How companies can get started with Artificial Intelligence

James Longbottom
James Longbottom

Deloitte modelled AI efficiency by using a range of simulations which evaluated level of investment against the average level of efficiency achieved based on that investment. The following table shows the outcomes: 

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How companies can get started with Artificial Intelligence?

This means, even at low level investment, 20% saving can be achieved using even the most simple AI system. 

Robotic process Automation (for more on RPA click here) could improve on that up to 100% efficiency saving. 

But how to get to that point, from a company perspective, be it start up, or multinational organisation?

The process is as follows (from a Pro AI perspective)

Human intelligence 

It needs to start with an interaction with a consultant, this can kick off that project in simple terms. This can lead to a quick evaluation of “can AI help me or not”. To set up one of these meeting for free, click here

Business evaluation 

If there is a fit for AI, the next step would be to conduct a business evaluation of the company in question, this spits into 3  different areas:

  • Current process evaluation – reviewing and questioning the current status quo of the organisation, with regards to the process or systems that an AI could work with or for. For example this could map the steps that documents in a company take, or what people are involved in doing certain things in a typical working week.
  • Personnel interviews – quick discussions or reviews of what operations take place, this informs “Use cases” which are essentially – “It does this” in a company, ranking these in terms of effort or cost is a great way to evaluate what a companies biting points and high absorbing cost areas are. 
  • Cost evaluation – reviewing what the maximum level of investment and how long returns would be based on past systems or expected ROI. This would take a level of cost (based from the previous 2 bullet points) and generate a timeframe for the ROI if an AI was employed. 


Putting together all these metrics, a design can be made, including what the AI would include, what systems would be needed, how long the project would take, how much the project would cost, what resources are needed and a road map to deliver such a system. 

Minimum Viable Product

If the proposal was accepted this would lead to the delivery of a minimum viable product (read more on what MVP is here) This would start providing Ai powered efficiency into the company! 

Scale and build depending on 

From there, using further cost evaluations based on how effective the MVP system is, the project could be scaled in the right direction based on what the system is doing best, what further evaluations found when using the system and much more. The best thing is that (if the system is user focused) the system evolves organically depending on real requirements. 

Manage benefits vs cost 

Good AI projects are based from expectations, keeping the project grounded on financials and expected returns are an easy way to keep the business case for AI systems relevant in any company. 


Be more efficient 

To conclude, repeating the scaling and building of this process can lead to better and better things, maybe even 200% extra efficiency saving as Deloitte simulates. 

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