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Data-driven companies are acutely aware that access to data is the key to generative and predictive AI. In fact, organizations working toward data-driven models are more likely to follow strategy, data, models, tools, technology, and talent best practices. These practices have contributed to data-driven companies’ success with AI over peers that have maintained siloed data models. The truth is that data-driven companies consistently outperform their counterparts, especially in maximizing the potential of artificial intelligence (AI). 

Finding success with AI through a data-driven approach      

The potential for AI to streamline processes and reduce costs is particularly enticing. Of the 27% of organizations using AI to increase productivity, 90% reported higher productivity levels than those that didn’t. Enterprises yielding the highest returns on their AI investments tend to follow best practices revolving around technology and data, and they develop a work culture around employees obtaining the skills to use AI to drive efficiency. 

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This is apparent when research shows that 50% of top AI performers have a clearly defined strategy and vision for AI; meanwhile, only 20% of under-performers have one. 

But having a clear AI strategy may come more easily when all your data is integrated and accessible – which is not true for most organizations. The difficulty of integrating disparate data remains a barrier to maximizing the potential of AI. More than half of the top AI performers and only 12% of all others can incorporate data into AI models as needed. Additionally, trouble with digital transformation also appears to lie within the ability to integrate data and systems, with 80% of organizations saying that integration hinders their efforts to continue digital transformation. 

The organizations successfully leveraging AI offer great insight into how others can evolve their approaches to start getting more out of their AI efforts.

Top AI performers actively empower employees across all departments to contribute to AI development. This means that IT teams have established the necessary systems, robust data structures, and tools to facilitate low and no-code AI implementation. 

Falling behind in this rapidly evolving landscape poses severe consequences, potentially costing organizations an average of $9.5 million, an increase of approximately $3 million from the previous year.

Despite the dynamic nature of technological advancements, the composition of high-performing groups has seen minimal change over the last five years, indicating that the integration barrier is playing a large role in the ability to adapt and use new technologies. 

These data points speak volumes about how business leaders think about AI: while heavier reliance on AI for many functions might be a goal, actually implementing AI does not appear to be fully vetted.   

Not all data is created equal

In artificial intelligence, output generation hinges on the data provided to the system and explicit instructions outlining the tasks the AI is designed to perform. Optimizing AI output requires data structured and tagged in a manner it can understand. 

The situation is analogous to receiving a disassembled piece of furniture in the mail along with instructions for assembly. Regardless of the quality of the instructions, it’s assumed that the reader knows how to use basic, necessary tools and that they can comprehend the instructions as written.

But if the reader can’t use the tools (disparate data) or the instructions (uncleaned data), they most likely cannot follow through with a process that ends with well-constructed furniture. You can do your best to build the furniture within the boundaries of your imagination. Still, it will take you much longer to produce the expected outcome than someone who understands how to use the tools and receives the instructions in a language they understand. 

This predicament resembles the setup of an AI system without proper data integration. The AI is akin to an individual confronted with a pile of unassembled furniture pieces and no idea how to assemble them.

The centrality of your data becomes evident in determining the quality of output your AI can produce. For instance, businesses commonly need to integrate siloed data before experiencing the full benefits of AI. The ingestion of disparate siloed data into an AI program can yield undesired outcomes due to inherent redundancies, disconnections, and ontological tagging or sorting variations. Like trying to decipher instructions in an unfamiliar language by relying solely on visual cues, AI outputs derived from inadequately managed data are prone to biases, inaccuracies, or accidental exposure of sensitive information. 

Data-driven companies have already strategically focused on integrating and harmonizing their data to glean detailed insights and pose previously unimagined questions. This foundational approach facilitates the seamless integration of AI models, setting them apart from counterparts struggling to unlock the practical potential of their data. With the high precision required for optimal AI output, the adept handling of data is required to unleash the full potential of AI.

Empowering the transition to a data-driven enterprise

The challenge with data architectures lies not in their complexity but in the seamless transition from conceptualization to implementation. The conventional approach of devising and executing a blueprint for a comprehensive redesign often resembles an attempt to construct an entire city in a single day for organizations aspiring to modernize their data architectures. 

While top AI performers with substantial financial resources can navigate this challenge, the associated costs and time investments are generally prohibitive for the majority of organizations. 

The solution lies in breaking free from legacy systems thinking and adopting a process that leverages existing information to facilitate incremental, organization-wide change. 

To begin, there is no need to start from scratch. Established integrated architectures, characterized by reliability, speed, and flexibility, already exist. One notable example is this reference data architecture, a proven solution implemented across diverse industries, showcasing notable reductions in time-to-market and associated AI-related costs. 

A pragmatic strategy to mitigate integration complexity involves working on one product or AI use case at a time. The process does not demand round-the-clock access to an organization’s data; it can initiate with the data essential for a specific project, progressively expanding to encompass broader organizational integration. This approach fosters efficiency and minimizes resistance from business leaders still adhering to legacy technology views. 

Lastly, not enough can be said about harnessing the power of the accomplishments made in the integration and data industries over the last few decades. Once you have started to form a data architecture that will transform your business into a more data-driven one, many tools, data sources, and other technologies can expedite the shift to more machine-augmented operations. 

This holistic approach ensures a modernized data architecture and an empowered, future-ready foundation for data-driven enterprises.

Leveraging market solutions to accelerate enterprise transformation

Simplifying the process of unlocking enterprise data will lead to faster transformation and adoption of revenue-generating AI. Working with industry leaders presents the opportunity to reduce the complexities associated with recreating data architectures, optimize integration processes, and shift to data-driven frameworks capable of effective AI implementation for sustainable growth. 

A prevalent challenge for organizations revolves around the fragmented nature of applications and data silos. Consider a customer-facing app scenario where a customer only orders to find an item missing upon delivery. Conventional food ordering applications, lacking in problem-triage functionalities, require the customer to initiate direct contact with the restaurant for issue resolution. 

By seamlessly unifying siloed data using API-led connectivity, the food ordering app can interact with the customer and draw information from the restaurant’s CRM, order management, payment, and logistics platforms. The integration facilitates real-time issue resolution within the app without cumbersome phone calls or additional staffing. 

MuleSoft provides these solutions and extends beyond integration, empowering employees to craft automation without coding and removing the need for IT involvement in every automation request. This transformative capability enables the conversion of any business from struggling to integrate to implementing data-driven strategies. 

MuleSoft’s AI-driven integration and automation functionalities effectively enhance productivity. In addition, MuleSoft offers API management solutions that ensure organizational-level security and governance when integrating with vendor AIs. This comprehensive approach empowers customers to build AI solutions tailored to their specific needs as they need them. 

We offer a simplified and cost-effective pathway for companies to achieve their integration and AI objectives efficiently. Learn more about seamlessly connecting disparate elements and leveraging AI to empower your business with MuleSoft – explore the demo or engage with an expert today