Monetarisation of AI

1. Status quo

Adoption of Artificial Intelligence (AI) technologies has been relatively slow so far. This has been changing rapidly in the past two-three years. However, the question of what the main hindrance for AI implementation remains. A couple of areas could be highlighted:

  1. Difficult to measure Return on Investment (ROI);
  2. Long time to market.

Let us analyse each point. AI can be used for making existing processes more efficient or developing new products. However, the below reasoning is valid for either scenario.

  1. Firstly, how could one assess economic returns from investing in AI? One way could be by monitoring company’s KPIs. If those are improved due to implementation of AI then one can measure Return on Investment (ROI) that has gone into implementing AI.

However, one only derives economic value from AI through deploying the models into production and not keeping the work on a purely academic level. This has been confirmed by findings in several reports, such as McKinsey[1] & Deloitte[2], that found that progressive AI practices are rewarded. Companies seeing the biggest increases in earnings from AI were not simply following common practices, such as establishing machine learning operations, MLOps, and IT process automation (AI for IT operations, or AIOps), but also spending more efficiently on AI and taking a greater advantage of new technologies. Secondly, the time to market. Entire departments might need to be set up and developed to identify usable data streams, create, train AI models, etc. On top of that, once a new technology is adopted, there is a dip in KPIs and performance. At times the length and the depth of such a dip impedes adaptation of new technologies.

2. What’s missing for improved KPI optimisations?

A lot has happened in the past years. However, there is, of course, substantial room for improvement in the economic returns from investments in AI. This can be achieved via:

  • Improvement of accuracy of AI models
  • Wider availability of models

2.1 How can improvement in KPI optimisation be achieved?

Introducing MIRANDA, which allows to exchange insights in a privacy preserving fashion to improve accuracy of models.

The number of AI models developed world-wide is, probably, approaching the infinity at this stage. This is no news. What is the crazy part in this? Many great initiatives get shelved or are kept at a purely academic level and don’t get a chance to reveal their full potential.

This is painful and frustrating for any company, especially for smaller ones with limited resources that need to adopt latest technologies as a core part of their business: medical companies looking to develop new and personalised treatments, fintechs looking to keep and expand their customer base through minimising customer churn… The list could go on, but you get the point.

To help them overcome these challenges, MainlyAI set out to build a platform that enables companies across industries to share insights across companies and even drawing upon insights from other industries. We can take the opportunity to name one concrete example here on how one can capture opportunities to learn from insights from other companies or adjacent industries. A drug company can apply insights from one type of autoimmune diseases, e.g. Diabetes type I, on the autoimmune disease it is researching, e.g. Rheumatoid arthritis.

We facilitate and democratise adoption of AI technologies, so any company can focus on their core business instead of building out support tools. The first indications for the future of such a platform have been very positive. MainlyAI has already been granted government funding for several high-profile projects.

Pardon, the obvious bias, but we think this is the next really, really big thing. It could be, in line with what Google did for search engines or Apple for app distribution. As crazy as it might sound, we are happy to explain our thesis, based on our own AIStore ™©, which allows storing and improving your existing models and purchasing trained or untrained models, off-the-shelf.

Should you like to join us on democratising AI in the society and help some very innovative companies in their continued growth along the way or, perhaps, you work for a company that is currently considering implementation of new technology – please reach out, we’d love to hear from you.

Regardless – if you are interested in learning more, go check out mainly.ai & follow us on LinkedIn.


[1] https://www.mckinsey.com/business-functions/quantumblack/our-insights/global-survey-the-state-of-ai-in-2021

https://www.mckinsey.com/~/media/McKinsey/Business%20Functions/McKinsey%20Analytics/Our%20Insights/Global%20survey%20The%20state%20of%20AI%20in%202021/Global-survey-The-state-of-AI-in-2021.pdf

https://www.mckinsey.com/industries/technology-media-and-telecommunications/our-insights/tipping-the-scales-in-ai

https://www.mckinsey.com/business-functions/quantumblack/our-insights/winning-with-ai-is-a-state-of-mind

[2] https://www2.deloitte.com/us/en/insights/industry/technology/artificial-intelligence-roi.html

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