"It's a common situation. Many organizations find themselves stuck in the early stages of AI and experimenting with different models and pilots. Still, they never quite scale them to full production," says Christos Goumenos with Radiant Digital.
If this sounds all too familiar to your business, now's the time to take your AI to the next level and develop a more strategic approach. To help get you started, consider these 7 steps:
Step 1: Assemble a diverse and expert AI team
Successful AI implementation and expansion throughout your company will demand a variety of technical expertise and business insight professionals. While data scientists and engineers build models, domain experts ensure these solutions address real-world challenges. Just as important to this process are change management professionals. These individuals can guide the cultural shifts necessary for AI's company-wide acceptance. Communication and collaboration skills among all teammates will enable seamless cooperation between technical teams and business leaders.
Step 2: Prioritize data quality
We all know data quality is the lifeblood of everything AI. While it's tempting to create a vast number of datasets, quantity without relevance will lead to poor outcomes. That's why it's important to concentrate on prioritizing collecting clean, accurate, and relevant data. This will ensure AI models perform effectively and scale smoothly.
Step 3: Develop a culture of reusable code
Efficiency in the development stages will speed up AI deployment. Your company should leverage reusable code instead of starting from scratch each time. This will help develop some standardization and reduce development time associated with new models. It can also lead to fewer errors and more consistency across applications.
Step 4: Avoid the quicksand
While it's important to experiment, having handfuls of endless pilots without clear outcomes will slow progress and leave your company stuck in quicksand. Businesses must continuously evaluate tests and pilot results, take advantage of successful projects, and discontinue those that don't deliver the results you want.
Step 5: Integrate components
AI models, algorithms, and datasets must integrate seamlessly to create a cohesive system. This will require secure, reliable data flows. When all these are put together, the outcome can be far greater and more successful for your business.
Step 6: Manage costs
Cost management is a critical component of sustainable AI scaling. While building AI models might represent a portion of total expenses, hidden data storage, computing power, infrastructure, and maintenance costs can quickly make things cost-prohibitive. Cloud-based solutions and open-source tools offer scalable, cost-effective alternatives. Tracking and monitoring these expenses will ensure your AI initiatives remain financially feasible.
Step 7: Streamline the tech
Make sure you streamline all the advanced AI tech. Every new tool out there could result in a complex "tech stack" that could hinder your company's scalability. Be sure to prioritize the AI tools that will align more closely with your business needs and eliminate redundancies. You should also consider leveraging flexible cloud services to create a more streamlined, adaptable AI environment that will work within your business' demands.
Scaling AI is not a one-time and done process. It is a continuous journey that will require strategic vision, cross-functional team collaboration, and pinpoint execution. By following these key steps, your business will be on its way toward scaling AI to your business' advantage.
Looking for strategic insight into scaling AI in your business? Reach out to Christos Goumenos at Radiant Digital now.
