As I was sitting down to write this piece during the year-end holidays, I wondered if you played any games during your time off, especially chess or poker. If you did, you would have gotten some good training on where and how to apply machine learning and artificial intelligence for your organization’s autonomous enterprise journey. 

When we at Digitate say, “autonomous enterprise,” we mean an enterprise powered by machines and driven by humans. Machines fulfill highly repetitive or defined tasks, while strategic, decision-making tasks are driven by humans.  

You may think that rule means it’s easy to decide which tasks can be assigned to machines. But as AI and ML continue to become more sophisticated and powerful, the line dividing machine tasks from human ones keeps moving. However, the distinguishing factor remains the same: Whether the task under consideration handles data in a defined or undefined way.  

  1. Defined: Activities in the defined cluster offer all the information (data) and instructions that you need to perform them. No information is hidden, and the specific instruction to use can be calculated using the data available. Defined data activities are ripe to be machine-managed. 
  2. Undefined: Activities in this cluster do not offer all the necessary information to perform. Intuition, interpretation, analysis, deduction, and guessing are required. Undefined data activities do not adapt well to machine management. 

Games that are prime examples of these two clusters are chess and poker, respectively. These categories were first defined by pioneering mathematician and computer scientist John von Neumann (who literally wrote the book on game theory).  

 I was reminded about von Neumann’s distinction when I recently attended a speech by scholar and poker champion Maria Konnikova that covered some of the points below. 

First, think about a chess game. It has a defined set of pieces with specific roles, a clear set of rules, and a defined space (the chess board). All data is on display for both players, with no hidden information (and no ambiguity about whether a move is legal or not). The total number of all possible moves is very high, but not infinite. This means a machine equipped with a good set of algorithms and enough computing power can beat any chess champion. (In fact, this first happened a quarter-century ago.) 

Now think about poker. It also has a defined set of pieces (a deck of cards), a set of rules, and a defined space (the card table). However, not all information is on display; in fact, the central mechanism of poker is to guess which cards your opponents secretly hold, and then successfully predict how they will bet. The game must be played by assumptions, clues, and intuitions about both the cards available and human behavior under specific emotional pressures.  

Know when to fold ‘em? That does not compute

Here is the major difference: Machines do not do well when not all the necessary information is available.  

While I understand that people might object that AI is progressing and it is mimicking human intelligence, I stress that there is no commercial application of such solutions yet. At least for the next few years, it’s still the case that machines are not good poker players. 

Looking at an enterprise end to end, it’s closer to poker than chess because often not all data are 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 set of data and a well-defined set of roles.  In many enterprises, operations from sales to HR are categorized by a clear set of information and a well-defined set of roles, also known as Standard Operating Procedures. In IT operations, where I’ve spent my career, 80% of tasks can be machine-managed. 

So, if you can get 80% of your Standard Operating Procedures automated, your organization can: 

  • Increase customer satisfaction (shorter Mean Time to Recover/MTTR, more efficient incident management, better coverage) 
  • Decrease operating cost (better utilization of IT operational support) 
  • Increase your workforce’s satisfaction (through greater control of operations, no more trivial tasks, and mitigating the IT talent shortage) 
  • Gain more negotiating power for the internal IT owner over system integrators and other external experts (because tribal operational knowledge has now been transplanted inside a machine). 

The typical journey towards an autonomous enterprise usually moves through these phases:  

  1. Manual: There is no support by machines; all tasks are executed by humans. 
  2. Augmented: There are specific routines that alleviate repetitive tasks, but these routines are triggered by humans. (This is the most common nowadays.)  
  3. Automated: The machine reacts to a ticket, triggering a specific routine to solve the problem.  
  4. Autonomous: Machines suggest and execute actions to prevent incidents or improve overall performance. Usually there is a supervisory period where the human is “teaching” or modeling actions for the machine to take, which later it will execute with no supervision.  

At Digitate, we built our flagship ignio platform to become fully autonomous. After its “learning” period ignio’s proprietary machine learning algorithm can filter out excess information generated by the production environment, focusing only on the activities needed to improve or rectify the situation. ignio can access a library of more than 10,000 customizable use cases to cover any possible action. 

ignio eliminates the noise to focus on what actions need to be taken. 

Staying one move ahead with autonomous operations

Like any good chess computer program, ignio has a library of moves (use cases) to apply when a situation occurs. Of course, at the beginning ignio will seek human approval before executing the use case. But when the machine learning period is over, ignio is ready to self-heal technical issues. 

The bottom line: ignio is designed to be an autonomous enterprise solution for IT operations. ignio focuses on the whole landscape, not just single aspects such as data flow, ticket management, or monitoring. ignio is not merely a tool for a specific need, but rather a solution to make the IT autonomous enterprise a reality.  

To see ignio in action, click here to request a demo.