Applied AI Engineering

Leveraging AI to Drive Value Creation

jambit prioritizes relevant AI projects, establishes the technical foundation for productive implementation, and integrates AI into business processes and digital products. This transforms AI potential into robust solutions that deliver tangible business value.

Applied AI Engineering | jambit

From AI Potential to Productive Value Creation

With the advent of generative AI, new opportunities have quickly become accessible to many companies. Individual tasks can be accelerated, ideas tested more quickly, and initial applications developed with manageable effort.

The bigger challenge begins afterward: Which projects justify an investment? How can prototypes be reliably scaled up to production? Where does AI improve a business process or a digital product? And how can costs, quality, and technological dependencies remain manageable?

jambit integrates these decisions with engineering and productive implementation. This ensures that AI is deployed where it can deliver tangible value to companies.

AI Is Becoming a Factor in Investment and Engineering Decisions

The more deeply AI is integrated into value creation, software, and business processes, the greater the impact technical decisions have on costs, risks, and future operational flexibility.

The selective use of tools can boost individual productivity. However, sustainable business value is created when companies select relevant initiatives and robustly integrate AI into their processes, products, and system landscapes.

For companies, this determines

  • which AI projects justify further investment
  • how quickly successful approaches can be put to productive use
  • how transparent and controllable costs and quality remain
  • how dependencies on models and providers are deliberately managed
  • how effectively AI is integrated into existing processes and digital products

Where AI initiatives are faltering

Use cases and experiments emerge more quickly than sound decisions about which projects are economically viable and technically feasible.

AI speeds up individual tasks, whilst workflows, handover processes and systems remain largely unchanged.

The AI function works in principle, but architecture, integration, quality and operations have not yet been adequately taken into account.

Models, providers and technical access points are evolving in parallel, making it difficult to achieve transparency regarding usage and interdependencies.

New functions are tested in isolation, rather than being meaningfully integrated with software, data and real-world user processes.

Four areas of action for productive AI

AI projects start from different starting points. That is why we begin wherever your specific project currently stands – whether it’s at the investment decision stage, the technical production capability stage, a business process or a digital product.

AI Strategy & Prioritization – Focusing Investment

Which AI projects create meaningful value and should be pursued?

  • Identify AI potential and specific use cases
  • Assess economic value contribution, feasibility and risks
  • Develop a vision and priorities
  • Consolidate investment and implementation steps into a realistic roadmap
AI Architecture & Operations – Building Production Capability

How can AI be deployed in a reliable, controllable and economically productive manner?

  • Designing AI architecture and infrastructure for productive deployment
  • Flexibly integrating models and providers
  • Ensuring transparent control over usage, quality and costs
  • Ensuring the technical reliability of operations and further development
AI in Business Processes – Making Processes More Effective

How can AI specifically improve workflows, handover procedures and decision-making?

  • Identify relevant AI levers within existing processes
  • Allocate tasks effectively between AI, software and people
  • Redesign AI-supported and agent-based workflows
  • Integrate processes technically and implement them effectively
AI in Applications – Enhancing digital products with AI

How can AI become a meaningful part of existing or new digital products and applications?

  • Aligning AI functions with specific user problems
  • Integrating AI with UX and software architecture
  • Integrating models, software, data and systems
  • Implementing and further developing digital products effectively

We pick up where your AI project is at

Not every AI project begins with a strategy phase. Perhaps a specific use case has already been identified, a prototype is in place, or a process has been identified that is to be improved using AI.

You can therefore start with any of the four areas of focus:

  • Strategy & Prioritisation provides clarity on value contribution and investment priorities.
  • Architecture & Operations lays the technical foundation for productive and manageable deployment.
  • Business Processes integrates AI into specific workflows and value-creation processes.
  • Applications integrates AI into digital products and software systems.

We link the relevant areas of focus where they need to work together to ensure a viable implementation.

From the decision to a fully operational AI system

At jambit, AI doesn’t stop at a recommendation or a prototype. We integrate the decision on suitable areas of application with architecture and software engineering, and develop the resulting systems right through to production.

This reduces the number of handover points between strategy and implementation. Technical feasibility, costs and operational viability are taken into account at an early stage and can be incorporated directly into development and integration.

What this means for your AI project

Manage investments in a more targeted manner

Making production roll-out more predictable

Maintaining technological agency

References: Applied AI Engineering in practical use

Selected projects demonstrate how jambit AI is integrated into digital products, processes and production system environments under real-world conditions.

StuByte FlexCo - RevealyIQ

Productive AI for scalable product data.

F.A.Z.

AI in the Newsroom – Machine learning for the F.A.Z. paywall

Mer Solutions GmbH

Digital PreCheck in the Mer Connect Plus app.

Next step – taking your AI project to the next level

Would you like to assess the potential of AI, make an existing approach ready for production, or integrate AI effectively into processes and digital products?

Together, we’ll identify the most relevant levers and use them to develop the next concrete step for your project.

Robert Kowalski jambit Head of Business Division Media

Robert Kowalski

Chief AI Officer

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