Sounds a basic question but we have seen several well intentioned AI projects stall as objectives get blurred. Can AI effect operational efficiency and cost reduction or is it a true driver of revenue growth for businesses?

To enhance the success of your AI project it should be clear whether you are addressing process automation (or what the market terms RPA – Robotic Process Automation) or AI.

Both are hugely important to business success and although they are surely converging they come from different perspectives.

RPA in industry goes way back with machines performing repetitive tasks. In an IT context it originated around data entry techniques and back to the days of ‘screen scraping’ of visual data and converting into a digital format to be automatically entered into backend systems. Nowadays RPA is key to operational efficiency in many industries, for example in financial services, client onboarding, invoicing, rate change uploads all use RPA techniques to improve efficiency and lower cost.

With AI however, the process continues to learn based on its outputs so each subsequent output becomes more reliable. It is applied to marketing and sales analysis, forecasting and continuous analysis of unstructured data from a variety of internal and external inputs and can analyse social media sentiment. Consequently, AI is more associated with revenue generation either directly by accurately predicting customer behaviour or in a softer context by improving the customer experience so they return and recommend others.

Businesses tend to expect the following benefits from AI:

  • Better data accuracy to enable informed operational or client decisions (decision making based on data not opinion)
  • Better engagement with and improved productivity from a skilled workforce (how can the knowledge of a business that lies within an organisation be more productive)
  • More agile workforce
  • More client oriented insight and focus

Short term cost reduction comes a long way down the list though obviously a more streamlined client focused business with a highly productive agile workforce will be more cost effective in the longer term.

From our experience, the biggest obstacle to many AI projects is the perceived immediate business case. We have seen too many projects judged by traditional ‘business case’ techniques that have tried to focus on short term savings rather than the revenue growth and longer term objectives and also failure to measure the softer concepts such as client experience. To measure client experience here is an example, this week I bought 2 products from 2 retailers, lets call them Lewis and Ashley. Lewis can give me free delivery, 2 hour delivery slots and even call me on the way to say they have been warned of traffic ahead (nice use of RPA and AI) while Ashley charges me for delivery, tells me it will come sometime on Monday only for it to arrive sometime on Tuesday. Lewis gets my next order and my recommendation to friends.

So before looking which cloud service to deploy your AI proof of concept taking a moment to revisit your project focus will ensure a greater success rate.

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