How can you keep your AI project within budget?
Define your scope and goals
We expect high-tech projects to be grand, but they require the unconventional approach needed for smart budgeting.
Surprised?
Let me explain.
AI software prices vary a lot, with subscription and consumption-based models being common. To budget well, leaders should ask basic questions about how the software is licensed and delivered (on-premises or SaaS) and what metrics are used for pricing. If the pricing involves things like API calls or consumption, it's crucial to understand how they define and measure these metrics.
Customized billing options, like a mix of fixed and consumption charges, can also be explored. These questions help leaders grasp the pricing details, ensuring resources are used wisely according to project goals.
Choose the right tools and platforms
Reports suggest between 50% and 85% of AI projects face challenges due to unsuitable tools, leading to delays, cost overruns, or even abandonment. Choosing the wrong tools for an AI project can literally cost you an arm and a leg, both in wasted resources and delayed progress.
Therefore, consider both interoperability and long-term viability. Interoperability ensures seamless integration with existing systems, minimizing challenges and avoiding costs and delays.
Select tools that work well together to ensure efficiency. Simultaneously, assessing the long-term viability of chosen tools is essential. This involves evaluating if the technologies have active developer communities and ongoing support and are not likely to become obsolete soon.
Manage your data and resources
Investing in your team's skills and knowledge is comparable to managing a championship-level sports team. Think of your team members as star players, each contributing unique strengths essential for winning the game, which, in this case, is a successful AI project.
Just as a sports team needs regular training to stay competitive, your team benefits from continuous learning about the latest advancements and best practices in AI.
It's akin to ensuring your players have the latest strategies and techniques to outperform the competition. Additionally, encouraging a collaborative team spirit is like building camaraderie among teammates, encouraging effective communication and synergy. This positions the AI project as flexible governance.
Test and validate your results
As Albert Einstein wisely said, "A theory is when you know everything about something & nothing about everything else." While your AI project may be bursting with potential, ensuring its success requires testing and validation to prevent resource black holes. Here are five tips:
Early and Often: Embrace the iterative approach. Start simple, test assumptions, & refine your model rapidly.
Data is King: Invest in high-quality, relevant data from the beginning. Clean, labeled data fuels & precise models.
Metrics Matter: Define clear, measurable KPIs. Are you aiming for increased customer engagement? Improved fraud detection?
Embrace Automation: Utilize automation tools to streamline data analysis, model evaluation, & reporting.
Deploy and maintain your solution
Ready-made AI brains vs. building your own from scratch – which side of the efficiency equation are you on?
For an effective AI project deployment, opt for ready-made platforms like Machine Learning as a Service, simplifying the process and enhancing the overall cost efficiency.
These platforms streamline Artificial Intelligence, providing cloud-based advanced analytics with support for various languages and algorithms. During the deployment phase, prioritizing scalability planning from the project's inception is crucial.
Foresee potential increases in data volume and user demand to ensure seamless scaling without requiring significant reengineering. Also, Incorporate automated monitoring tools to identify anomalies and issues proactively.
