We are in the Age of With, where everyone realises that AI touches our daily lives.

An AI-Moment is an interaction between a person and an automation, and these moments are now commonly boosting productivity or reducing our unwanted activities. Yet are we truly prepared to seize these opportunities as individuals, organisations, or society?

AI-Moments may be insignificant to us, for instance when a presentation slide is re-designed, or your car prompts a better commute route. These AI-Moments may be more significant when they determine every student’s academic grade [1] or rapidly evaluate a new vaccine [2]. AI-Moments are touching us all and they are the building blocks for imminent exponential growth in human and business performance.

Exponential growth needs AI-Moments that are ubiquitous, accelerated and connected.

Ubiquitous adoption of AI-Moments has already happened. It may be subtle, but everyone is already working with AI-Moments. Take this article that you are reading. An AI-Moment probably moved this up your notice list, created a list of people to share this with, helped your search tool find this article or prompted an individual to send this to you. As I am writing this piece, AI-Moments are suggesting better phrases, ways to increase effective impact, or improvements to my style [3].

Beyond the immediate pool of technology, AI-Moments are affecting how factories function through productivity tracking [4], changing call centres by replacing people with automated responses [5], or transforming our retail industry and high streets through online shopping. Take a moment to look at your daily routine or immediate environment to realise just how AI-Moments are already ubiquitous.

As you look around, consider how their adoption is accelerating in terms of quality and scale. This is because it is easier to create and adopt AI Moments. Applications are readily available that children can use to build AI-Moments that identify plants, recognise hand gestures, or detect emotions [6]. Monitoring satellite images for changes [7], recognising galaxies, or equipment analytics are all just as simple to build and adopt. Our most critical systems might require more robust solutions for the moment, but the acceleration of AI-Moment adoption is clear. AI-Moments that were not possible five years ago are now commonplace. They are, quite literally, child’s play [8].

Elsewhere, the first better than human translation with two languages occurred in 2018 after 20 years of research [9]. Applying that research to a further 9 languages only took 12 months [10]. This pace of change is universal. Google DeepMind solved a 50-year-old protein folding grand challenge in biology in November 2020, after four years of development and then mere weeks of training their AlphaFold solution. They are already now using that same model on diseases and viruses, predicting previously unknown COVID-19 protein structures [11].

AI-Moments are changing how we act, and their creation is changing how quickly we can re-act.

This creates a significant survival challenge, especially to organisations. An organisation that recognises, adopts, and accelerates AI-Moments across its functions has a distinct advantage over one struggling to do the same. Survival needs AI-Moments to break out of innovation or technology spaces, as rival organisations who deploy AI everywhere can act, re-act and improve faster while their competitors are still experimenting. Winners adopt and scale solutions better and faster using AI-Moments [12].

This will create the platform for exponential growth. First, we recognise AI-Moments are touching everything at greater pace and that they combine to multiply our performance. Then, as their pace expands, we realise that only AI-Moments can effectively manage this growth. People will find it too complex, or time consuming to understand, combine and exploit multiple AI-Moments. We will need AI to manage our AI with more AI-Moments.

AI-Moments are the common platform for exponential growth

Take the child’s app to recognise animals. The child shows the application a collection of cat photographs and the machine recognises cats. Show it a dog photo and it knows that it is not a cat, so we need another process to train dog recognition. The only way to improve recognition is through more cat or dog images, and even the internet has a limited quantity of cat photographs [13].

Instead, create an AI-Moment to recognise cats, then another AI-Moment to create synthetic cat photographs in new positions or environments. This is already a standard approach to train AI [14]. Using AI-Moments in this way exponentially accelerates learning as the only limit is the computing power available and not the quantity of cat photographs.

We can apply this approach to our current activities and processes, yet that creates a dilemma that will confront every single person and organisation: we will need more AI-Moments to manage, exploit and grow our performance. This will create exponential growth and, in turn, require more AI-Moments.

Our current concerns are around automating processes, replacing roles, or accelerating functions. They are “A to C” solutions, with success measured by how well an AI-Moment completes step “B”. Creating more complex flows is already normal, whether using another application to create them or copying someone else’s pattern to replace a familiar activity. These new complex flows effectively extend our solution from “A to n” with multiple steps in-between.

Automated AI-Moments will drive exponential growth, and will occur when existing automations are everywhere, accelerating performance and connections.

We are now on the cusp of significant transformation, where multiple AI-Moments interact, regularly in ways that we did not predict, expect or, sometimes, even request.

As an example, consider the routine of a typical salesperson. There are already solutions to automate office routines for meeting requests, room bookings and email responses. The first step is collating those automations into one “Get to Inbox Zero” AI-Moment, that involves a quick review of proposed responses and then responds: email replies based on your previous responses, all rooms booked, all requests sent ,automated prompts for more complex responses (that are expressed in simple language for the user to approve, “Yes, agree to request, use the agenda from the meeting last Tuesday”).

Then add in automated lunch reservations, travel tickets booked, hotels reserved, agendas created, minutes captured, presentations built, contracts drafted, and legal reviews completed. Include automated suggestions for new clients based on your current sales, existing targets, customer base, and market insights, with people identified to bring you together through an automated request that is already drafted in just the right way to get a positive response.

All these routines exist today in separate AI-Moments. Very soon these AI-Moments will connect and automate together.

There is often talk about the Singularity – the moment when machines will surpass human intelligence, and the idea that a single AI machine will achieve this superiority. The combination of AI-Moments does not need a super-intelligent AI, or General AI able to process any problem. It just requires a connected collection of ubiquitous AI-Moments, each replacing a small step of a larger routine. Each applies the rules of marginal gains and they come together to create exponential growth in potential. It may not be the singularity that futurologists predict, but its effect will be similar, as AI-Moments replace human activity in a way that surpasses human insight or comprehension.

This is the Age of With, and AI-Moments are the common units of change.

[1] A-levels and GCSEs: How did the exam algorithm work? - BBC News

[2] UK plans to use AI to process adverse reactions to Covid vaccines | Financial Times (ft.com)

[3] Introducing Microsoft Editor – Bring out your best writer wherever you write - Microsoft Tech Community

[4] This startup is using AI to give workers a “productivity score” | MIT Technology Review

[5] AWS announces AWS Contact Center Intelligence solutions | AWS News Blog (amazon.com)

[6] https://lobe.ai/

[7] As wildfire season approaches, AI could pinpoint risky regions using satellite imagery | TechCrunch

[8] Machine Learning for Kids

[9] Translating news from Chinese to English using AI, Microsoft researchers reach human parity milestone

[10] AI wave rolls through Microsoft’s language translation technologies

[11] https://www.deepmind.com/blog/article/alphafold-a-solution-to-a-50-year-old-grand-challenge-in-biology

[12] Building the AI-Powered Organization (hbr.org)

[13] https://en.wikipedia.org/wiki/Cats_and_the_Internet

[14] Adversarial training produces synthetic data for machine learning (amazon.science)