You're integrating AI technologies into your digital strategy. How do you ensure expectations are met?

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.

Setting clear goals for any project keeps your team aligned and focused on what truly matters to your business. These goals shouldn't just be about numbers – consider how AI can enhance daily operations or customer experiences. Be specific, but stay flexible. The tech world moves fast, so regularly review and adjust your targets. This approach ensures you're not just implementing AI for the sake of it, but using it to drive real value. By balancing measurable outcomes with broader objectives, you transform AI from a buzzword into a powerful tool for growth. Remember, effective goal-setting also helps manage expectations and provides a framework for evaluating success, making it easier to demonstrate AI's impact to stakeholders.

ChatGPT doesn't get your job done. And hardly any other LLM does. AI is not a way for creative copies/work. Using AI tools to analyze trends and automatize your workflows is more than enough.

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.

The below 7 steps will help you set the right foundation: 1. Identify Specific Business Needs 2. Evaluate Data Requirements 3. Pilot and Test 4. Consider User Experience with said AI technology 5. Look at the AI tool Vendor’s Track Record 6. Plan for seamless Integration with existing workflows 7. Long-term Vision where the tool has potential to grow with your business By considering these factors, you can make an informed decision that minimizes disruptions, enhances efficiency, and sets a strong foundation for your AI-enhanced digital strategy.

Selecting the right AI technologies is crucial for meeting your expectations. Assess the market and understand the strengths and limitations of different AI tools. Ensure that the chosen technology aligns with your goals and integrates smoothly with your existing systems. It's important to consider scalability, compatibility, and the level of support available. Making an informed choice will minimize disruptions and set a strong foundation for your AI-enhanced digital strategy.

Arguably, if you are integrating any technologies into your digital strategy, data needs to be an important consideration and even more so with AI. The need for robust data management and data governance practices is certainly not new but as we move towards operationalizing more AI solutions, the urgency needs to increase on making the required foundational investments. On the bright side, AI can also be an ally in making some of those foundational implementations more streamlined than they were in the past.

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.

In Switzerland, for example, a member of senior management of a major international technology company, heavily invested into AI, said to me, that fewer than 1% of SMEs are prepared to harness the benefits of AI, primarily due to insufficient data quality and quantity. This underscores the necessity for a robust and meticulously crafted data strategy, positioning it as a top priority for companies aiming to engage in the AI landscape. To effectively integrate AI into your business, start by focusing on a strategic approach, evaluate your existing data to pinpoint gaps and areas for improvement. Pro tip: Do yourself a favour and get help! Hire a data science consultant or at least an analyst, if you don’t have one internally.

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.

The successful implementation of AI also hinges on your organization's capacity to adapt to and manage these technologies. Investing in training and resources to upskill your employees is crucial. Equipping your team with knowledge of AI principles will enable them to make more informed choices and utilize AI tools to their full potential. Fostering a culture that embraces continuous learning and experimentation is key. This proactive stance towards skill development can help guarantee that your workforce is well-prepared to tackle the challenges and capitalize on the opportunities presented by AI.

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.

Consistently monitoring the advancement of your AI projects is essential for guaranteeing they fulfill expectations. Define metrics and Key Performance Indicators (KPIs) that align with your objectives. Utilize these metrics to assess the performance of your AI systems, making necessary modifications along the way. Conducting regular reviews enables you to stay informed about any problems and confirm that your AI integration is progressing as planned, in harmony with your overall digital strategy.

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.

It doesn’t work without this. The feedback cycle and challenging the results are core to the process, ensuring your AI integration aligns with your digital strategy.

Establish a robust framework for monitoring and evaluating the performance of your AI initiatives. Use key performance indicators (KPIs) to track progress and measure success against your defined goals. Regularly review and refine your AI strategies based on feedback and performance data to ensure continuous improvement.

Remember to revisit your KPIs periodically. Don’t stay tied to the same old metrics if they are not still demonstrably tied to a valuable objective. Another reason: while strategies often change from the top, cascading down to impact the tiers of goals and KPIs below, strategy is also informed bottoms-up from the insights of KPIs at all levels, so ensure they are the right ones.

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.

Integrating AI can lead to significant changes in workflows and processes. Proactively manage these changes by communicating clearly with your team, addressing concerns, and providing the necessary support during the transition. Change management strategies can help mitigate resistance and foster a positive attitude towards AI adoption.

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.

Fundamentally, if AI is implemented merely for the hype sake - it will be soon abandoned as the hype curve slopes down, undermining trust in this technology. Hence, it is essential to manage realistic expectations with the stakeholders - going beyond hype and right to the point of where AI could actually drive the business success.