As a newly appointed CEO one of my first tasks was to deep-dive into the impact of Artificial intelligence (AI) on the business I manage and run. The Disruptive brands that I am custodian of have been at the forefront of technology adoption, with a bleeding-edge/leading-edge mindset throughout the organisation.

For reference, we use the wonderful model from Geoffrey Moores’s classic Crossing the Chasm to benchmark technology adoption. (see below)

Internally, we used a very simple model to classify our business operations, which has now developed into our AI strategy; it was based on 3 words: Human (for human tasks), Hybrid (for human-assisted tasks), And Automated (for AI tasks not needing human intervention).

I find it simple and easy to explain and has helped me clarify my team’s and I’s minds about where we are heading.

Moving forward to current times, I have seen across the technology industry landscape that reductions in the workforce are a trend that will continue as technology companies adopt more AI tools.

SaaS background

The launch of Software as a Service models has always been a game changer for a smaller nimble business. The ability to use (in various circumstances) Enterprise level software becomes a game changing advancement.

The usual pricing schema was per-user with a ramp to more advanced features, the predictable revenue being shored up by annual payment discounts and in nefarious cases by auto-subscription and dark patterns (more on this in another blog).

The various SaaS tools, encompassing everything from marketing through to accounting, have revolutionised the IT industry, with many SaaS providers opting to use cloud providers and migrating away from on-premise hardware (although we are seeing the pendulum swing the other way with AI projects).

The SaaS models in many cases brought a plethora of on-demand tools and services, a complete shift away from traditional operational IT plans.

As with many industries though is SaaS feeling the pinch based on the pricing models which were the bedrock of there growth.

The Subscription Squeeze

Platform-driven services such as SaaS are always dependent on both sides of a model working. An example (albeit a cheesy one) I use internally:

“If Air BNB had no homes to let, it would fail the people going onto the platform to book a stay; if UBER had no drivers, the passengers would not use them. Alternatively, if either had no users, the platforms would die on both fronts.”

Therefore, “born on the cloud” SaaS providers need platform economics to drive rising cloud consumption costs while also funding development to retain monthly users who may be transient if adopted based on cost switching. This becomes a higher risk. The good news is that Cloud providers dropping egress charges helps with new platform migrations, albeit on-premise or cloud.

The end-user or ultimate consumer is reducing; therefore are companies looking to reduce costs on users and negotiating renewal contracts hard, of this I have no doubts. Is it time for something new?

My vote would be yes, the many SaaS companies who extolled the values and virtues of SaaS vs. on-premise hardware now need to look at new dynamics, the users are.

Embracing the Consumption Model

Loss aversion is a strong motivator of change, whether in a business cost or personal expenditure. We all despise paying for something we did not use. The plethora of unused SaaS licences will be subject to the FINOps movement, and it will take time to modernise or get cancelled.

Learning from software history

In previous downturns or disruptions, there have always been historical precedents; it’s a case of hunting those nuggets of wisdom and bringing them back like a tardis to execute now.

Sorry, reader, I had to tease that out. For users that are now subject to loss, why not take back the age-old model of licence metering? whereas the consumption is based on licences used rather than a fixed number; another great term for this was floating licence.

Move to a model that offers these licence types or alternatively look at going completely in on consumption.

Consumption models, currently being practised by companies such as AWS, IBM Cloud, Azure, Snowflake and many others, is where the cost is based on usage, I.e. you have used 100GB of storage and that is what you pay, if it reduces you pay a reduced fee.

This model is fairer, based on AI workloads coming into systems to be analysed, and for the SaaS providers, you do not get slammed in the renewal as you have migrated to a fairer model for you and the customer.

Prepare for a big “Downskilling Surprise”

The march of AI is relentless; It reminds me of the verticalisation concept, and it impacts every business sector. An example of this is Salesforce, which sold sales tools across multiple industries because it was not verticalised.

Across every sector, AI is being implemented to drive efficiency and to make us leverage tools such as code generation through to websites and predicted failure, even tackling the age-old concepts of complex systems such as traffic management.

As AI tools undergo the usual technology adoption process and grow smarter and more ubiquitous, we will experience a paradigm shift. This uninvited shift will be the de-skilling of the workforce. As AI gets smarter, many jobs will not need the required skills, and we will see AI coexist in these roles.

This democratisation of technology skills and white-collar tasks will eventually see disruption. With this disruption, as always, will come opportunities (I will be discussing this in another blog soon).

Conclusion

The impact of AI on tech business models extends far beyond the shift from subscription to consumption-based pricing. It’s reshaping the very nature of tech work, influencing decisions about infrastructure (on-premise vs. cloud), and altering the skill sets required in the industry.

Adopting, consumtion-based models, I predict, will be the next shift we see in technology spending. Like the shift to SaaS models, will we see a huge shift to fairer licence models?

Let me leave you with a quote from Daniel Kahneman.

“Loss aversion refers to the relative strength of two motives: we are driven more strongly to avoid losses than to achieve gains.”

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