With the current hype surrounding all things Artificial Intelligence (AI) it is a good time to consider not only what we are augmenting or automating with AI, but how and why we are doing it. Before adding AI, step back and ask yourself this question: Are our processes and systems too complex?

If the answer is yes and you are in a mood to reimagine business for the better, then let's give this more thought, before implementing AI. This blog post focuses on Fierce Reduction: a quest to simplify everything business and tech, before you innovate, augment or automate IT.

Countering Complexity

Fierce Reduction is an attitude of mind. Firstly, you should pause and take a critical, rigorous look at everything related to everyday business processes and tasks to see what you can eliminate, before you contemplate simplifying IT systems ort adding AI innovation.

Less is More

Pablo Picasso’s The Bull is a series of eleven lithographs created in 1945¹. It depicts the bull at various stages of abstraction, starting with a realistic depiction and ending with nothing but a few lines. This review demonstrates art as seeing basic patterns amongst the 'noise'; seeing basic forms amongst the complex. In business, Fierce Reduction means the same: seeing basic forms among the complex.

In 2021, American scientist Leidy Klotz wrote a book called Subtract². He called this topic 'The Untapped Science of Less'. In this work, the author has made an extensive study of using less to change the system: scaling subtraction. To quote:

"Essence is the soul of complexity, its irreducible building blocks. All the complexity brought by biological evolution, for example, builds from the genetic code within DNA, which is represented by just four letters, combined into patterns of three letters each. Genetic code is essence."

Furthermore, Klotz goes on to talk about working memory: the cognitive system that temporarily holds the information we have available for processing - meaning, the trade off between the level of detail required to complete a task and our ability to avoid what others may call 'cognitive overload'.

In the context of AI-powered digital innovation, a key question becomes: how much detail could be offloaded to an AI Agent as a Copilot to enable a task to be completed by a human in a faster and maybe, more accurate, and an even less stressful way?

Now, if we look at the state of software today, we see the Software-as-a-Service (SaaS) model delivering a continuous stream of more features, with the aim of maximising our commitment to high user adoption and renewals to monthly or annual subscriptions.

We also have an information technology industry that, twenty years ago, made the shift to cloud computing and pay-as-you-go SaaS. This was countering the complexity and cost of the-then client/server on-premise hardware and clunky perpetual software licences.

Where are we now, twenty years later with SaaS? The answer is deep in a quagmire of complex systems. This is where the quest to add features many times each year by the SaaS publishers is driven in the mistaken belief that this is the best way to ensure year-on-year customer retention.

As with Picasso's eleven stages of abstraction, it was the thinking observed by Ken Segall³ of another genius - Steve Jobs, cofounder of Apple. This is the pursuit of simplifying every idea, every innovation down to its essence. To quote from Segall's first book:

"In all cases, it's a reminder of what sets Apple apart from other technology companies and what makes Apple stand out in a complicated world: a deep, almost religious belief in the power of simplicity."

Simplifiers

In their book Simplify, Richard Koch and Greg Lockwood⁴ cite powerful examples of what they called 'simplifiers': entrepreneurs like Henry Ford, who embraced a number of key principles that apply equally in today's business world:

• Redesign from first principles.
• Reduce product/service line variety.
• Reduce the number of components.
• Eliminate frills.
• Automate tasks.

What these authors went on to talk about was the 'Complexity Trap': the tendency to think that adding new features to a product or service is the only way to retain customers. This is especially true for the information technology industry, where leading Software-as-a-Service (SaaS) publishers typically commit to several product feature updgrades every few months.

When we look at Salesforce as a startup challenger in Customer Relationship Management (CRM) systems in 1999, it delivered on the power of simplicity versus established on-premise software publishers, such as Siebel, Inc.

Today, the 'enterprise' SaaS industry is like its mainframe computing predecessors, indulging in a never-ending, upward spiral of complexity and costs for customers. However, there are now early signs of disrupters entering the SaaS marketspace, talking of a new AI-powered technology that will change the way software is architected and priced. But, the Complexity Trap remains open.

I have two questions for AI technology disrupters: 1. How will your AI product simplify the customer's business operations?; and, 2. How will your product roadmap maintain a focus on simplicity and avoid the Complexity Trap?

Deep Work

If we want to subtract from everyday business processes and tasks, we also need to deal with what has been described as Deep Work⁵ by Cal Newport in his book of the same title. The author here defines Deep Work as:

"Professional activities performed in a state of distraction-free concentration that push your cognitive capabilities to their limit. These efforts create new value, improve your skill, and are hard to replicate."

Similarly, and inspired by an earlier work by thought leader Nicholas G. Carr⁶, Newport, goes on to define the opposite of Deep Work as Shallow Work:

"Noncognitively-demanding, logistical style tasks, often performed while distracted. These efforts tend to not create much new value in the world and are easy to replicate."

Of course, these books referenced above were mostly written before AI tech emerged. Today, AI has mostly an Augmentation role, as Copilot in the pursuit of Deep Work. Over time, AI will evolve into more of an Automation role, as Coworker to replace human labour in performing Shallow Work.

This also relates to something called Flow. I am not talking about the Salesforce technology of the same name, but a serious work from psychologist Mihaly Csikszentmihalyi⁷ which talks about a 'Flow State' that means productive, satisfying Deep Work.

In summary, a Flow State generates Deep Work. This relates to designing an AI-powered app as an Augmentation of human work, where Flow can be achieved if:

1. It enables timely task completion.
2. It maintains the user's attention.
3. It provides continuous feedback.

From a User Experience (UX) Design perspective, Flow State can be thought of as a need to create a Meaningful Journey as an intuitive path through a process. This is where Design Thinking and experiementation is key to understanding:

1. What is intuitive without AI Augmentation.
2. What is intuitive only when supported by AI Augmentation.
3. What is so intuitive that AI Automation can completely replace human effort.

This also means ensuring Progressive Disclosure, where desktop, tablet and smartphone user interface layouts avoid cognitive overload by keeping actions to the simplest design per screen.

The last category replacing human effort with AI Automation could be thought of as something that has to pass a 'Turing Test⁹. In this context, the author talks about a revision of Alan Turing's Imitation Game. Today, this could simply mean that the test is passed if, say, a consumer talking to AI thinks they are talking to a human.

In the current startup phase that is all things AI it is a good time to consider not only what we are automating, but how and why we are doing it. Value Engineering focuses on dividing AI-powered Digital Innovation into two categories:

Summary

The time has come to subtract, not add to current state business processes and their corresponding information technology systems. Keep it simple. Less is more. Before embracing AI in your Future State organisation, it is time to apply Fierce Reduction to your Current State environment.

Start with the broadest set of stakeholders and engage in Design Thinking. As this begins to reveal areas for simplifying business processes and tasks, it also explores corresponding Current State IT systems as Future State areas for removing technical debt and improving user experience.

With Design Thinking, you can introduce Value Engineering, creating compelling ROI Models for setting out the prioritisation of transformation in your Future State scenarios. This is also where you work out where AI best fits a particular use case: Augmentation (AI Copilot to support human tasks); or, Automation (AI Coworker to replace human labour).

References

1. Scott, D. (2019). The Bull by Pablo Picasso – A Lesson in Abstraction. Draw Paint Academy. https://drawpaintacademy.com/the-bull/
2. Klotz, L. (2021). Subtract. The untapped Science of Less. Flatiron Books. https://leidyklotz.com/media/
3. Segall, K. (2010). Insanely Simple. The Obsession That Drives Apple's Success. Penguin Group (USA), Inc. https://kensegall.com/books/
4. Koch, R. and Lockwood, G. Simplify. (2016). Piatkus.
5. Newport, C. (2016). Deep Work. Grand Central Publishing. https://calnewport.com/deep-work-rules-for-focused-success-in-a-distracted-world/
6. Carr, N.G.(2010). The Shallows. Atlantic Books.
   https://www.nicholascarr.com
7. Csikszentmihalyi, M. (1990). Flow. The Psychology of Optimal Experience. HarperCollins. https://www.researchgate.net/publication/224927532
8. Mishkoff, H. (1988). Understanding Artificial Intelligence. Macmillan. https://www.si.edu/object/siris_sil_1017019
9. Turing, A. (1949). Imitation Game. Stanford Encyclopedia of Philosophy.
   https://plato.stanford.edu/entries/turing-test/