The success of your AI strategy depends on building these 5 competencies

The term “artificial intelligence” was first coined by James McCarthy in 1955. In less than 70 years since then, AI has gone from being a scientific concept to a fact of life. 

Let there be AI 

The undeniable transformative potential of AI is causing disruption measurable by the economic impact of trillions of dollars.  

In fact, AI will contribute $15.7 trillion yearly to the global economy by 2030; that’s more than the current output of China and India combined.  

To be honest, considering the current pace of AI adoption and billions being poured into cutting-edge research, the $15 trillion number looks conservative at best. 

The trillion(s) dollar game 

So, with trillions of dollars at stake, how can enterprises capitalize on AI? The instinctive response is to invest in their own AI initiatives, and that’s what companies started doing post-ChatGPT. In fact, Deloitte’s 2024 “State of GenAI” study found that the majority (67%) of companies are planning to or already ramping up their AI investments.  

Since billions of dollars are being poured into AI research, all’s well, right? Clearly, companies must be seeing ROI (or at least the promise of it) to not only continue their spending but to increase it as well.  

That does appear to be the case for most companies, but in July 2024, Gartner predicted that around 30% of AI projects would be abandoned by the end of 2025. They listed poor data quality, inadequate risk controls, escalating costs, or unclear business value as the reasons for this abandonment.  

In other words, there are trillions of dollars to be made. Companies are investing more in their AI initiatives. And finally, not everyone will succeed in their AI quest. If you believe Gartner, the failure rate will be 30%. That said, how can companies ensure their AI initiatives are a moderate-to-raging success? It starts with an AI strategy with a robust data foundation. 

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It all starts with the right AI strategy 

IBM defines “AI strategy” as the guide and roadmap for organizations to address the challenges associated with implementing AI, building necessary capabilities, and defining its objectives.  

In simpler terms, AI initiatives should align with the broader business goals to extract meaningful value from AI and maximize its impact.  

But there’s a catch. 

The right AI strategy is not just about AI. It’s about mastering a set of key competencies in the right combinations in the following five dimensions: data, AI, organizational strategy, culture, and talent. 

This means that your AI strategy should not only include foundational AI capabilities like cloud platforms, data platforms, architecture, and governance but also encompass C-suite buy-in, innovation culture, etc., to realize AI’s value.

1.   Organizational Strategy

For AI projects to be successful, they need to be more than projects. By championing AI as a strategic priority for the organization backed by the full support of leadership, companies can save AI initiatives from floundering.

When leaders make AI central to their organizational strategy, they empower teams to deploy AI solutions to solve problems, identify opportunities, and outperform their peers.

2.   Culture of Innovation

A culture of innovation within the organization is an important prerequisite for a successful AI strategy. Leadership should create and nurture this culture strategically and deliberately to serve as a vehicle for learning and experimentation across the board.

For the AI leaders within the organization, the goal should be to encourage end-to-end innovation by enabling structures and systems that help teams demonstrate their innovation experiments and seek constructive feedback.

3.   AI-Fluent Talent

Talent is a crucial part of the AI success equation, as organizations that invest heavily in talent are better positioned to maximize their AI investments. Investing in talent doesn’t necessarily mean looking outward. Instead, the goal should be to build AI literacy and proficiency across the workforce.

For instance, an Accenture study shows that 78% of companies seeing success with their AI have mandatory AI training sessions for C-suite execs and development engineers alike. Investing in talent also makes it easier to scale AI and human collaboration while ensuring that AI adoption doesn’t get siloed but permeates the organization.

4.   AI Core

Another key competency centers around developing an AI core by industrializing AI resources (tools and teams). This core should serve as a centralized operations platform to tap into the organization’s tech, data, and talent ecosystems, enabling it to strike a balance between execution and experimentation.

In simpler terms, the AI core would help with the productization of their AI applications. This, of course, will be powered by the cross-functional collaboration of data scientists, ML and systems engineers, and domain experts.

5.   Data Foundation

Just as, if not more important than building an AI core, is the organization’s data competency. Building higher competencies in data-related skills and increasing overall data fluency across domain teams is crucial for a successful AI strategy.

Deloitte reports that AI initiatives have led 75% of organizations to increase their technology investments in data lifecycle management.

This is because successful AI initiatives require making full use of internal and external data while also ensuring the data is trustworthy and has appropriate policies around it for usage, monitoring, and security purposes.

In fact, Accenture reports that 32% of AI-successful companies are likelier to work with a partner offering data solutions to extract value from their data effectively and quickly.

Recommended Read: Why You Should Use AI to Improve Data Quality | Astera

T-Minus AI: Time to build your successful AI strategy

To wrap up, building a successful AI strategy is about more than AI. It’s about building and rebuilding the right combinations of the five dimensions we have discussed to achieve AI maturity.

Even companies with the right strategy, culture, and talent can flounder without the necessary data foundations.

Making sure your data is AI-ready should be the foremost step on your AI journey. Astera can help with that, thanks to our experience working at the intersection of AI and data management.

As an AI-powered data solutions provider, we are uniquely equipped to help enterprises looking to ready their data for AI.

Connect today to learn how we can empower you.