Global investment in AI is predicted to reach $200 billion by 2025. But as companies race to integrate AI – often at a high cost – many struggle to estimate and demonstrate the business value. It is crucial that your project goals and objectives match your organization’s strategic direction. When you are ready to kickstart your AI project, following these steps will help you get it off the ground and maintain its momentum.
To ensure your project’s success, it is important to have a clear understanding of the problem you are trying to solve. This involves understanding the specific business needs and setting measurable goals. As with any software project, It is important to clarify the scope and deliverables and make sure that stakeholders are aligned with a shared understanding of the project goals, objectives and expectations. Methodically assessing AI readiness and data-driven initiatives will help you prioritize the right projects. Be selective with your AI initiatives. Prioritize use cases where there is a legitimate foundation: clean, representative data (and lots of it) and a confirmed proof of value. Don’t try to reinvent the wheel, don’t over-promise and make sure to manage unrealistic expectations fueled by the ongoing hype around generative AI.
It’s not uncommon to experiment with AI before fully implementing an AI use-case in a project. Businesses tend to focus their experimentation on small incubation units and innovation labs with trained data scientists. However, with AI potentially disrupting all areas within a business, those initiatives should not be confined to certain organizational units or spaces. So when forming the team for your AI project, make sure to include domain experts, engineers, and business leaders from all relevant areas, alongside data and AI professionals. A cross-functional team is crucial to ensure that the developed solution will align with business objectives, is technologically feasible with manageable effort and actually solves a problem.
No matter whether you plan to build your project with foundation models or train your own, your AI will be only as strong as the data that you feed it. Rather than assuming AI is a default fixer, make sure your data is accurate and up-to-date. Develop a robust data strategy that covers data collection, cleaning and governance. Simple algorithms with relevant high-quality data will outperform the most sophisticated AI models with poor input data. Address ethical considerations and ensure compliance with relevant regulations. This includes managing data privacy and avoiding biases in your data.
Be realistic about what you are trying to achieve. It is one thing to build a demo that “kind of works” and another to build a stable AI application that provides the desired return on invest. Define clear evaluation metrics for your AI project as early as possible. Your standards should be high. Your AI application can have the most beautiful UI and the most optimized scalable architecture but still fail if your AI model only works 90% of the time. Make sure you have a realistic plan to take your AI project from a promising POC to providing tangible business value. Not having this assurance will result in wasted resources and investment.
Select appropriate AI technologies and tools that fit your project’s requirements. This includes machine learning platforms, data warehouses, analytics tools and cloud services. Decide whether your use-case requires you to do additional research and build custom ML models or if you can rely on cloud services and pre-trained models.
If you train your own models, integrate MLOps practices to ensure reliability and a scalable deployment of your models. Facilitate continuous integration and delivery, use MLOps frameworks for model tracking and develop strategies for monitoring model performance.
You may find that you have a strong use case and foundation for AI, but don’t know where to start. You might not have the technical expertise – or not enough of it – within your organization to successfully get going with a new AI project. Or, you might be looking to work with teams who are mature enough in AI technology that they can support you in building beyond prototypes. In these cases, bringing in a partner like intive makes sense.
We support you building end-to-end AI applications that focus on what AI can do for you now with current state-of-the-art technology. Our deep industry experience and world-class software engineering capabilities allow us to identify challenges and uncover opportunities throughout your operations, and help prioritize initiatives with significant impact. We support you choosing the right AI models, machine learning algorithms, and tools that fit your technical requirements and business objectives. Combining our AI knowledge and ability to create action plans for AI projects with your domain expertise, you can realize your AI project faster and with greater efficacy.
Speak with intive to discover how we can collaborate on your next AI project.
Sources:
AI investment forecast to approach $200 billion globally by 2025, Goldman Sachs
The Billion-Dollar Price Tag of Building AI, Time
The Pillars of a Successful AI Strategy, Gartner