10 Things Projects Managers Should Know Before Starting an AI Project

10 Things Projects Managers Should Know Before Starting an AI Project

Cover photo

AI and machine learning technologies are transforming the way organizations streamline their business processes and operations. Across industries, companies are realizing the benefits of AI technology like data-driven outcomes, automated processes, and faster implementation.
A recent TechRepublic survey found that 90% of organizations are currently working on AI projects. However, only one in three AI projects is proving to be successful. To leverage maximum benefits, project managers must plan their strategy before executing their AI project.
Here are 10 things for project managers to consider before starting their next AI project.

10 Things to Consider in Any AI Project

1. Ensure high-quality data.

High-quality data is the most important resource for the successful implementation of your AI project. Data is integral to making your AI model effective for your business case.

Ensure that in your AI project planning, you set aside sufficient time and resources to obtain good-quality data.

2. Check if AI is Feasible for your Project Requirements.

Before choosing AI technology for your next project, you must ensure that AI is the plausible solution for your business problem. With the help of a qualified AI consulting partner, you can determine if your AI project is a feasible solution to a specific business challenge.

3. Quantify the Output of the AI Project.

What should be the expected outcome of a successful AI project? Without clearly defined expectations, it is difficult for project managers to quantify the output of their AI projects.

To deal with this situation, always treat the first version of your AI implementation as as base case to judge if the quality of the solution is good enough to solve the initial problem. If the initial accuracy is much lower than the human average, then that means you have issues with the quantity or quality of data, or the methodology you are using for your classification scheme needs to be adjusted to what can be expressed in the data.

Once you establish a base case, and identify what solutions are viable given the initial accuracy numbers, use the later versions and iterations to improve its business value and ROI.

4. Define the Problem to be Solved

Many project managers and companies implement AI projects without a clear understanding of the business problem or objective they are trying to address. The overall "hype" about AI technology makes it seem that it can address every type of problem. In reality, it does not work that way.

Define the business problem – and find out if it is too complex to be solved by any AI-based solution.

5. Ensure all Project Stakeholders have the same Understanding of AI

Based on their previous experience and learning, stakeholders including project managers, team leads, and developers have a different understanding of AI technology.

An AI solution is typically limited by the data available for training and the expertise to make solutions work with the data that you have. Often there is no code or available data to recreate the latest research. Because of this AI is not a solution that you buy and forget, it is a process and takes constant inputs and stakeholders need to reset their expectations from other software solutions.

Before implementing any AI project, develop a consistent approach towards implementing the AI solution.

6. Define the Scope of the AI Project

Despite its numerous benefits, AI projects are just like any other IT and development projects, and typically have a project deadline and budget. Based on your allocated resources, define the overall scope of your AI project – or what you plan to achieve. Map out a timeline for gathering and cleaning the data to power the project and identify data needs that could hold up a project and who will be responsible for delivering such data.

Evaluate the scope of your project for its costs, available resources, and return on investment.

7. Choose the Correct Data

Before selecting the right AI algorithm, project teams must select, clean and filter training data to match the task at hand. By preparing and gathering the right data sets for model training, project engineers can accelerate future AI development and experiments. For proper training, project management teams require skilled experts who are proficient in Data Science to understand what is significant in the data and results, and programming languages like Python, but understanding how to use Java does help with building some client side applications that can run on an individual machine.

8. Choose the Correct AI Algorithm

Be it for supervised or unsupervised learning, project managers must choose the right AI algorithm for their projects. The important consideration is that depending on the project requirements, there are different types of AI algorithms for each task including for classification tasks, entity recognition, recommendation, generative text, generative visual content, clustering, anomaly detection, and random forest to choose from.

9. Ensure Data Security

Like any software solution, an AI-based solution must also fulfill all security-related requirements. As a project manager, you must take appropriate measures to protect any AI system from potential attackers. Additionally, make sure you comply with data security regulations when working on an AI development project.

10. Customize your AI Solution

No AI-based software is a “one-size-fits-all” type of solution, mostly because the data you are working with and solutions that are unique to your business and industry require niche data or personalized-to-your-company data. Solutions need to be customized according to unique business requirements. If your organization is working with a technology partner, check if they can deliver customized AI solutions and how their models can be tuned to your data..

In addition to customization, build your AI product to easily integrate with third-party systems your organization uses.

Conclusion

Just like other technologies, AI is not a “silver bullet” that can solve business problems on its own.Project managers must approach an AI differently than other IT projects by starting with what the data shows with your base case solution. Among other considerations, project management teams must clearly define the business problem that they are trying to address using AI technology.

Let’s Discuss Your Idea

    Related Posts

    • top 10 quotes langchain ceo on ai

      Harrison Chase is the co-founder and CEO of LangChain, an open-source framework that enables developers to easily build applications powered by large language models (LLMs). Chase launched LangChain in October 2022 while working at the machine learning startup Robust

      LLMs / NLP
    • Langchain top 10 tools

      LangChain has emerged as a game-changing platform that empowers developers and enterprises to create sophisticated large language model applications. By providing a unified framework for integrating various AI tools, LangChain simplifies the process of building intelligent agents that can

      LLMs / NLP
    • Langchain enterprise ai

      For today's businesses and entrepreneurs, there is an absolute necessity to leverage large language models (LLMs) for enterprise AI applications. These powerful models, trained on vast amounts of data, have the potential to transform how businesses operate and engage

      LLMs / NLP

    Ready To Supercharge Your Business

    LET’S
    TALK
    en_USEnglish