Organizations attempting to realize the tremendous value and potential of AI often struggle with technical, organizational, and cultural roadblocks. Going back to basics can help get machine learning solutions off the drawing board and into the business.
In a recent executive roundtable that I moderated along with Andy Walter, Lingaro’s strategic advisor, we presented a framework for addressing common challenges in implementing AI and machine learning (ML) projects. We also shared how we went “back to basics” to structure the company’s MLOps practice. The executives then shared feedback based on their experiences on driving AI and ML projects in their own organizations.
AI projects are not just about technology
In a survey of over 3,000 company managers and executives, only 10% reported significant financial benefits from their investments in AI. Only half of AI projects move from pilot to production, and those that do take an average of nine months to be launched. Based on our own experience, the pain points behind these woes are derived from:
Vague business case. AI is perceived as a strategic differentiator, but adoption is stymied by a lack of investment and buy-in.
Long delays. Choosing the technologies and building the teams around them take significant time and resources.
Poor data quality and integration. Without properly curated data, AI initiatives only add costs, miss deadlines, and increase regulatory risks.
These challenges are often set off by a bottom-up, technology-centric mindset. Decision-makers should instead define a strategic objective and bring together the technologies and capabilities necessary to achieve it.
Go back to basics to move forward with AI
To move forward with AI, organizations need go back to fundamentals and orchestrate them around a business strategy:
Understand your organization’s data and analytics maturity. Gauging the organization’s data and analytics maturity helps set realistic objectives that can be achieved within an acceptable timeframe.
Eliminate silos between DevOps-inspired practices. Organizations are now increasingly applying DevOps to the world of data and analytics. This led to offshoots: artificial intelligence for IT operations (AIOps), DataOps, MLOps, AI model operationalization (aka ModelOps), and PlatformOps — collectively known as XOps. Throughout our experience, we’ve seen how easy it is for enterprises to fall for the hype, causing silos to emerge around these XOps-inspired practices.
A sample illustration of interdependent elements required to operationalize AI and ML projects, where best XOps practices, talent, and technologies are brought together toward achievable objectives
Organizations that learn, adapt, and mobilize quickly and think beyond the technology will be more equipped to take their AI into production. Learn more about our insights through our white paper, “Back to Basics: A Blueprint for Operationalizing AI and Machine Learning,” which explores:
Common roadblocks to implementing AI and machine learning projects.
A back-to-basics framework for developing AI and machine learning projects, which involve understanding the organization’s data and analytics maturity and eliminating silos in XOps practices.
How Lingaro successfully built its MLOps practice.
A real-life case study on how a company markedly improved its financial performance by using machine learning.
Key takeaways from the executive roundtable and the lessons we’ve learned from working with global companies.