Using AI to supercharge your businesses productivity

Part 3

The next lens is how you can start to identify and use AI in your business. An important point here is to be on the lookout for ‘snake oil’.  Every provider, consultant, conference and ‘exclusive roundtable candle lit supper’ I am now invited to has an AI slant.  Educate yourself so you are vaccinated against being mis-sold products and services that are little better than scripts.

There are two key terms that I have heard that I think are going to be discussed more and more;

1.      AI Transformation - all digital services need to be intelligent moving forward, all existing need revisiting, see part 2 of this series.

2.      AI Aptitude - humans need to learn a new way of interacting with computers, not unlike when search engines came along but now it can actually be dangerous

Technologies

There is a race going on a big one.  Enterprise AI.  SAP started the early ERP race in the mid 90s and proceeded to dominate for some time, arguably still does.  Enterprise AI is going through the same evolution, except much faster.  Microsoft are now live with Co-Pilot, different variations for different use cases, Google are deploying DuetAI and others will be sure to follow. The key here is being clear on capturing your processes, particularly the ones that drive value, then applying the data, architecture and experience points over that so you can start to select the right tools.

Here are some use cases that immediately spring to mind.

Finance

1. Risk Assessment: AI can analyse market trends and financial indicators to assess the risk levels associated with various investments.
2. Fraud Detection: Machine learning algorithms can detect unusual patterns and potential fraudulent activities in real-time.

Operations

1. Supply Chain Optimization: AI can forecast demand, optimize stock levels, and even suggest suppliers.
2. Quality Control: Computer vision can be used to identify defects in products, improving the overall quality.

Commercial

1. Customer Segmentation: AI can analyze customer behavior and segment the customer base for targeted marketing.
2. Sales Forecasting: Machine learning models can predict future sales trends based on historical data.

Logistics

1. Route Optimization: AI algorithms can find the most efficient routes for transportation, reducing costs and time.
2. Inventory Management: AI can predict when stock is likely to run out and automate the reordering process.

Technology

1. Cybersecurity: AI can monitor network behaviour, detect anomalies, and protect against potential threats.
2. Software Development: AI tools can assist in code generation, bug fixing, and even design aspects.

Human Resources

1. Talent Acquisition: AI can sift through resumes and predict candidate success, streamlining the recruitment process.
2. Employee Engagement: AI can analyse employee surveys and feedback to identify areas for improvement in workplace culture.

The key is to think about how you can use the above examples with existing efficiency tooling to really drive transformational efficienceies. These AI capabilities are the brains, automation and low-code/no-code tooling the action taking part of the equation and data is the fuel that runs it all. I need to be frank here, it will be disruptive, very disruptive the next 12-24 months we will see significant numbers of people needing to change what they do and how they do it. Some wont, they will fail.

The support services in most organisation will be able to operate on significantly less people...if done properly there will be an accompannying INCREASE in productivity as well. I would look to a plan with these high level actions.

AI excels at human to machine and machine to human interaction. Human to human, not so much. If you offer a human service in your business double down the investment here, savings unlocked in your efficiency drive needs to be redeployed into humans dealing with humans.