Going Non-Linear

Knowledge-intensive services firms have an inherent constraint: as they grow revenues, so too, their costs grow in the same straight line trajectory. This is Linear Revenues.

Any unbilled hours are an instantly perishable asset in the services firm: you cannot warehouse time. No wonder services firms barely reach x1 revenues valuations, whereas cloud software firms regularly achieve x5 revenues valuations on exit. With this stark difference in mind, there is a compelling business case for knowledge-intensive services firms to consider Going Non-Linear.

From Analog to Digital

As a knowledge-intensive services firm you may be focused on marketing, IT services, legal, accounting, or management consulting. Whatever your specialisation is, taking the first steps towards Going Non-Linear is very clear: it's the journey from analog to digital.

In a recent Harvard Business Review article, entitled Putting Products Into Services, the author talks about how tech product firms, such as Google and Adobe achieve gross margins of 60-90%, whereas consultancies, law firms, ad agencies and other professional services firms struggle to achieve anything above 40%. Here, the argument calls for applying the power of 'algorithm-driven automation' and data analytics to productise services.

In this journey from analog billable hours to digital subscriptions from technology, knowledge-intensive services firms can apply Mutual Value Discovery to create technology from know-how and, crucially, validate the relevance to clients, by engaging them from day one in this process.

Technology Enablers

When client and firm come together in a Mutual Value Discovery, the key to enabling a digital outcome in short order is the selection of the right Platform. Conceptually, the Platform should be a number of layers of prebuilt business logic in the cloud, enabling subject matter experts, data scientists, user experience (UX) designers and software developers to come together to create a new product from know-how of the services firm.

In detail, this Platform may include emerging technologies: a choice of prebuilt AI, Blockchain, Big Data and Machine Learning tools and frameworks, sitting on top of robust cloud infrastructure. In turn, this underlying technology provides the Platform-as-a-Service (PaaS) and Infrastructure-as-a-Service (IaaS) layers, supporting what will become a Software-as-a-Service (SaaS) product outcome - typically delivered via desktop PC, tablet and smartphone user devices.

Pricing Models

Going Non-Linear means either augmenting or even replacing services based on the billable people hours Pricing Model with scalable subscription fees, where price is aligned to measurable value outcomes. In practice, this needs detailed validation to be generated through creating a series of ROI Models for the Mutual Value Discovery engagement between the firm and would-be early adopter clients of this new productised service.

The Pricing Model could be a simple usage model - e.g. per user per month. However, it is likely that clients will increasingly insist on payment (or at least part-payment) for outcomes. This obviously presents an opportunity for knowledge-intensive services firms to disrupt business-as-usual offerings, where billable people hours are typically based on a time and materials consumed approach. The Mutual Value Discovery engagement will enable the right Pricing Model to emerge.

Next Steps ...

Going Non-Linear is easy to granulate into small, manageable steps for any knowledge-intensive services firm: digital agency, IT services, law firm, or management consultants. It starts with the first Mutual Value Discovery engagement: the firm and one or more friendly, would-be clients who instinctively know that there is a better alternative to the billable people hours business model.

It requires tech experts to join the process: insiders and outsiders who understand the latest Platforms and technology enablers - prebuilt AI, Blockchain, Big Data and Machine Learning tools and frameworks - plus underlying world-class PaaS and IaaS cloud ecosystems, such as Amazon Web Services, Google App Engine and Salesforce Heroku.

What follows is an iterative process: mapping human know-how to the the power of 'algorithm-driven automation' and data analytics to productise services. This is the pragmatic journey from analog to digital: and creating a win-win for clients and services firms alike.