Top five AI-Must-Knows for the Decision Maker
According to The CEO’s guide to generative AI1 from IBM, 64% of CEO’s say they face significant pressure from investors, creditors, and lenders to accelerate adoption of generative AI. It further states that making the right investments in this emerging tech could deliver a strategic advantage and generate growth and gained market shares. But also, that the wrong bets could open the door to data privacy concerns, legal liabilities, and a whole host of ethical issues.
In this article we share how to think when you as a decision maker are exploring how to best harness the technology in your business. We also suggest that you may want to check our article Trustworthy & Responsible AI is Everyone’s Business.
1. Start With the Problem You Want to Solve
Do you remember when all businesses wanted an app? And that too many started designing and building a solution without making sure that it would be relevant to the customer experience. Now we see that many businesses risk falling into the same trap with generative AI.
AI works best when it is applied to a well-defined problem with clear objectives, and choosing the right problem to solve is crucial for a successful outcome. That also ensures that your AI efforts align with organizational goals and priorities.
- Going shopping for technology before having defined the problem can lead to mismatched solutions, wasted resources, and inefficient outcomes, says Christian Pallinder, AI Lab General.
2. Work With Your Data
To maximize the potential of generative AI, decision makers should prioritize a comprehensive approach to data management. This involves not only collecting, storing, processing, and securing data but also ensuring its quality through cleansing, normalization, and standardization. Additionally, organizations should consider the ethical implications of data usage and implement practices to maintain transparency and accountability throughout the AI development process.
- When organization collect data, they often focus on traditional metrics and overlook valuable unstructured data sources, such as sentiment analysis and chat transcripts from customer service, says Christian.
3. Select the Right Model
Not all AI models are created equal. They have different strengths and weaknesses, so it's crucial to pick the one that fits your problem best. If you don't, you might end up with bad results, wasting resources, and spending more money than needed.
- Different AI models offer distinct advantages and drawbacks. Machine learning excels in pattern recognition, while deep learning handles complex data like images, says Christian.
4. Infrastructure is More Important Than You Think
Selecting the right infrastructure is important to prevent unnecessary spending and maximize your investment. Oversized equipment may be both costly and inefficient. For instance, powerful GPUs may offer lightning-fast responses, but are wasteful if not needed. Understanding your needs ensures efficient resource allocation, avoiding unnecessary expenses and wastage.
- Before investing in infrastructure, do the same research as you would when you plan a trip. Before selecting your mode of transportation learn what your destination is. Bigger is not always better. Sometimes, a simpler and less costly option like a bicycle is the best fit.
5. Pick the Low Hanging Fruit
Exploring the low-hanging fruits in generative AI unveils readily accessible opportunities for transformative innovation. These ripe prospects encompass areas such as automated content generation, personalized recommendation systems, and image synthesis. Below, you'll find illustrative examples of generative AI use cases, showcasing its practical applications across various industries and domains.
Automated Grading: AI algorithms can assess and grade student assignments and tests, saving teachers time and providing students with immediate feedback to aid in their learning progress.
Medical Imaging Analysis: AI algorithms can analyze medical images such as X-rays, MRIs, and CT scans to detect anomalies and assist radiologists in diagnosing conditions like cancer and fractures.
Predictive Policing: AI algorithms can analyze historical crime data to identify high-risk areas and predict where crimes are likely to occur, allowing law enforcement agencies to allocate resources more effectively and deter criminal activity.
Fraud Detection: AI algorithms can analyze financial transactions and patterns to detect fraudulent activities such as credit card fraud, identity theft, or money laundering, helping financial institutions mitigate risks and protect customers.
Personalized Product Recommendations: AI algorithms can analyze customer browsing and purchase history to generate personalized product recommendations, enhancing the shopping experience and increasing sales conversions.
[1] https://www.ibm.com/thought-leadership/institute-business-value/en-us/report/ceo-generative-ai
Christian Pallinder
AI Lab General
Arrow
More articles
Can we trust AI?
AI is a powerful technology that we can use to analyze data, optimize processes, and generate personalized content. But as they say: with great power comes great responsibility, and trustworthy and responsible use of AI is something we all must take seriously. Allow the generative AI model to assist you but make sure to maintain human oversight and decision-making authority.
Use AI to Solve Real Problems
Cost cutting is often at the top of the list of desired outcomes of an AI implementation. But a better way to achieve a successful application of the technology is to identify and address real business problems. Companies that can monetize their organizational data will gain nearly double the ROI from their AI capabilities than their peers.
Test AI in the lab
AI has turned a lot that we knew about business development on its head, and we’re all trying to orient ourselves in this paradigm shift. Do you want to level up your knowledge and be an IT-partner who can guide your customers on their AI journey? Arrow welcomes you to join them in the AI lab to learn and explore
Understand the value of AI. Try it out in our lab
Sponsor
About Arrow
Arrow ECS is a technology enablement company that delivers innovative IT solutions to solve complex business challenges in the market. We offer value-added distribution, business consulting and services to leading technology manufacturers and their channel partners.
Följ oss
 |
 |
 |
 |
© 2026 Arrow Electronics.