Close-up portrait of software engineer working on AI app

AI strategy: making AI work for you!

Unlocking data-driven excellence with AI-powered solutions in an efficient and agile manner across industries

Becoming an applied AI enterprise with T-Systems and Detecon

Global AI footprint is at its peak and the convergence of strategy and implementation has become essential for enterprises seeking to harness the transformative power of artificial intelligence. Developing a well-defined AI strategy provides a roadmap and implementation breathes life into strategy, translating it into tangible actions. Together with Detecon and T-Systems, our strategy and implementation create a synergy that guides enterprises towards AI-driven innovation, efficiency, and competitive advantage.

Unlocking the power of data thinking approach

Our collaborative AI approach at T-Systems and Detecon harnesses the power of three robust methodologies:

  • Design Thinking serves as our guiding framework, allowing us to explore, design, develop, and validate data-driven solutions and businesses with a user-centric, data-driven, and future-oriented perspective. This approach seamlessly integrates data science, fostering user-centred solutions with substantial business potential within cross-functional teams
  • In parallel, our agility methodologies, including sprint review and retrospective, Scrum, and sprint planning facilitate nimble development and testing processes
  • Finally, we adhere to the CRISP-DM methodology, ensuring a comprehensive approach covering business understanding, data understanding, data preparation, modelling, evaluation, and deployment

This integrated approach empowers us to drive innovation, user-centricity, and efficiency in our data-driven solutions and projects. As a strategic component within the T-Systems portfolio, Detecon has a proven track record of collaborating effectively with T-Systems on numerous projects. Our offerings, including the AI Solution Factory and the Telekom Data Science Platform, facilitate smooth AI training, development, and effortless deployment through cloud platforms.


Get in touch for a responsible AI strategy!

Our team can help you aptly implement AI, establish your organisation’s AI strategy, foster a culture of AI adoption, or answer any AI-related issues. Contact us and we can guide you to AI success.

AI strategy

An all-encompassing strategy delineating the organisation’s utilisation of AI to realise its business goals. This plan encompasses tasks ranging from pinpointing viable AI applications, allocation of resources (both human and financial), infrastructure enhancements, implementation schedules, and metrics for gauging achievements. “Assessment of preparedness: a methodical evaluation of an organisation’s readiness for the effective integration and embrace of Artificial Intelligence technologies. This assessment scrutinises the current state of IT infrastructure, governance, organisational structure, and roles that may either facilitate or impede AI adoption.” Reach out to us to know more!

Discover all aspects of AI with us

AI use cases development

  • AI use case ideation/selection
  • AI-based product/service design
  • AI-based process optimisation
  • AI-based business models
  • AI PoC development
  • AI solution design implementation

AI ethics

Responsible AI:

  • responsible AI encompasses the establishment of values, policies, standards, and legal boundaries to guarantee the ethical and transparent utilisation of AI, with a commitment to preventing harm
  • Ethics, regulation, and principles: mitigating risks related to reputation, liability, and business involvement

Leadership and oversight:

  • leadership: defining a vision, making decisions, allocating resources, and advocating for the advancement of AI transformation
  • Steering: providing strategic supervision to ensure that AI initiatives align harmoniously with broader business strategies

Roles and responsibilities:

  • clearly outlining the tasks, timeliness, and collaborative efforts expected from all participants in AI projects

Risk management and compliance:

  • Risk management: identifying, evaluating, and effectively handling potential risks in AI projects, encompassing technical (e.g., system failures, security breaches), operational (e.g., data inaccuracies, skill shortages), and strategic risks (e.g., misalignment with business objectives)
  • Compliance: guaranteeing that AI systems conform to internal compliance requisites

AI foundation

  • Infrastructure: alignment IT and business units
  • Data management and data quality: establish robust processes, techniques, and tools for managing, organising, storing, and maintaining data 
  • Data quality: ensure set of values of data variables is accurate, complete, reliable, and relevant
  • IT processes: activities for designing, developing, delivering, and supporting IT services.
  • IT architecture: structure of the organisation's IT systems
  • Tool landscape: consists of software applications, platforms, and technologies in use

AI governance

Organisational and change management

  • Organisational: comprising values, behaviours, and a common vision that shapes the organisation's receptiveness to AI
  • Change: facilitating and aiding individuals in adapting to AI-driven modifications, including shifts in workflows, roles, and skill sets, which are pivotal for the effective integration of AI


  • Establishment of systems for performance tracking to assess factors such as model drift, value generation, and usability

AI training and awareness

  • Employee training and fostering acceptance to empower personnel in the utilisation of AI tools and guarantee user engagement
  • Appoint champions within the organisation to champion this initiative

AI-powered transformation of data-centric enterprises

Today, organisations are increasingly becoming AI-centric enterprises, guided by a holistic approach that transcends traditional silos and embraces change. This transformation revolves around democratising data, surmounting challenges like data discovery, and nurturing a data-centric culture. At its core, it places data in the spotlight, fostering a culture and mindset built on data-driven decision-making while ensuring universal data access and usability. These enterprises understand that their data strategy is inseparable from their business strategy, with those leveraging AI insights being 2.8 times more likely to achieve double-digit growth. Through AI, they gain the capability to analyse data at scale, unearth valuable patterns, and make predictive strides, driving innovation, efficiency, and competitiveness in an increasingly AI-infused world, extending their product optimisation and business model expansion into new market realms.

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