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What is AI readiness?

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AI readiness: what does It mean to be AI-ready?

With the onset of gen AI and the machine learning revolution, everybody wants to talk about AI readiness, AI governance, AI maturity, and other AI-related buzzwords. However, when you research these terms, you get various answers and recommendations on where to start and how to adopt AI efficiently.

Words like gen AI, artificial intelligence (AI), and machine learning (ML) are used interchangeably even though they mean different things. Companies have all heard of the power and benefits behind these technologies (and are fiercely racing to adopt them). Still, they do not clearly understand what they need to perform to the best of their abilities – only 14% of companies worldwide report complete confidence in their AI projects.

That's why we prefer the term "AI readiness" when preparing for AI. The concept goes beyond the mainstream problem of "garbage in, garbage out." AI readiness encompasses all elements and best practices that companies should apply (i.e., data fit for purpose, business change management, identifying opportunities and threats, security, and governance of AI apps, etc.) to get the most out of their AI initiatives.

In this blog, we'll define AI readiness and explain why it's essential for every company. Let's get started!

What is AI readiness?

AI readiness defines the degree to which a company is prepared to adopt, integrate, and build value from AI initiatives. Beyond what technology you already have in place, AI readiness is a holistic approach to AI with a built-in path to successfully leverage artificial intelligence to achieve organizational goals.

At Ataccama, we believe it centers around three distinct pillars:

  • AI Business Strategy
  • AI governance
  • Data for AI

AI business strategy

This pillar emphasizes a clearly defined business strategy that enables you to embrace an AI transformation. As mentioned earlier, AI success isn't just a matter of technology. Without organizational buy-in and support, your projects could be dead on arrival.

AI literacy refers to the ability to understand, use, and critically evaluate artificial intelligence (AI) technologies. It involves a combination of knowledge, skills, and awareness related to AI concepts, applications, and implications.

Investing in education via "AI literacy" can garner the support you're looking for, inspire cultural change, get everyone up to speed on AI and best practices, and help you invest in the necessary talent to get things up and running. You need a shift in operations AND mindset to create change and set yourself up for real business value.

This all begins with business leadership. Without these initiatives coming from the top-down, you run the risk of AI only being used in silos for isolated purposes instead of creating positive benefits for the organization as a whole.

Your leadership teams must establish clear goals, lay out the framework for AI adoption, and develop visions for the future. As with every digital transformation, you can always open doors to new opportunities, reveal more efficient processes, and create new products or services. Ultimately, you could end up redefining the organization’s goals entirely.

You can incorporate these concepts into your AI readiness assessment plan and AI business strategy to get a better picture of what's to come and increase support and excitement throughout the process.

AI governance

"AI governance" is a popular topic this year, with several data management providers using it as a main selling point for their AI packages. However, we believe great AI governance, and sustainable AI in general, has to come from great data governance and management. Depending on how your company handles data governance, you can set the foundation for the responsible handling of AI with principles, guidelines, and standards.

Data governance is a foundational element of any AI project because it provides granular controls over data, leading to more transparency into data flows, policies and procedures, defined standards, and existing security measures. It also helps with metadata management, which allows you to understand the contents of the data, leading to a faster understanding and selection of what data to use for each model.

All of this is valuable for AI because you can translate this understanding and regulation into confidence for your models. If your data is well-tracked, regulated, and secure, you can infer that the models you build will follow suit.

This way, when it comes to watching for ethical and responsible AI practices, adhering to government regulatory requirements (such as the upcoming EU AI Act), and providing transparency into the models you build, you'll already have the groundwork in place to do so (from your data governance initiative).

These regulations and requirements for AI readiness differ depending on company, location, and industry, but there are a few commonalities that companies look for when building AI governance. Models must be:

  • Human-centric
  • Responsible
  • Governed and accountable
  • Safe and secure
  • Robust and resilient
  • Transparent and explainable

You can achieve these outcomes with an adept AI governance program, which is much easier to implement if you have already invested time and effort in your data governance initiative.

Data for AI

Reliable AI hinges on data that is not only high-quality and well-structured but also "fit-for-purpose." Data scientists, data engineers, and ML engineers must thoroughly understand the data's suitability for a specific AI model, including its inherent biases.

This meticulous assessment is crucial to avoid costly "garbage-in, garbage-out" scenarios, where flawed data leads to unreliable or even harmful AI outputs. Ensuring data is consistently high quality and fit-for-purpose enhances the sustainability and reliability of AI initiatives, ultimately driving long-term success.

Therefore, "Data for AI" is a crucial pillar of AI readiness, as it establishes a strong foundation of high-quality, reliable data upon which trustworthy and effective AI solutions are built. Companies know all this yet struggle with data volume, storage, cleansing, management, maintenance, and consumption.

To avoid inaccurate, inaccessible, or otherwise bad data, you need strong data governance (DG). Governance was mentioned above, but comprehensive DG also includes integrated data quality (DQ). It helps apply DQ controls, enhance data cleansing and enrichment, and prevent timely issues so your apps and downstream efforts aren't impacted. These processes must be automatic; if you do them manually, you won't be able to scale.

Data must evolve to meet AI needs. You need to set the foundations with “traditional” data management, which includes profiling, rules, cleansing, matching and merging, workflows, and so on. Once that's in place, you have to focus on data labeling, mitigating data bias, and data enrichment.

Why is AI readiness important?

AI is at an inflection point, transitioning from assisting businesses to becoming a foundational technology. The cost of inaction will be high. Waiting to see what others will do means lagging behind, and catching up will be extremely difficult.

AI is moving forward fast, so early adopters will have an advantage in unlocking new use cases, business opportunities, revenue streams, and improving operational efficiency. On the other hand, companies with a low AI readiness assessment will find themselves stuck in the implementation stage while their competitors reap the benefits.

Adopting AI is not an overnight process. AI readiness is not a destination but a journey. It requires continuous effort, strategic planning, and a deep understanding of how AI can bring value to an organization. By putting effort into it sooner, you won't just have better models but a leg up on all your competitors who are too patient and don't have the same sense of urgency.

Everyone knows that this is the time to start caring about AI, but if you're not sure why, here are some numbers that might convince you:

  • Having data ready for AI drives greater business outcomes by 20%.
  • By 2026, 95% of workers will routinely use AI.
  • The first AI regulation, the EU AI Act, will have its first policies come into effect in late 2024 or early 2025, and it will fully come into force in the next two years.

AI governance vs. AI readiness

Since "AI governance" is the more popular term when it comes to preparing for an AI initiative, we wanted to make the distinction between the two as clear as possible:

AI readiness

AI readiness focuses mainly on an organization’s level of preparedness to adopt and integrate AI effectively. It is an umbrella concept that encompasses a wide range of factors, such as strategic alignment, organizational capabilities, technology, and data.

AI governance

AI governance is a framework encompassing the lifecycle of AI systems, from design to operation. It involves technical, legal, and ethical considerations, with the latter addressing the broader societal impacts of AI. AI governance aims to create policies and guidelines to manage AI risks and maximize benefits, focusing on transparency, accountability, fairness, and overall societal impact.

Let us help you become AI ready

In a rapidly evolving AI landscape, achieving a high AI readiness assessment is not just an advantage but a necessity. It involves a comprehensive approach encompassing AI literacy, robust data management, and effective governance. By prioritizing these pillars, organizations can unlock AI's full potential, driving innovation, efficiency, and growth.

Ataccama empowers organizations to achieve AI readiness through comprehensive data management solutions. Our expertise spans data quality, governance, and preparation, ensuring that your AI initiatives are built on a foundation of trustworthy and reliable data.

Don't let data challenges hold back your AI ambitions. Contact our team today to learn how we can help you unlock the full potential of AI.

Written by David Gregory

David is our head of content creation at Ataccama. He's passionate about all things data, cutting through the mundane "new oil" narratives to extract real-world value from this indispensable resource.

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