Understanding the Layers of an AI Strategy

It seems as if every company on planet Earth has an AI transformation project in the planning or pilot stage while some have even achieved production status. The bad news is that every industry statistic is reporting:

• Gartner Group: “only 41% of AI projects make it from pilot to production”
• Boston Consulting Group: “50% of enterprises report stalled or abandoned AI initiatives”
• VentureBeat: “87% of AI models will never reach production”

The harsh reality that most forget is that a successful AI strategy is built on many layers where each layer is built on the last. Unfortunately, many organizations get mired at a specific level or try to skip levels with disastrous results. Understanding the nature of this stack (what is included, where it exists in relation to other requirements, and what is its value to AI transformation) versus how it is being attempted today versus what is possible with a no-code solution.

Understanding the AI stack

Understanding the layers of AI transformation matters because it turns AI from a costly experiment into a practical business tool. When the foundation is not aligned—from data and computing systems to the way people use AI—the results can be inaccurate, hard to scale, and difficult for employees to trust or adopt.

From bottom to top the layers to a successful AI stack includes:

• Infrastructure: Cloud infrastructure, including hyperscaler services, container orchestration
• Data: Data source connectors, cloud data store, data pipelines, lineage
• GenAI: LLMs, text to SQL generation, RAG, threaded conversations
• Predictive Models: No-code solutions, data wrangling, data masters, predictive modeling
• Reinforcement Learning: Use of predictive models and data to optimize decisions, decision agents 
• Agentic AI: Agent orchestration, agentic protocol support
• Explainable AI: GenAI interpretation of reports and predictive insights, text explanations, avatars
• Infrastructure: Today, organizations need specialized cloud engineers, dev ops, AI ops. Cloud technologies need to be managed

While this stack is easy to identify, and it is understood that “Infrastructure” is required before “Data” which in turn is required before “GenAI” all the way through “Explainable AI”, the effective implementation of each level, in succession, is a different matter.

Which layer are you spending the most time on right now?

Each layer of the AI stack has a unique set of requirements that today may be cost or resource prohibitive to the average organization. Here is a brief look at how organizations are approaching each level:

• Infrastructure: Today, organizations need specialized cloud engineers, dev ops, AI ops. Cloud technologies need to be managed.
• Data: Today, organizations need specialized data engineers. ETL tools need to be managed, and data pipelines need to be written to operationalize data flows.
• GenAI: Today, organizations need specialized prompt engineers and often LLM consultants to manage security. LLMs need to be vetted by use case.
• Predictive Models: Today, organizations need specialized data scientists who code in python. Notebooks and python packages, other modeling tools need to be managed, updated, and coordinated.
• Reinforcement Learning: Today, organizations need specialized and senior data scientists. Only the most advanced engineers build agents to optimize business outcomes.
• Agentic AI: Today, organizations need specialized frameworks and toolsets from various providers.
• Explainable AI: Today, organizations need specialized and skilled people who can explain data and insights to business leaders

The good news is that you won’t have to wait months or even years to realize the benefits of an AI transformation project or get stuck at one of the lower levels. New developments in open-source, zero-code SaaS platforms mean that legacy system modernization projects can be tackled with a data intelligence cloud that reduces dependencies on proprietary systems and the costs of dedicated tools and resources.

Why get stuck when you can realize measurable ROI in days?

If you are a business leader interested in leveraging the power of AI to empower executives to make better decisions without the need for specialized tools and resources, then AI-driven transformation has never been easier with UBIX, which will allow you to breeze through all of these layers.

UBIX delivers on the promise of a zero-code, highly scalable and flexible cloud architecture that leverages to power of GenAI, Reinforcement Learning and Agentic AI to enhance its capabilities and transform data into usable information accessible by the average person starts with ensuring you have the right data to the right person at the right time in the right format.

The UBIX Data Intelligence Cloud for AI transforms fragmented financial and market data into actionable, decision-ready insight. UBIX is a turnkey, self-service, cloud-native AI platform that enables business analysts and IT teams to build predictive analytics and automated reporting in weeks, without data scientists. By leveraging existing systems, UBIX delivers trusted intelligence that improves data and analytics, reduces costs, quickly surfaces what matters, and tests future scenarios before decisions become costly - all with a zero-code, self-service AI platform.

Learning how GenAI and emerging advancements like Reinforcement Learning and Agentic AI can deliver on the promise of a data intelligence cloud to derisk AI transformation has never been easier. Download our free eBook titled “Agentic AI and the Power of Action Agents” to help better understand the nuances of emerging AI concepts and technologies and offer a set of best practices for consideration to ensure digital transformation and business-led AI success. Or if you can spare 22 minutes for a mini–AI Readiness Workshop, you can contact one of our AI experts today.