Pointers for Success on Your First AI Project
Technology

Pointers for Success on Your First AI Project

The rise of AI is underway with AI being widely adopted across industries. According to the McKinsey Global Institute, 69% of data processing and 64% of data collection activities can be automated.

It’s easy to see why many companies are all too eager to launch their AI project or initiative, but enthusiasm does not guarantee success. Aside from the technicalities, there is the question of whether the AI project serves business needs and goals. Figuring this out requires some thought and analysis. Perhaps your company has an AI initiative in mind, but have you evaluated its likelihood of success?

Given the plethora of AI success stories across several industries, it is natural to want to harness this powerful technology. However, proceeding without some careful analysis and consideration can lead to a lot of wasted money and effort down the line. So here is some guidance for anyone at the starting line of their first AI project.

Determine Your Use Case

AI and ML can present you with many opportunities, but you must determine your ideal use case before launching your project. A clear concept of what your use case is will help you steer it without confusion. It will also help in the decision of whether the project is feasible and likely to succeed.

Some of the questions to consider are: Is it worth considering the use case? How will it benefit the business? Use cases may range from automation of document processing, intelligent support for helpdesks or many other potential scenarios where Intelligent Process Automation (IPA) supplements a Business Process.

Once you have determined your use case, use our pointers to evaluate your AI project.

Pointers for Consideration

There are many points to consider when evaluating whether your AI project is a good idea or not. Positive responses to all the questions are a good indication for success and set the foundation for starting the project. If most of your answers are negative, then it probably isn’t a good idea.  After going through these pointers, you are ready to start forming your team.

  1. Relevant Tasks: How many decisions are you automating? Why are you automating these tasks? Are they worth automating? Are they too many?
  2. Realistic Expectations: Can you accept occasional pitfalls with your system? Or would any error be critical and how will it be handled?
  3. Production Feasibility: Can your automation scale in production without intensive engineering support? The answer to this impacts the likelihood of success and retaining your sanity once the project is up and running.
  4. Availability of Adequate Data: Is there pre-existing data that can be used for training? Can you access such data and inputs, if they exist?
  5. Sufficient Examples: Are there enough similar examples? What type of output are you expecting?
  6. Computational Power: Will you have access to adequate processing power to tackle the size of your datasets? Keep in mind that with cloud technology, the most likely answer to this question is yes.
  7. Cross-functional Team: Do you have access to resources who can be assembled into a team with all the required skillsets to get a complete process view?
  8. Data Quality: Do you trust the data quality? This is crucial for training and the success of your AI project.

With its growing popularity, every industry is looking forward to harnessing the power of AI/ML. Unfortunately, many forget that AI and ML are not a magic solution to every problem, use case and business goal. It’s better to be analyse your project before you get started. With our pointers to guide you, you’ll be able to decide whether to ahead with your AI project. If most of the answers to questions for consideration are negative, you’ll be able to avoid unsuccessful projects and wasted funds.

If you would like to explore how implementing AI can help your business, the RAP AI team would be glad to help you. Our next-gen, AI-powered content intelligence platform RAPFlow automates unstructured content processing and AI orchestration. It can be used in tandem with our RPA solution RAPBot for end-to-end workflow automation, and it can be deployed in just week. Our platform also has specific applications and you can build your own use case that easily integrates with your existing systems. Please book a demo to explore our solutions.

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