Seven things CIOs and software buyers should know about artificial intelligence
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This document outlines key considerations for CIOs and software buyers regarding artificial intelligence (AI) and machine learning (ML) applications. It emphasizes the need for a proper understanding of AI, the importance of accurate training data, and the value of a centralized AI infrastructure for scalability and efficiency in business operations. Furthermore, it highlights the limitations of traditional automation without AI and stresses the necessity of intelligent data management for leveraging big data effectively.
Seven things CIOs and software buyers should know about artificial intelligence
- 1. before making any decisions. Seven things CIOs and Software Buyers should know about Artificial Intelligence
- 2. Artificial Intelligence AI and ML applications keep improving rapidly. This sector is extremely dynamic and it's sometimes hard to keep up with innovation. Here are seven things everybody needs to know before making any decisions regarding AI applications of any kind.
- 3. Artificial Intelligence is most of the times just a marketing gimmick 01 AI is becoming pretty much a buzzword. And since a lot of people out there still struggle to properly define the boundaries of this field, companies oversell the capabilities of their platforms by throwing in trendy terms as "AI" and "Machine Learning".
- 4. A system that doesn't learn over time is not an AI application. A system that makes decisions based on data (either collected by the system itself or entered in the system manually) and adapts its features to this information has nothing to do with AI. Conditional functions and responses rely on intrinsic rules. And any rule-based system is per definition not AI. Systems that leverage AI learn autonomously over time, require a specific training infrastructure, and adopt specific ML algorithms that govern the way the system learns. Complex, unstructured, and unpredictable applications profit from AI because there's a direct ROI from spending time training the system. AI is a marathon not a sprint solution!
- 5. There are different ways to evaluate AI: And most vendors won't tell you the truth 02 When it comes to AI applications, many vendors and buyers rely on the F-score but Intelligent systems are developed in stages to increase accuracy throughout the entire process. Only focusing on a single value for a single stage that doesn't take into account post- processing and quality assurance is a myopic way to evaluate a system.
- 6. Accuracy needs to grow over time It's totally okay if an AI applications doesn't spit the right results 98% of the times right out of the box. AI is a long-term investment. And the better the system is the faster it'll learn. Check how fast a system can improve its accuracy, not how accurate it allegedly is right out of the box. You need to measure the overall score of the entire chain after validation and verification and see how potential post- quality gate HITL (human in the loop) stages are designed to increase quality. When comparing different ai solutions, check the overall level of accuracy of the entire process.
- 7. When testing: Don't compare apples and oranges 03 When comparing different solutions, it's not smart to assign the same tasks to two completely different systems with different approaches. If you compare performance of a rule-based system with an AI-driven system right out of the box, the AI system will probably loose.
- 8. Carry our tests based on the capabilities of the systems you compare and your actual needs If you want to compare different solutions, don't stop at the F-score (see previous point) and don't assign trivial tasks to actual AI systems. And most importantly: don't judge an AI system that hasn't gone through a PROPER training process. If you need an AI-driven system, it doesn't matter how poorly it performs at the beginning compared to a rule-based system. Think long-term!
- 9. It's your responsibility to provide the right training data When you deal with AI vendors, you'll be requested to provide real samples of data that can be used to train the system. If your data is either too good or irrelevant the system will never learn how to process information. 04
- 10. If you train your system with bad or unrealistically perfect data you'll never reach a satisfactory level of accuracy. Provide training data that reflect the reality. If the training data is too perfect the system will learn how to deal with best-case scenarios but won't be able to cope with variance. At the same time, don't throw in random data that can't even be interpreted by the system as this will affect its training speed.
- 11. AI transformation is not about one-trick ponies 05 If you plan to implement an intelligent AI infrastructure that truly transforms business operations across the board, scan the market for a centralized training environment that would allow you to process data systematically for every process in your company based on the task you want to speed up. Don't settle for scattered single use case AI-driven API solutions.
- 12. Having access to a modular system that can tackle multiple use cases, will immediately pay off as you won't have to manage hundreds of providers for single micro tasks. This will reduce both costs and maintenance efforts while guaranteeing that you have full control over the models you're using so that each model is 100% tailored to your own needs. An AI system needs to adapt to your processes. you shouldn't adapt your workflows to accommodate an intelligent solution. Invest in scalability and modularity to maximize your ROI.
- 13. Process Automation without AI makes little to no sense 06 Process Automation applications might able to carry out thousands of processes a minute, but if you need to manually feed data into such systems, the result is that you can only leverage a tiny portion of their capabilities.
- 14. Intelligent Document Processing (IDP) Data extraction technologies and Intelligent Document Processing Systems automatically extract and process information to feed Robotic Process Automation (RPA), Business Process Automation (BPA), ERP, or Input Management Systems to match the speed of data processing with automation. Combine process automation technology with centralized data classification and extraction to achieve the highest level of output performance.
- 15. Big Data without intelligent data management is just "Big Waste" of cloud space 07 70% of data contained in modern data lakes consists of completely unstructured data. This means that, no human, no rule-based system and no trivial data extraction technology can actually derive any meaningful insights from it.
- 16. Invest in a centralized AI infrastructure that allows you to make sense of any kind of unstructured data to gather the insights you need for a digital strategy. Investing in Big Data means leveraging the power of information to generate actionable insights, discover patterns and dependencies, categorize information for R&D, and increase customer intimacy with precious analytics. Digital transformation without data management is like planning the perfect racing strategy for a car without an engine. The strategy will only work on paper.
- 17. Thanks for your Attention! 2021 ExB Group Intelligent Document Processing https://exb.de LEARN MORE