AI applications are increasingly sought after by organizations and agencies, but many companies aren’t sure they actually need to invest in AI.
Experts in this area believe one of the first steps for a business is to define clear objectives and metrics. The potential implementation process can be determined by setting specific goals for what hopes to be achieved with AI, along with key performance indicators (KPIs).
It’s also wise to start slow with AI. Launching a pilot project before rolling out AI solutions across the board is often a good idea. In other words, test the technology first, assess its impact and make adjustments with minimal investments.
Some companies also prefer to invest in data cleaning – the process of preparing data for analysis by identifying and correcting errors, inconsistencies, inaccuracies, duplicates or incomplete information within a dataset.
This is important because given that all AI systems rely heavily on data, making sure that the data quality is excellent improves an AI investment.
If AI makes financial sense, there should be a use case that is compatible with just about every company. Some of the most popular uses for AI technology include:
- Personalized recommendation engines in ecommerce
- Chatbots and virtual agents
- In the financial industry AI fraud detection systems are useful
- Disease detection through image recognition
- Predictive models for public sector planning
Want to learn more? Tonex offers Artificial Intelligence: Business Strategies and Decisions, a 2-day course where participants learn the fundamental concepts of artificial intelligence and its applications in business.
Those in attendance also analyze real-world case studies to assess the impact of AI on various industries as well as develop proficiency in evaluating AI technologies and their potential for strategic decision-making.
Additionally, Tonex offers more than six dozen courses in Artificial Intelligence and Machine Learning where participants learn comprehend and master ideas on machine learning concepts, key principles, techniques including: supervised and unsupervised learning, mathematical and heuristic aspects, modeling to develop algorithms, prediction, linear regression, clustering, classification, and prediction.
Courses include topics such as:
Financial Engineering with AI Technologies
Generative AI Security: Principles and Practices
Ethical AI: Principles and Practices
Healthcare AI Systems Engineering Training
AI and Human Interaction Design
For more information, questions, comments, contact us.