ChatGPT and other artificial intelligence tools have dominated the discussion lately. Their ability to mimic human writing and art raises concerns that machines may begin to replace white-collar workers, just as they took over many blue-collar jobs in the 19th century. At Digitate, we also think about the role of machines at work when we develop software tools to make the autonomous enterprise a reality.
In our vision of the “autonomous company”, machines (or rather AI algorithms) perform highly repetitive or defined tasks, while strategic, decision-relevant tasks are driven by humans.
You might think that this rule means it’s easy to decide which tasks can be assigned to machines. But as AI and machine learning become more sophisticated and powerful, the dividing line keeps moving. However, the distinguishing feature remains the same: whether the task under consideration deals with data in a defined or undefined manner.
Defined: Activities in the defined cluster provide all the information (data) and instructions you need to run them. No information is hidden, and the specific instructions for use can be calculated using the available data. Defined data activities are ripe for machine management. Undefined: Activities in this cluster do not provide all the necessary information to complete them. Intuition, interpretation, analysis, deduction and guesswork are required. Undefined data activities do not adapt well to machine management. Games can help to understand how to use AI
Games that are prime examples of these two clusters are chess and poker, respectively. These categories were first defined by the pioneering mathematician and computer scientist John von Neumann (who created an entire field of study with his 1944 book Theory of Games and Economic Behavior).
I was reminded of von Neumann’s award while attending a speech by scholar and poker champion Maria Konnikova last fall that covered some of the following points.
First think of a chess game. It has a defined set of pieces with specific roles, a clear set of rules, and a defined space (the chessboard). All data is shown for both players, with no hidden information (and no ambiguity as to whether a move is legal or not). The total number of all possible moves is very high, but not infinite. This means that a machine equipped with a good set of algorithms and enough processing power can beat any chess champion. (In fact, this first happened a quarter of a century ago.)
Now think about poker. It also has a defined set of playing pieces (a deck of cards), a set of rules, and a defined space (the card table). However, not all information is displayed; In fact, the central mechanism of poker is guessing what cards your opponents are secretly holding and then successfully predicting how they will bet. The game must be played with assumptions, clues and intuitions both about the cards available and about human behavior under certain emotional stresses.
Do you know when to fold them? That doesn’t pay off
Here’s the big difference: Machines don’t work well if all the necessary information isn’t available.
While I know people might argue that AI is advancing and mimicking human intelligence, there is no enterprise-wide application of such solutions yet. Machines won’t be able to beat us at poker, at least not for the next few years.
End-to-end business operations are more a game of poker than chess, as all the data is often not available. Decision making is often driven by limited data, information interpretation, and intuition.
Machines are very effective and efficient at managing tasks with a clear record and a well-defined set of rules, also known as standard operating procedures. In many companies, a wide variety of processes from sales to HR can be described with SOPs and thus automated. (In IT operations, where I’ve spent my career, 80% of tasks can be managed by machines.)
The typical journey to becoming an autonomous enterprise usually goes through these stages:
Manual: There is no machine support; All tasks are performed by humans. Advanced: There are specific routines that make repetitive tasks easier, but these routines are triggered by humans. (The most common phase these days.) Automated: The machine responds to a ticket (request from a human) and triggers a specific routine to solve the problem. Autonomous: Machines propose and execute actions to prevent incidents or improve overall performance. There is usually a supervision phase where the human “teaches” or models the machine actions that it will later perform without supervision.
At Digitate, we engineered ignio™, our flagship AIOps platform for IT and business operations, to become fully autonomous. After its “learning” phase, ignio’s proprietary machine learning algorithm can filter out excess information generated by the production environment and focus only on the activities needed to improve or correct the situation.
Always one step ahead with autonomous operation
Like any good chess computer program, ignio comes with a library of over 10,000 customizable moves (use cases) that can be applied when a situation arises. Of course, ignio will ask for human consent at the beginning before running the use case. But when the time of machine learning is over, ignio is ready to not only heal IT problems itself, but to optimize all kinds of business processes.
Conclusion: ignio is designed as an autonomous business solution for IT operations. ignio focuses on the entire landscape, not just on individual aspects such as data flow, ticket management or monitoring. ignio is not just a tool for a specific need, but rather a solution to make the IT-autonomous company a reality.
And you can bet your entire bet on this deal.
To see ignio in action, click here to request a demo.
