What is the key to achieving high levels of data + AI Maturity faster so your organisation can start to leverage AI innovations?
A 2024 McKinsey study showed that 65 percent of organisations are regularly using gen AI across the business. That's nearly double the percentage from their previous survey in 2023.
So yes, the adoption speed and potential of the technology is incredibly high, but many organisations today still don’t really know where to start when it comes to Data + AI Activation.
The challenges?
Many companies today are limited by their existing IT infrastructure, fragmented data estates and disconnected tools that create data silos in the organisation. It takes far too much effort to turn raw data into actionable data.
Governance, policies, security and compliance remain a big challenge too.
Successfully leveraging data for AI purposes goes beyond implementing a few new tools. The answer to the challenges above is far more fundamental than you might think; If you want to successfully leverage AI and get business value from it as fast as possible, you need to accelerate your company’s plan for data + AI Maturity first.
But what sits at the core of Data + AI Maturity? This blog will explore the three main pillars of Data + AI Maturity; Strategy, Activation & Technology. For deeper insights, download our full Data + AI Strategy Guide here.
Pillar 1: Data + AI Strategy
Great AI starts with great data, which is why a successful data + AI strategy ensures that data and AI are strategically aligned with the organisation’s goals across all levels.
Data is what fuels your AI engine. As a result, the most critical step to scaling data, analytics, and AI is to create a comprehensive, actionable data strategy that aligns with your organisation’s corporate priorities and ensures your data is trustworthy.
The first step here is to identify the right use cases within your business and establish the right goals and KPIs for each use case. There are many areas in a business where data and AI can make a positive impact. But not every area might be a priority for your organisation. Great use cases are aligned with your overall business objectives, and the right KPIs will make sure you track success or failure properly.
No matter the use case, effective governance and robust data management are essential for success and are fundamental in building trust in your data strategy. Your organization must first consolidate and unify all company data. Only with a foundation of unified, harmonized, high-quality, and well-governed data can you then expand access and harness the power of data and AI to drive innovation.
Pillar 2: Data + AI Activation
As with every strategy - it’s only as good as its execution. So the success of your data and AI strategy depends on your ability to execute upon it. In other words, how well you’re able to activate data + AI across the business.
That means you need to prepare your data so that it can become the data foundation for AI activation. Building that data foundation first will allow you to activate and unlock the advantages and innovation that AI and machine learning have to offer.
Data + AI Activation can take on different forms. From data-driven decision making, to forecasting, predictive analytics and AI Agents. Each of these forms of data + AI activation uses specific techniques and technologies tailored to the needs and strategic goals of your sectors or business functions.
But again, the key to successful activation lies in the quality of the data, the alignment of AI applications with business objectives, and the organization's ability to integrate these technologies into their operational processes.
Pillar 3: Data + AI Technologies
Technology is a driver for most of the above. And it’s too important to ignore in your data + AI strategy - as you want to future-proof your stack and infrastructure as much as possible.
As organisations transition from large, rigid on-premise stacks to the cloud, they gain significant flexibility—but also introduce complexity, which can be costly. Integrating multiple apps and systems becomes challenging, and upgrading one solution may disrupt others.
One major aspect of a Data + AI technology stack is data unification. This process involves integrating disparate data sources—whether structured or unstructured—from various departments, systems, or external sources. The rationale is straightforward: unified data provides a comprehensive, accurate, and up-to-date foundation that is essential for training robust AI models.
These models rely on quality data to generate reliable insights, predictions, and decisions. Without data unification, organizations run the risk of AI systems producing skewed or erroneous outcomes due to incomplete or inconsistent data inputs.
Additionally, unified data enhances operational efficiency by providing all stakeholders with a single source of truth, facilitating smoother collaboration and decision-making processes across the business. Thus, data unification is not just a prerequisite for effective AI deployment; it is also a cornerstone for driving overall business intelligence and competitive advantage in the digital age.
Conclusion
In conclusion, achieving high levels of Data + AI Maturity is not just about adopting new technologies; it's about strategically integrating these technologies within the fabric of your organization. The rapid adoption of generative AI across industries underscores the potential for significant business transformation.
However, to truly leverage AI to its fullest potential and see tangible business results, organizations must focus on the foundational elements outlined in our three main pillars: Strategy, Activation, and Technology.
By developing a cohesive strategy that aligns data and AI with business goals, actively preparing data to fuel AI systems, and embracing advanced technologies that support data unification and integration, companies can overcome the common hurdles of fragmented systems and poor data quality.
This holistic approach will enable your organization to not only keep pace with technological advancements but also lead the charge in innovation, driving substantial growth and maintaining a competitive edge in an increasingly data-driven world. For those ready to delve deeper into crafting a comprehensive Data + AI strategy that propels your organization forward, our full Data + AI Strategy Guide offers further insights and practical steps to guide your journey towards true AI maturity.
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