5 Steps to Game-Changing AI Readiness in 2024

In this article I discuss how companies may prepare for implementing game changing #AI in their business. In a very general sense there are five steps to the process:

1. Preparing for AI readiness
2. Creating Industry Cloud Platforms
3. Adopting Platform Engineering
4. Adding Intelligence to Applications
5. Democratizing Generative AI

Step #1: Preparing for AI readiness

Adopting generative AI depends on the readiness of the organization intending to implement it. Such readiness may be divided into three categories:

• Organization. Your organization must set the principles and guidelines that drive the interaction between humans and systems. Understand what you’ll accept and what you won’t accept.
• Data. You must also make your data AI-ready and there are a variety of ways in which to do so some of which will be covered below. At a base level your data must be secure, enriched, accurate and governed.
• Security. For all its benefits, there will be bad actors putting that technology to negative use. Your organization should prepare for new attack vectors and carefully develop policies that ensure the security of both the organization and your customer base.

Organization

Every action you take with technology is oriented toward an outcome for your customers and your business.

The stronger you hold things such as customer centricity as a core value, the better decisions you’ll make. It will urge you to create better products, be more protective of customer data, and use AI not to needlessly disrupt their experience, but enhance your value.

You will also need people inside your organization who are able to operationalize these values.

Your organization will need personnel with the following competencies:

• User Research Experts
• AI Product Designer
• AI Product Manager
• AI / ML Engineers
• Data Scientists

AI-ready data

AI rises or falls depending on the data used to build and train it. To avoid poor performance, your data should be ready for consumption in AI models, machine learning, and LLMs. Making data AI-ready extends its lifecycle beyond that of traditional pipelines.

Because building an LLM prototype can be relatively inexpensive, it’s often attractive to build proofs of concept (POCs) for GenAI capabilities. However, building such models at an enterprise level, powered by AI-ready data, is a different story entirely.

To scale and productize generative AI, your data must be modeled to be usable and consumable. Targeted outcomes and considerations include improved accuracy, minimized API calls, and mitigation of hallucinations. This requires a concerted effort toward data labeling, enrichment, and bias mitigation.

The foundation of AI-readiness is democratized access to data prepared for consumption which can be accomplished by having a unified data store in the cloud, making your data available to your AI models. Essentially, you’re centralizing data, while balancing accessibility and security, preparing data for consumption, and ensuring the right velocity of data for product innovation, insights generation, and advanced analytics. Today's data architecture has evolved over time to meet the requirements of AI and augmented analysis.

Conversely, companies with siloed data, duplicate technology stacks, and gaps in capabilities along the data lifecycle may struggle to adopt generative AI for game-changing use cases.

Security

According to a Gartner study, by 2026, organizations that operationalize artificial intelligence (AI) transparency, trust and security will see their AI models achieve a 50% improvement in terms of adoption, business goals and user acceptance.

In addition to having the right personnel on your side, proper machine learning operations #MLOps processes can help ensure that data ingest, preparation, model training, tuning, deployment, and monitoring are all conducted according to the highest standards of security and safety.

Because AI models require regulatory scrutiny and regular drift-check, MLOps can ensure faster response to issues.

Step #2: Creating Industry Cloud Platforms (ICPs)

Industry cloud platforms #ICPs combine software #SaaS, platform #PaaS, and infrastructure as a service #IaaS capabilities to provide industry-specific solutions. ICPs offer industry players a more agile way to manage workloads and accelerate change in ways most responsive to their unique industry and business needs.

ICPs are the blueprints that enable organizations to move faster with a data fabric approach by democratizing access to AI-ready data for modeling and productization while delivering a library of packaged business capabilities (PBCs).

PBCs are the building blocks, ready made software components for your product innovation teams. PBCs provide you with a competitive advantage by making your core value-added services available for rapid software development so development teams don’t need to start from scratch. Ultimately, this composability helps your teams accelerate innovation while improving quality and consistency.

For generative AI specifically, a data fabric design pattern, democratizing access to AI-ready data, combined with PBCs empowers teams to develop AI-enabled products faster within a context relevant to your industry.

Step #3: Platform engineering

The vast majority of technological development conversations involve front-facing platforms that serve consumers and business users. Platform engineering serves developers and engineers by providing reusable tools and self-service capabilities.

Platform engineering can improve consistency, productivity, and the overall development experience. This year I expect the enterprise and large software engineering organizations to establish platform engineering teams as internal providers of reusable services, components and tools for application delivery.

While still somewhat nascent, Generative AI can already automate routine tasks such as managing merge and code changes, testing software, and managing security. Over time, AI should be able to provide more creative and unique engineering solutions, transforming the work processes of developers and engineers.

Step #4: Intelligent applications

Intelligent applications are those augmented with AI, infused with data from transactions and external sources. Rather than serve as static waystations for users and customers to perform core functionality, they learn from interactions and improve autonomous responses over time.

Most applications today are built on some form of rule-based, conditional logic preset by the developer or engineer. While these approaches can be highly sophisticated, at the end of the day they’re still predetermined and do not change without human input. Intelligent applications take a different approach, deploying machine learning to elicit the appropriate response across a broader range of circumstances, including those that have not been pre-programmed in advance.

Benefits to the user experience include:

• Adaptive experiences, like digital assistants and natural language interfaces, to discover, learn about, and respond to user needs
• Augmenting processes to drive dynamic business transformation
• Enabling a continuous connection between analytics and decision making

Step #5: Democratized generative AI

Because generative AI doesn’t require a deep bench of skill sets in order to operate, it has the potential to level the playing field in access to skills, innovation, and capabilities:

• Rapid ideation and faster time to market for new products
• Increased information and insight accessibility, enabling contextual search and transforming information retrieval
• Improved efficiency and increased business productivity by accelerating manual or repetitive tasks

The impact of democratized access to generative AI is substantial. If you can proactively transform your business model to respond to this changing landscape, understand the capabilities and limitations, and reimagine your products with game-changing innovation, you can exploit this rising tide to succeed and leave your competitors behind.

#digitaltransformation #platformengineering