AI knowledge

AI knowledge for decision-makers

AI is unavoidable

Like electricity and the internet, AI is becoming a natural part of the economy and society.

The question of whether we are too early or too late or whether AI is actually relevant for our companies or society has long since been decided. And the process will only be accelerated by new technologies for energy generation and storage or quantum computers.

The question is therefore not "whether at all", but exclusively with what degree of efficiency companies can deploy and use AI.

Those who wait will be replaced

"Within the next 3 years, anything not connected to AI will be considered obsolete or ineffective"

McKinsey: "Beyond the hype: Capturing the Potential of AI and Gen AI in Tech, Media and Telecom"

McKinsey's assessment is not an empty threat or a distant future scenario. Practice already shows that early adopters are not only gaining decisive competitive and cost advantages through the use of AI, but are also ahead in the race for talent.

Generative AI and AI agents can almost completely automate so many administrative tasks that entire organizations can be rethought and redesigned. And we are only at the very beginning of the rally for autonomous systems and humanoid robots.

As fast as technologies transform, organizations and people follow slowly and sluggishly.

Established companies are therefore under immense pressure to maintain or further expand their market position in the face of new market players that can start out on a greenfield site without any ballast.

Without AI, neither one nor the other will succeed - and the time for decades-old recipes such as "wait and see", "we'll sit it out" or excuses such as "we're not ready yet" or "we have to sort it out internally first" has run out.

If you don't act, AI reality will catch up with you.

Documents as a source of knowledge

Since ChatGPT, Gemini and CoPilot at the latest, language understanding has been used to analyze correspondence or documents in order to support automated processes in sales or customer service.

Interpreting sensor data

The machine processing of sensor data in real time is a typical task in a data fabric that enables real-time reactions with machine learning.

Integrated use of data throughout

The cross-system use of previously non-integrated systems enables completely new ways of working and the creation of data-driven scenarios

Process data in real time

Transaction-heavy tasks such as booking engines, online stores and portals benefit from the real-time capabilities of data fabrics and AI.

Complex and unstructured data

In data lakes, data of any structure and complexity can be combined with structured data and queried. This allows the simple mapping of previously very complex processes.

Intelligent automation

Event-based pattern/image/video recognition is an important discipline of AI models to analyze data, images and videos partly in real time to enable important industrial automation.

Intelligent analytics

Real-time AI-supported analysis of your data, processes and events is a typical task in data fabrics.

Intelligent solutions for global scenarios

The targeted, filtered real-time synchronization of data in different country zones is a common field of application for AI and data fabrics

Language-based queries

Natural language queries are a core task of AI models that use semantic information in data fabrics for smart answers that make your employees' work much easier.

AI maturity level

The efficient use of AI benefits from the continuous improvement of subsequent disciplines. The better companies master all areas, the higher the level of AI maturity.

Your data strategy points the way

In order to be able to use AI effectively and efficiently, a few prerequisites must be created that very few companies have at a sufficient level of maturity. As the level of maturity increases, companies benefit sustainably and effectively, but the journey is the reward. Every step forward at every stage of development helps companies.

Data fabric and AI as a foundation

If you want to use artificial intelligence (AI) effectively, you need to combine data and AI in the best possible way. A data fabric provides data management for all enterprise data, data governance ensures an overview and control, and AI and analytics make your data valuable and usable.

AI and data security

Data governance and AI usage guidelines provide the professional, ethical and automatable guidelines for the successful use of data and AI. Establishing these foundations is an ongoing (learning) process that must be validated or expanded with each use case.

AI and data capabilities

From infrastructure to processes, knowledge and change management, the use of modern data management and AI comes with many organizational and technical challenges. The development of AI knowledge and skills requires teamwork between internal and external knowledge holders as well as the persistent support of strong decision-makers.

Private, hybrid or public cloud?

Despite all the AI euphoria, concerns about data security, costs and scalability are important issues when designing the right AI and data fabric architecture for you.

Even though the basic technologies from HPE and Microsoft can be used in many different ways, the individual requirements of your company for the integration of data sources, your data types and volumes as well as your use cases play a decisive role in the design of your specific AI and data fabric architecture. Below you can see the most common scenarios that we find and support for our customers.

Private cloud

Private Cloud AI and Data Fabric leaves your company data of every sensitivity level and your questions to a private cloud AI engine in your own network.

Secure | scalable | affordable

We rely on HPE's ready-to-run solutions for private cloud AI. Sensitive and load-intensive AI scopes particularly benefit from this setup.

Hybrid Cloud

Hybrid Cloud AI and Data Fabric ensures that sensitive data remains exclusively in the private cloud. For defined scopes, however, data and queries are processed in the public cloud.

All options open

Any scenario is conceivable in hybrid setups. Flexibility may be necessary, but in case of doubt it is also complex and expensive and needs to be carefully considered.

Public Cloud

Public Cloud AI and Data Fabric process all data and queries in the public cloud. This model - with all its security concerns - has been the standard for many companies to date.

Convenient | limited scalability

Public cloud conveniently delivers shared services and is an ideal model for smaller data and AI scopes and limited concerns about using your enterprise knowledge in the cloud.