There are generally three parts to an Artificial Intelligence approach: generating data, translating that data and determining what to do with that data. An AI team may also require a minimum of three separate roles for completion; possibly a data engineer to organize all information, a data scientist to investigate all information and a software engineer to implement all applications.
It may be difficult finding the right researchers, data scientists and software engineers with experience in building AI-enabled software to hire. Embracing aggressive recruiting is one route, but new hires to build a team don’t always work out. This is something that requires an assessment of business needs and ROI. You can also start from the bottom-up with development by training a team of internal engineers on the new paradigm.
What Makes Integrating Artificial Intelligence So Difficult?
All AI products are driven by tons of valid data. Without solid data practices within an organization, your data almost becomes pointless. Organizations that succeed with the utilization of AI are skilled at strategically acquiring their data. For successful AI integration, you will need to:
- Identify data sources
- Build data pipelines
- Filter and prepare data
- Identify threats and changes
- Measure your results
Understanding where data comes from and how to get more is a key factor in any artificial intelligence algorithm. Merely having data is not enough. Take a skeptical look at how data is presently being used as a part of creating your AI strategy.
Training Applications
All AI projects need to be trained (running the application on a collection of known data and allowing it to create a model) before they can do any useful work. With the power of machine learning, this allows automatic tweaking of internal limits until satisfactory results are met on your test data. Following with running the algorithm on a different set of test data that it has not seen, to be confident that the results are adequate.
Training work in junctions with writing and debugging the software; more effort may be put into training than any other part of the development process, so be sure to account for training time in planning an AI project.
We need to constantly be open to new ideas and approaches, such as artificial intelligence (AI), and be willing to challenge assumptions.”
- Marc Benioff, CEO of Salesforce
Can Training Go Wrong?
An AI application will memorize training data. If results show one-hundred percent accuracy on training data, it may perform poorly on real-world data. This is a never-ending issue known as over-fitting. Alternatively, if your application doesn’t perform one hundred percent accurate on training data, it will never result in one hundred percent accuracy with real-world data.
Be that as it may, few applications need more accuracy than others; ninety-nine percent for a self-driving car is not good enough, but sixty percent accuracy is acceptable for an application built to recommend products to users.
Nonetheless, training is not a one-time deal. Business conditions are constantly changing, as well as products, customers and business environment. Application performance will slowly degrade over time, so re-training time in any AI strategy is necessary.
Where Do You Start?
What exactly are you looking to transform in your business? Want to enhance your internal processes? Looking to improve customer and employee experience? Are there individual features in products that would benefit from AI? Are you brainstorming product design? Maybe there are specific tasks that are tedious or error-prone but can make employees more efficient with a little help from AI.
Artificial Intelligence is a tool that can be a solution to many business problems, but to succeed, an AI strategy should be a key factor in an overall business plan. No matter if you’re improving a current business or building a new one, AI should serve your business.
