Enterprise AI Management today

Enterprise AI Management is the action of injecting intelligent processes into an organization, networking all departments and business units in a way that silos get connected, friction, productivity leaks get identified and autonomously fixed, data will no longer need to be cleaned up as AI can do that on the fly and operations experience productivity gains of more than 15%. Enterprise AI Management is not replacing the old but orchestrating the new. Today's Zurich is built on the Zurich from 2,000 years ago. The AI Layer is a grandiose way to build new technology on top of the old without ripping everything apart.?We will talk more about the 'Zurich Model' in another post.

Injecting intelligent processes into your business

Conventional business processes are manually created, suggest specific behaviors for the teams, and, at best, are guided by a computer requesting data entry and output to be visualized, measured, and managed.

Intelligent business processes have significant 'Intelligence' in the processes. What does it mean "Intelligent Processes"? It means:

• Changes that always cause headaches can be intelligently recognized, assessed, and drive process adjustments.
• Employees can always report issues but don't need to be reviewed by a human. Instead, the AI reviews the challenge, assesses it, compares it with other reports, and creates an intelligent report about what to do, how to fix it, and maybe even creates the fix itself.
• We focus on everything that needs Intelligence, has to do with Complexity, and results in Productivity (ICP) as a perfect field for AI.

We are no longer dealing with conventional SOFTWARE instead we are dealing with INTELLIGENCE

The biggest problem with AI

Our language is not advanced enough to articulate how AI works, the tangible benefits, and why it matters to you. It's like our inability to explain how a piano sounds. However, now we have some good use cases:

Enterprise AI Use Cases Today.

Chatbots and co-pilots are great tools to start with, but they are far from 'Mission-Critical'. By now, over 100 medium—to large enterprises on our platform have evolved with use cases that go far beyond bots and tools. None of the solutions below could be created with conventional software.

Enterprise AI Solutiuons must be Mission-Critical - And thee won't come from conventionnal software providers.

1) Using Compliance Assessment for Everybody

When building a new compliance management product for our internal AI product development, we build an agent checking our results against the new EU AI Act. In the beta process, we learned something new: The biggest problem with compliance is that it drags down performance across an entire enterprise because one cannot train and regularly retrain 100,000 employees. So they have to check with the compliance department about every new product, major product updates, new processes and more. So, we enhanced our internal compliance application to a full product so that any employee can ask the agent at any given point in time about their level of compliance.

Initially, they send the agent's report to their compliance manager, who can quickly determine whether it needs more review or is simply acceptable. The gain is

• Time to market
• Resource reduction
• Productivity gain
• Process intelligence that can further enhance the compliance assessment

2) Observing and closing Productivity Leaks

The bigger the company, the more productivity leaks there are. Many are too small to reach any decision-maker, but they will be accepted. In a 5,000-person company, that may be 100 hours per person per year. In this case, we are talking about 5,000 x 100 x 30 € = €15 Million.

Remember that these are the minor issues that arise with every merger or acquisition: new product line, new ruling, new growth step, new market shift, and so on.

And even if some of the best consulting companies could fix it, 12 months late,r they start from scratch. "Change is the new normal". Intelligent systems consider the change and run continuously, catching new leaks as they arise. This is not a technical solution with complex measurement systems. It is done with a bottom-up approach where the victims of those processes, the employees, can help report their experience, and the AI does the rest. No conventional software could do that.

The biggest advantages include

• Continuous workflow observation, suggestions, and autonomous adjustment
• Increased employee satisfaction to alleviate the nerve-killing administration
• Direct and measurable increase in productivity
• Process implementation that can further increase productivity over time
• Intelligent process implementation that can learn from arising changes and prevent future leaks

3) Innovating at unparalleled speed

Many companies were told to stop innovating and fix their problems first. Although this may sound like the right thing to do, it often also influences a nation to deindustrialize.

With an Autonomous Innovation System, we have demonstrated that an end-to-end innovation process can be done in 8 hours. End-to-end means the following process:

• Innovation opportunity discovery
• Ideation for new solutions or even breakthroughs
• In market idea validation
• Innovation Financing
• Innovation BluePrint development - possibly including tools building
• Minimum Viable Product for beta testing hardware or software
• Innovation-to-Market strategy and execution
• Solution scaling and going global
• Process intelligence that can adjust to new and future innovation requirements

Interestingly enough, ideation is just about 12% of the process. Execution is everything. Within 8 hours, the concept and large parts of the execution are done. Human interaction points in every agent delay the process and production development unless robots are directly connected to the solution, which will take another chunk of considerable time. Realistically, a breakthrough innovation still takes one or two months, not five or 10 years.

5) Stop seeking your own best use cases. Use AI to do so.

Ironic - yet so obvious. You have a challenge: use AI. In any situation. Like seeking use cases for AI within your organization. To do that, we created an entire agent set. That does it within 48 hours across your organization.

TRANSFORM is an AI application that lets your employees tell their stories of friction, waste of time, silly rules, uncoordinated processes, old mechanisms, and more. The AI assesses the team's response in real-time and can help make their story clearer. Then, assess the entire stream of inputs, organize and structure it, and develop the best AI Use cases you could get

6) Managing their banking processes like never before is possible.

Another use case is an AI Agent System that considers all banking data, from annual reports to detailed internal reports and assessments. The agent system aims to reduce the current CIR (Cost-Income Ratio) to below 50% to make the bank healthier.

This solution is exclusively designed for private banks. However, the base architecture and intelligence design could soon be adjusted for retail and business banks.

The core benefits include

• Banking process optimization - mainly with the help of AI.
• Reducing internal friction from overly aged processes.
• Increase in productivity.
• Making room for new business field development without extra cost.
• Establishing intelligent processes that can learn and help adjust to even more changes.

7) Our Own Use Case

We are developing Enterprise AI Solutions. These solutions include enterprise organization management, role and rule management, unlimited users, simultaneous processes, autonomous data consolidation in the backend, and countless other unique features.

The average development time of such solutions with conventional software is 3-4 years. We will release approximately one new solution every month. This incomprehensible speed is possible because we use Agentic AI to the max.

This example may be helpful to understand what you could do to make a quantum leap ahead of your competition.

Moving from Theory to Practice

Next Wednesday, March 12, we will conduct an online event to introduce the next?Enterprise AI Solution?and give a quick overview of the solutions we plan to introduce in 2025.

https://bluecallom.com/business-transformation-with-ai/

Would love to welcome you to this event.

Where do we go from here?

Workflow Shifts

In the next 12 to 18 months, workflows will increasingly incorporate intelligence. This will allow less trained employees to work on projects they have never heard of. This, in turn, will help overcome the 'Skilled Worker Shortage' challenge, enable more rapid job changes, ease re-entry into the workforce, and address many of those education-related challenges. Work will get done much faster, freeing up time for more human-to-human relations with customers, patients, suppliers, business partners, and other relevant business companions to expand the business.

Education will have a completely new meaning. The new 'Centers of Knowledge' are AI Large Language Models. Selling knowledge in any form will become very tough as it is omnipresent. On the other hand, applying knowledge and connecting knowledge with businesses will rise in importance. And equally important creating new knowledge fast.

Future of Work

Humans in business will become managers. AI will end the blue and white-collar separation, and all will need to take managerial roles. The new difference is in managing people versus managing agents. But in no case are we actually performing work that requires specific skills. Our common and most important skill set will be "articulation". Managing an Agent requires articulating a human intent to a machine - no matter how knowledgeable it is. Intent Management is the job we will perform. And to do that well, articulation is the key. It all started with "Alexa - play TuneIn station" or "Siri - what time is it". But now it is "Hey Mentoha, show me my project performance and tell me what should be next on my priority list".

Educational Shift

We will still need to Read, Write, Calculate, and understand our History. We also need to know the basics of Physics, Biology, and Chemistry. Equally important is learning articulation, exploring our goals and wishes, and communicating and managing our intent with the help of agents. No car mechanic, let alone a driver, knows all the ins and outs of modern cars. We don't need to fully understand AI Agents, but in both cases, we need to be very well educated in their respective behavior.

Future of Education

The speed at which things will evolve over the next 24 months will exceed the speed at which any university or school can adjust its curricula. That means most of the University degrees will entirely lose their value. The time spent at a university for a conventional degree is too precious even to explore it. Instead, educational institutes will survive on classes that teach the current and future possibilities and provide lifelong learning subscriptions in all academic fields whether they are "certified" or not.

Why do those solutions always come from Europe?

Another interesting side of AI Enterprise Solutions is the dominance of European solution developers:

Amadeus (ES), Asseco (PO), Atento (ES), BlueCallom (CH), Dassault (FR), Hexagon (SE), IFS (SE), Micro Focus (UK), Proximus SpearIT (BE), Sage (UK), SAP (DE), Temenos (CH), Visma (NO), Yandex (RU)... to name a few

No other continent hosts as many global players in complex enterprise solution development, such as ERP, CAD, Complexity Management, and similar solutions, as geographic Europe. We recognized this and started BlueCallom in Europe after living 20 years in Silicon Valley. Today, Switzerland has the highest density of AI companies, with over 100 companies in the Zürich region alone. We also see the highest density of US companies creating their AI centers in Switzerland, mainly Zürich.

The reason is believed to be the ability to think in large and complex structures and explore their permutations until they successfully and reliably perform the designated job.

Keep Europe Great!