players who don't want sustainability, diversity, or social responsibility. They've turned shareholder primacy into a political agenda and are so far making good headway, having forced some of the largest investment firms such as Blackrock to change their stance on ESG based investing. (Fast Company)

Bad Ideas: continuing in the same vein, this take by Simon Schama talks about how the ‘Thwarting of Science Will Always be a Human Failing’. Health and medicine is a rich area for a history of rejection of science, from Semmelweiss to Haffkine. We reject new ideas just like our antibodies reject new organisms. (FT)

Retail/ eCommerce: We've known about 'Click and Mortar' models in retail since the start of this century. The relative position of online and physical stores in the portsfolio varies according to the price behaviours of real estate and online advertising and other underlying relative economics. But the model is clearly here to stay as demonstrated by the number of pure-play eCommerce brands that are creating physical stores. Warby Parker is likely to have over 240 stores by the end of this year. (WSJ)

Venture Funding: Softbank’s Vision Fund has announced losses of $32 billion. That’s $32 billion, in case you missed it. Makes you wonder, was Softbank very good once? Or did they just come into a market entering its upturn with a lot of money? As Scott Galloway says, ‘Bull markets and good relationships only require bravado and presence.’ (Crunchbase)

Voice Forensics: can a person by identified by their 'voice print'? Apparently yes, a lot of detailed analysis can be done based on a voice sample. Here's an interesting story about identifying the perpetrator of a prank distress call made to the coast guard. As always the technology can do good, and also harm. (IEEE Spectrum)

Thanks for reading and see you next week!