What is AI Readiness?

Simply procuring AI technology is not sufficient; you also need to become AI-ready. This article explains the key elements of AI readiness.

Over the past few years, from a business user perspective, AI has gone from a well-known but little-understood backend technology to one with multiple dimensions that can be consumed company-wide. Yet, a degree of caution is required.

One BCG survey found that 45% of leaders lacked guidance or restrictions on AI and GenAI use at work. Before jumping in at the deep end and implementing AI use cases, you must become AI-ready.

AI readiness is about ensuring that your company has all the elements in place to enable AI across your business. Many organizations are propelling AI initiatives, but without being AI-ready, there's a strong chance of failure. This can be an expensive gamble.

Three Key Elements of AI Readiness

To ensure your organization has the most likely chance of success, you must follow a framework for AI readiness that asks three critical questions: why, who, and how.

Why?  

Organizations need to understand why they are choosing to adopt AI technologies. They must have clear goals and objectives that drive their AI ambitions and a universal interpretation of why AI is an important addition to their business. These drivers fall under three categories: operational changes, productivity gains, and strategic advantage. 

Operational changes

For some organizations, AI can disrupt an underperforming operating model. For example, in a law firm, AI can read, interpret, and summarize legal documents, dramatically reducing the time this would take to complete manually. 

Productivity gains

Other organizations can use AI for internal productivity improvements. For example, an insurance company may want to use the technology to expedite the underwriting process. Or, a manufacturer with IoT devices monitoring machines' health could use AI to scale up and use this data to predict failures. 

Strategic advantage

Some organizations can use AI for strategic gains. For example, a bank may have a strategy to grow its customer spend by developing an AI system that accurately targets financial products. 

Who?

Once you are done articulating a clear why, you need to think about building a strong team to execute the AI roadmap.

However, this is not as simple as reconfiguring your workforce. You should focus on two key areas: workforce readiness and change management.

Workforce readiness

You’ll need a workforce that has received training not only in the technical specifications of your AI tools but also in the ethical and regulatory considerations that they bring with them. Furthermore, they’ll need to interpret, analyze, and operationalize the data that AI tools have unearthed.

It’s not as straightforward as simply acquiring tools. You need employees with the technical skills to operate them. At the top of the list is data literacy. Without understanding where your company data is, how to access it, what it means, and what it represents, your workforce lacks the basic knowledge required to run AI technologies. It would be like giving someone a car without telling them the fuel it runs on or how to fill the gas tank.

Training should involve continuous upskilling and reskilling. AI is constantly evolving and you need a skills development program that evolves with it to see the greatest benefits.

Change management

When developing a change management strategy for adapting your company's organizational structure to the introduction of AI, you can’t underestimate the importance of managing the cultural shift these changes will bring. To mitigate the impact of AI on company culture, you must ensure that these cultural processes are prepped for rebooting.

Change management requires a top-down approach. You need to have the C-suite on board and actively promote the use of AI in everyday business tasks. Beyond this, from an organizational standpoint, you must look at your company's core business goals and values and ensure that AI aligns with and accentuates these principles.

Once you establish a culture in your company in which employees consistently adapt to and even seek out AI capabilities, you can consider your change management plan a success.

How?

This encompasses three key aspects: IT infrastructure, data quality, and governance.

IT Infrastructure

First, you must have the right technology and platforms to support your AI ambitions. Along with onboarding AI platforms, you must also consider other hardware and software solutions that support interoperability. You need a cohesive AI strategy, but this doesn't always translate into the functionality of your technology stack. That's why developing a scalable system that can operate concurrently is so important.

Beyond this, you need robust and capable infrastructure to process the sheer volume of data required for AI workflows. This concerns companies that have yet to migrate to the cloud.

Data

However, your AI infrastructure investments will be misplaced and will not achieve their potential if you do not have high-quality data. That's why, to ensure high-quality AI, you need AI-ready (high-quality) data. Only then can you roll out AI initiatives company-wide. These data quality improvement initiatives should form part of your overall data strategy for seamless and sustainable AI adoption.

Related Post: AI Needs Domain Knowledge to Boost Data Quality

Governance

Managing the data needed for AI requires stringent governance. Companies must ensure that AI delivery and implementation are well-governed, compliant, and secure. This requires various policies and mechanisms that provide transparency at every implementation stage, deployment, and processes to avoid bias and maintain privacy.

Governance is an ongoing process, so companies must conduct regular, comprehensive reviews of how AI initiatives are funded and how this investment is monitored. Beyond this, controls must be in place to ensure that AI is delivered on schedule and in line with other business processes. Crucially, your governance efforts must be driven by a determination to implement transparent, responsible, and ethical AI. To achieve this, you’ll need to establish an ethical AI framework that includes continuous monitoring of your AI output.

Related Post: Data Governance: What, Why, Who & How. A practical guide with examples

AI consumes enormous data, so a manual approach to data quality improvement won’t cut it. Instead, you need to deploy a data quality improvement tool. The best tools will enable you to consistently measure, assess, and evaluate the quality of your data and provide you with actionable workflows to fix any data quality issues.

Measuring the Progress of AI Readiness

While AI readiness can be divided into three key elements, these aren’t the only factors to consider. You also need to monitor your progress.

To that end, there are three stages of AI readiness, and every company's goal is to reach the transformational stage. Here, AI is fully integrated and facilitates major business changes.

• Foundational: At this basic level, companies will be evaluating whether the infrastructure and interfaces they have in place is capable of handling AI technologies. They will need to look at network bandwidth, cloud resources, data sources, and the relevant software packages required to execute their AI strategy.
• Operational: With clear management and governance, companies can develop sustainable AI initiatives. Companies at this level will be looking at the agility of delivery mechanisms, workforce readiness, cybersecurity, and governance, which includes compliance and other associated risks.
• Transformational: At this level of AI readiness, companies can maximize their investment and output. The business case for AI is clear for every employee in the organization, and regular business users are adopting AI technologies to support their job-specific ambitions. Leading the charge are business executives who have adopted the technology and approved budgets to fund more AI initiatives.

Conclusion

AI will transform the world. It’s already happening. Different organizations will use AI for different outcomes. Yet, irrespective of the desired outcome, every organization must cover each of the three elements we've written about in this blog to become AI-ready.

As mentioned, data is one of the core elements of AI readiness. Our next blog will walk you through the essential steps of making your data AI-ready.

Related Post: 4 Steps to AI-Ready Data