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In AI transformation keep an eye on your rear-view mirror

Navigating AI adoption challenges in enterprises – learning from the past to shape the future

31 January 2024Jörn Kellerman

Discovering transformative AI potential for enterprises

AI adoption is no longer a buzzword but rather a strategic imperative. Across industries, organisations see AI’s potential for efficiency, decision-making, and growth. What sets AI apart is its all-encompassing nature, unlike previous trends. It automates tasks and drives data-driven insights. Successful adoption means cultural change, promoting AI literacy, ethics, and strong data governance. AI’s integration will reshape industries and spur innovation.

Counter-intuitive strategies – mitigating AI barriers for enterprises

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Accelerating AI adoption in large enterprises can be challenging, but there are some counter-intuitive strategies that can help overcome common barriers.

  • Start small, think big: Instead of launching massive AI initiatives, begin with small, manageable projects that demonstrate quick wins and ROI. Scaling up can happen gradually as the organisation gains confidence in AI’s capabilities. The key question here is, “What is small?” A good example is what one of our large automotive customers did: when they wanted to deploy AI in their manufacturing they started with one product, one factory, and then harnessed what they learned for global roll-outs in other geographies. 
  • Cross-disciplinary teams: Create diverse teams that include not only data scientists and engineers but also domain experts from various departments. Collaboration between these teams can lead to more relevant and effective AI solutions. We recommend a use-case driven approach to create a cross-functional focus and our AI portfolio consists of precisely this – high potential use cases (industry, cross-functional and IT) and enabling services. 
  • Employee education and upskilling: Invest in continuous education and upskilling programs to empower existing employees to work with AI tools. This can be more cost-effective than hiring new talent. Research (McKinsey) shows that most organisations lack the skills to effectively use AI capabilities like those provided by Generative AI and reskilling is the only way forward. 
  • Customer-facing, AI-centric approach: Prioritise AI projects that directly benefit customers. Customer-facing AI applications, such as personalised recommendations or chatbots, can drive quicker adoption as they demonstrate tangible value. And as mentioned earlier we strongly recommend adopting a use-case driven approach, bringing cross functional teams together to score those early victories.
  • Flexible IT infrastructure: Invest in a flexible and scalable IT infrastructure that can adapt to changing AI needs. Cloud-based solutions can provide the agility required for AI experimentation and deployment. A good example is our own Telekom Data Science Platform, an end-to-end platform for data science and AI development/production, that can deployed on any cloud provider of your choice.
  • Regulatory compliance as an opportunity: Instead of viewing regulatory compliance as a burden, see it as an opportunity to enhance data privacy and security practices, which can ultimately bolster AI adoption. Our experience suggests that some commonly less focused use-cases, such as law monitoring, contract analysis, or risk assessment, provide the biggest take-aways.

Act now – learning from your implementations

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While a strategy of pausing and learning from one’s experience is invaluable in every walk of life; it is of particular importance while pursuing AI. AI is fast-evolving. We are just scratching the surface of the potential of Generative AI and Artificial General Intelligence (AGI) may be upon us in no time at all (OpenAI recently announced that AGI has been achieved internally). Here are few key learning points you should gather from ALL your AI transformation playbooks, implementation, trials and experiments.

  • Embrace failure and learning: Encourage a culture where failure is seen as an opportunity for learning and iteration. Not every AI project will succeed, but the insights gained from failures can lead to more successful implementations.
  • Open-source and collaboration: Consider open-source AI solutions and collaborative partnerships with AI communities and startups. Leveraging existing resources can save time and resources while promoting innovation.
  • Ethical AI governance: Establish ethical AI governance frameworks early in the adoption process. Ensuring transparency, fairness, and accountability can build trust both internally and externally.
  • Innovation contests: Hold internal AI innovation contests or hackathons to encourage employees to come up with creative AI solutions. Rewarding innovation can motivate employees to actively participate in AI adoption.
  • Proceed with caution: The possibilities with AI are exciting and path-breaking. But AI is also known to have its own shortfalls, especially with regard to content generated by Generative AI, which can be biased or totally wrong in some cases. So, it is recommended that you look back and exercise caution and/or have a human in the loop.

By embracing these counter-intuitive strategies and learning points, large enterprises can navigate the complexities of AI adoption more effectively and achieve the successful, sustainable integration of AI into their operations and strategies.

About the author
Jörn Kellermann

Jörn Kellerman

Senior Vice President of Global Portfolio and Technology Excellence, T-Systems International GmbH

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