Hier erfahren Sie, wie Sie Ihre Problemlösungsfähigkeiten als IT-Strategieexperte mithilfe von Technologie verbessern können.

Leverage AI for Analysis: Use AI algorithms to quickly process data and identify patterns. Enhance Decision-Making: Employ AI to predict outcomes and inform strategies. Automate Tasks: Allow AI tools to handle routine processes, freeing your focus for complex problems. Partner with AI: Integrate AI as a strategic asset in problem-solving. Example: Imagine using AI to analyze consumer behavior data. This can rapidly provide insights into trends and preferences, allowing for more targeted marketing strategies Conclusion: Embracing AI transforms your approach to problem-solving in IT Strategy, enhancing both efficiency and effectiveness, and ultimately positioning you at the forefront of technological advancement.

1. Utilize Problem-solving Tools: Explore software like mind mapping, decision trees, or problem-solving apps. 2. Data Analysis: Leverage data analytics tools to identify patterns, trends, and insights for effective problem-solving. 3. Collaborative Platforms: Use online collaboration tools to brainstorm solutions with team members and stakeholders. 4. Automation: Implement automation tools to streamline repetitive tasks and focus on complex problem-solving. 5. Continuous Learning: Stay updated on emerging technologies and methodologies through online courses, webinars, and workshops.

AI can be the best tool as far as the employee training part is concerned in pharmaceuticals. Hope it may face more challenges in Pharma mfg

AI components help to analyse and validate theories. For example, intelligent process mining can be used to 𝐦𝐞𝐚𝐬𝐮𝐫𝐞 𝐭𝐡𝐫𝐨𝐮𝐠𝐡𝐩𝐮𝐭 𝐭𝐢𝐦𝐞𝐬 𝐚𝐧𝐝 𝐝𝐞𝐟𝐢𝐧𝐞 𝐭𝐚𝐫𝐠𝐞𝐭 𝐩𝐫𝐨𝐜𝐞𝐬𝐬𝐞𝐬 based on the results. Alternatively, process mining can be used as an exploratory approach to uncover bottlenecks and inefficiencies. 𝐅𝐫𝐨𝐦 𝐨𝐮𝐫 𝐨𝐰𝐧 𝐞𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞: process analysis is an efficient means of conducting business engineering. However, the 𝐫𝐞𝐬𝐩𝐞𝐜𝐭𝐢𝐯𝐞 𝐝𝐞𝐩𝐚𝐫𝐭𝐦𝐞𝐧𝐭 𝐢𝐬 𝐚𝐥𝐰𝐚𝐲𝐬 𝐚𝐟𝐟𝐞𝐜𝐭𝐞𝐝 𝐚𝐧𝐝 𝐬𝐡𝐨𝐮𝐥𝐝 𝐛𝐞 𝐢𝐧𝐯𝐨𝐥𝐯𝐞𝐝 𝐢𝐧 𝐭𝐡𝐞 𝐚𝐧𝐚𝐥𝐲𝐬𝐢𝐬. In reality, there are often rational factors that have led to the current state of the process.

Strategic issues are inherently complex and carry a significant weight on an organization's trajectory, leaving no margin for error. Thus, factual evidence is crucial for resolution. Data analytics emerges as a formidable ally in the realm of strategic problem-solving, providing insights that foster sound decision-making. Through the examination of extensive data sets, hidden patterns and trends emerge, crafting more robust strategies. This methodical approach guarantees that choices are substantiated by solid evidence rather than mere hunches, cementing its vital role in the modern era of data reliance.

I would like to introduce the eternally annoying subjective "data quality" at this point. 𝐀𝐧𝐚𝐥𝐲𝐬𝐞𝐬 𝐬𝐭𝐚𝐧𝐝 𝐚𝐧𝐝 𝐟𝐚𝐥𝐥 𝐰𝐢𝐭𝐡 𝐭𝐡𝐞 𝐮𝐧𝐝𝐞𝐫𝐥𝐲𝐢𝐧𝐠 𝐝𝐚𝐭𝐚 𝐪𝐮𝐚𝐥𝐢𝐭𝐲. However, this can also be measured and ensured by tools (with and without AI support). I recently reported on 𝐩𝐚𝐫𝐭𝐧𝐞𝐫 𝐝𝐚𝐭𝐚 𝐦𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 on my profile: This is part of Master Data Mgmt. and goes too far for this article. What is important, however, is that 𝐝𝐚𝐭𝐚 𝐪𝐮𝐚𝐥𝐢𝐭𝐲 𝐬𝐡𝐨𝐮𝐥𝐝 𝐛𝐞 𝐜𝐨𝐧𝐭𝐢𝐧𝐮𝐨𝐮𝐬𝐥𝐲 𝐦𝐞𝐚𝐬𝐮𝐫𝐞𝐝 𝐨𝐧 𝐭𝐡𝐞 𝐛𝐚𝐬𝐢𝐬 𝐨𝐟 𝐝𝐞𝐟𝐢𝐧𝐞𝐝 𝐪𝐮𝐚𝐥𝐢𝐭𝐲 𝐫𝐮𝐥𝐞𝐬. Especially in processes with a high degree of automation, this is the only way to ensure sustainable output quality.

Though I will use AI methodology I will not implement AI here because it's not cost-effective. I will just train my brain to think in terms of artificial intelligence. What I was doing before was based on maximum human abilities. raw data => observe => learn => mental model =>make decision. AI Concept Raw Data => Collect Data => Math Model => make decision What I will use is a combination. Human Abilities + AI Concepts Raw Data => observe => Collect Data => observe => Math Model => learn => mental model => make Decision.

Data analytics empowers IT Strategy professionals by providing insights into performance metrics, trends, and patterns within organizational data. Leveraging data analytics enables informed decision-making, identification of opportunities for optimization, forecasting future needs, and designing more effective strategies to align IT with business objectives.

𝐌𝐚𝐧𝐲 𝐜𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐢𝐯𝐞 𝐩𝐥𝐚𝐭𝐟𝐨𝐫𝐦𝐬 𝐟𝐚𝐢𝐥. I myself played a key role in the development of EnTranCe, a social network for experts, and have often received this feedback when I have told people around me about it. 𝐖𝐡𝐲 𝐝𝐨 𝐭𝐡𝐞𝐲 𝐟𝐚𝐢𝐥? Motivating members to participate in contributions and knowledge sharing is incredibly difficult. This is where 𝐠𝐚𝐦𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 can help with company-external tools, while company-internal 𝐛𝐚𝐝𝐠𝐞𝐬 𝐚𝐧𝐝 𝐩𝐞𝐫𝐜𝐞𝐩𝐭𝐢𝐨𝐧 𝐨𝐟 𝐜𝐨𝐥𝐥𝐞𝐚𝐠𝐮𝐞𝐬 𝐚𝐧𝐝 𝐬𝐮𝐩𝐞𝐫𝐢𝐨𝐫𝐬 can act as an incentive. However, if members who actively participate are not recruited at an early stage, existing members will gradually withdraw and the project will fail.

Collaborative platforms act as catalysts for enhancing problem-solving abilities by fostering an environment where individuals can leverage diverse skill sets and experiences. Through real-time collaboration and feedback mechanisms, team members can iteratively refine ideas and solutions, leading to more robust outcomes.

BPM (Business Process Mgmt.) tools such as ARIS, Signavio and Co. as well as EAM (Enterprise Architecture Mgmt.) tools such as Alfabet, LeanIX or ADOit enable the 𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 𝐨𝐟 𝐛𝐥𝐮𝐞𝐩𝐫𝐢𝐧𝐭𝐬 𝐭𝐡𝐚𝐭 𝐜𝐨𝐦𝐛𝐢𝐧𝐞 𝐭𝐚𝐫𝐠𝐞𝐭 𝐩𝐫𝐨𝐜𝐞𝐬𝐬𝐞𝐬 𝐰𝐢𝐭𝐡 𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧 𝐚𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞. 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬: Planning of target processes and derivation of business functions that support individual process steps. 𝐈𝐓: Derivation of data objects and target applications that provide the necessary business functions, including technology planning. This allows 𝐭𝐚𝐫𝐠𝐞𝐭 𝐩𝐫𝐨𝐜𝐞𝐬𝐬𝐞𝐬 𝐭𝐨 𝐛𝐞 𝐬𝐢𝐦𝐮𝐥𝐚𝐭𝐞𝐝 𝐚𝐧𝐝 𝐚𝐧𝐚𝐥𝐲𝐬𝐞𝐝 𝐟𝐨𝐫 𝐞𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲 before they are used productively.