Sie integrieren KI-Technologien in Ihre digitale Strategie. Wie stellen Sie sicher, dass die Erwartungen erfüllt werden?

Customers. Users. Citizens. Clients. Whatever you call them, understand who your company serves and how you help them. Everything else should flow from that. There's not much else in the way of truly universal advice, in my experience!

Integrating AI into digital strategy requires clear objectives, robust data, and ongoing performance monitoring. We can ensure expectations are met by aligning AI initiatives with business goals, leveraging quality data, and continuously refining models based on real-time feedback. This iterative approach guarantees AI delivers tangible value and drives meaningful outcome.

There are various types and applications of AI. For instance, Gen AI creates new content, whereas machine learning analyzes data to make predictions. It's important to understand what you're trying to accomplish (e.g., business productivity, analysis, consumer personalization), with measureable goals, in order to choose the most suitable solution. This way you have a clear understanding of the problem you're trying to solve and a hypothesis to test and measure with AI.

Start by understanding the scope of your project. Determine if you will use third-party AI apps, leverage APIs to build your own solutions, or create an AI platform from scratch. If your budget and skills are limited, focus on the first two options. Visualize the workflow of your digital strategy and identify where AI tools can be integrated. Compare the features and pricing of different AI tools. Look for tools that offer the necessary functionalities to meet the objectives of the digital strategy while staying within the budget. If they do not meet your expectations, consider building a custom solution using APIs. Set realistic expectations about what AI technologies can deliver and how they will impact your digital strategy.

First and foremost, it’s important to ensure you are involving stakeholders and communicating clearly across the organization. Include how and why you are integrating Ai into your digital strategy and include high-level decision makers in the discussion, goal setting and workshops from the start. It will all go much more smoothly if you have buy in and even better if you can cultivate curiosity, insight and involvement across groups. This will help you with ongoing change management through out creating, communicating and implementing the strategy.

AI holds immense potential, but choosing the wrong tool can derail your entire strategy. Do your research by: - Don't be overwhelmed by options! Research different AI tools and understand their strengths and limitations. - Ensure the chosen technology directly addresses your specific goals. Don't get sidetracked by shiny features that don't solve your core problems. We need to think beyond the tech & investigate the level of support offered by the AI provider. Implementation and ongoing maintenance are crucial for success.

Choosing the right AI is a crucial decision. Before implementing multiple AI applications, hire some interns to test those applications. Employee time is very valuable.

Selecting what works best for you and your business goals can be a challenge. The first step is to set the objectives clearly. Ask the relevant questions so that you are able to build a detailed prompt. In my field of work, where we are exploring ways of creating an impact by delivering the relevant messaging to our TG it becomes crucial to customise content and target it to various cohorts. Perchance is a free tool for generating images through text input and I find it quite useful for generating visual static content. There are many other tools but chat gpt and ask qx are a few of my favourite ones as they have a good UX and help resolve tasks efficiently.

To choose the right AI solutions: Understand your needs: Clearly define the problems you want AI to solve. Research solutions: Explore AI tools and platforms that align with your goals and industry. Consider cost and complexity: Balance your budget and technical resources when evaluating options. Assess scalability: Choose solutions that can grow with your business. Evaluate vendors: Research vendor reputation and support to ensure a successful implementation.

Most companies don't have the data infrastructure and quality to even build basic automated reports and dashboards. If that's the case, AI won't save you - in fact, it may amplify the problem, leading to completely wrong conclusions. Focus on the foundations first!

Data is the key to unlock the magic of AI! 🪄 ✨ The quantity and quality of data that you feed in, will enable the tool to generate better outcomes. Timely validation of data will help provide authentic results. Best practices of data collection, storage, classification and analysis need to be implemented. This will ensure that you are taking decisions backed by authentic data and past performance.

When I talk to companies that want to do AI projects, the first thing I ask them is "Do you trust the quality of your data?" Normally, 90% of them say no. To tackle an AI project, it is essential to have clean and well-structured data, and a clear data governance strategy and integrated knowledge governance.

Using AI tools is part of almost every effort today. It's important to understand that "garbage in, garbage out" applies! We need quality data to train these tools effectively. We shouldn't leave everything to AI alone. That's why human oversight is crucial to ensure the data is accurate and relevant, interpret AI-generated insights correctly, and make strategic decisions. By combining AI tools with human expertise, we can achieve more accurate and effective outcomes.

The basic notion and dependency of data has to be managed. Many incumbents have data sets which are unconsummable but one should not reject the possibility of moving towards synthetic data. Now this synthetic data should be treated as Recycling and Refurbishing of the available data to be fed into learning models. Also working with encrypted data is to be considered besides the availability of data, ensuring privacy and non-repudiation of data fed into models and algorithms.

As AI unlocks several avenues for Marketers to enhance media and marketing strategies, creative design and deployment, it becomes crucial to be well adept with tools that help increase productivity and enhance efficiency. Understanding generative AI tools, training oneself on prompt engineering, will add immense value to your career. Select and prioritise which tools work best for your requirement as there are several options to select from.

Overall we can see 3 different markets: In the EU we have to consider testing more AI models in different Real applications and use cases. In the US I see a trend of already tested a lot of AI applications, here the market shows already which use cases are important for customers and others which are sorted out by negative product market fit. In China, a trend is visible for a lot of AI use cases for entertainment, especially in the automotive and mobility industries. Here you can see for instance way more demand for interaction with the car via social media. A good example is here that many Asian CEOs are way more present in social media to push sales. Here more and more AI use cases are in tests with mainstream customers.

To ensure successful AI integration, the top advice for upskilling your team is to establish a continuous education and professional development planning. Create a structured training program that covers fundamental AI concepts and industry-specific applications. Partner with educational institutions or online platforms for courses and certifications. Implement regular workshops, seminars, and hackathons to encourage hands-on learning and innovation. Additionally, set up a mentorship system where experienced professionals guide less experienced colleagues. By fostering a culture of continuous learning and experimentation, you can ensure your team remains adept at leveraging AI tools and ready to tackle emerging challenges and opportunities.

Overall we can see 3 different markets: In the EU we have to consider testing more AI models in different Real applications and use cases. In the US I see a trend of already tested a lot of AI applications, here the market shows already which use cases are important for customers and others which are sorted out by negative product market fit. In China, a trend is visible for a lot of AI use cases for entertainment, especially in the automotive and mobility industries. Here you can see for instance way more demand for interaction with the car via social media. A good example is here that many Asian CEOs are way more present in social media to push sales. Here more and more AI use cases are in tests with mainstream customers.

To ensure AI initiatives' success, regular monitoring is essential. Define metrics and KPIs like accuracy, error rates, response times, and resource utilization. Use tools like Tableau, Power BI, or Grafana for dashboards, ELK Stack or Splunk for logging, and Prometheus for alerts. Conduct weekly or monthly reviews, model audits, and gather user feedback to identify improvements. Perform continuous testing with A/B tests and cross-validation, and monitor for biases. Integrate with project management tools like JIRA or Trello, and align OKRs with KPIs. This comprehensive approach ensures AI systems meet expectations and provide continuous value.

While adopting AI in an enterprise, there are challenges and resistance for change. AI technology will create a change in the process, automate the existing process, move the systems to cloud, change the paper based processes, come up with kiosk based process etc., Most of the time through interviews, you can identify the pain points and the areas where resistance is high. The goal is to identify the least resistance path and address the pain points specific to that path. The company stakeholders start seeing the value and participate in the change initiative. As we move along the ascending order of the resistance paths, you see that the eagerness to learn the AI technology increases and decrease in the challenges.

Start with pilot programs to test AI technologies on a smaller scale before full implementation. This allows you to evaluate their effectiveness and make necessary adjustments. For example, in my digital marketing company, we piloted an AI-driven customer segmentation tool on a single campaign. By analyzing the results and gathering feedback, we fine-tuned the tool and strategies before rolling it out across all campaigns. This approach helps identify potential issues early, ensuring that the AI technologies meet expectations and deliver the desired outcomes when fully integrated.

To succeed in managing change in the AI-era you must have a comprehensive change management strategy focused on communication and involvement. Begin by clearly communicating the benefits and implications of AI to your team, ensuring they understand how these technologies will improve processes and workflows. Involve employees in the integration process by seeking their input and addressing their concerns. Provide ample support and training to ease the adoption of new technologies. Establish a feedback loop to continuously monitor and address issues as they arise. This approach will help maximize the benefits of AI, ensuring your digital strategy evolves positively and your team remains engaged and adaptable.

Bei der Integration von KI-Technologien muss geprüft werden, dass alle Schritte den geltenden Datenschutzgesetzen und ethischen Standards entsprechen. Das bedeutet, dass die Datenverarbeitung und Nutzung transparent gestaltet werden müssen, um das Vertrauen der Nutzer zu gewinnen und rechtliche Vorgaben einzuhalten. Genauso wichtig ist es, die Infrastruktur bereitzustellen. Die Systeme müssen in der Lage sein, die KI-Technologien effektiv zu unterstützen, wie z.B ausreichender Rechenleistung, Speicherkapazitäten und Netzwerkinfrastrukturen.

Additionally to TUBEO, consider change management, scalability, performance monitoring, data quality, interdisciplinary collaboration, continuous learning, vendor selection, risk management, user training, and having a long-term vision. This holistic approach ensures AI projects are well-integrated, valuable, and sustainable.

You should also carefully consider whether and how you want to use embedded AI, API calls or platform. You’ll almost certainly use a combination thereof, each with its own tradeoffs and benefits, but you should be working towards creating strategic differentiation for your business. You’ll need to find the right balance of speed to implementation and internal enablement. Remember, it’s still very early days and you need to be constantly ready for change and adaptation, there is still incredible opportunity, for those who are willing and to learn and adapt.

Innovation and Adaptability: AI keeps us at the forefront of technological advancements, allowing us to innovate continuously and adapt to changing market conditions. This agility is crucial for staying competitive and achieving long-term success.