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

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 organization 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 executed – when they wanted to deploy AI in their manufacturing they started with one product, one factory and then harnessed the learnings 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 bring together 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 organizations 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: Prioritize AI projects that directly benefit customers. Customer-facing AI applications, such as personalized 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, bring 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 like law monitoring, contract analysis, risk assessment provide the biggest take aways.