Why Data-Driven Companies Have More Success With AI
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.
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 underperformers 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.
Insights from McKinsey’s research emphasize that 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.
The MuleSoft platform is 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.
The Advantages of Generative AI-Driven Process Automation
Organizations have been working to make things easier through automation for decades. Today, generative artificial intelligence (AI) shows great promise in assisting humans to complete tasks faster and accomplish more within their organizations. Business automation has grown: 67% of organizations are now centrally managing automation, and 59% are tracking automation work.
Organizations can use generative AI to improve and accelerate their business process automation. Systems like ChatGPT offer unparalleled efficiency and innovation because they can understand and generate human-like text. These capabilities empower businesses across diverse functions, encompassing customer service, sales, operations, supply chain, and more.
Beyond these functionalities, generative AI will also enable the rapid adoption of business process automation by introducing the capacity to create automation without excessive IT intervention. As organizations embrace low-code and no-code automation software, the dynamics of workforce activities and the types of work people do will evolve.
AI-driven process automation is the future
When computers first became mainstream in the workplace, thought leaders assessed that these machines would replace up to 98% of jobs. Contemporary perspectives echo similar sentiments. Some forecast that generative AI will likely touch every facet of industry, causing huge workforce shifts.
The truth is, no one knows what the results of generative AI and other automation tools will ultimately bring. Firms such as Goldman Sachs are using the trajectory of past innovation cycles as a lens to view how the impact of generative AI may unfold.
Organizations are leveraging generative AI to refine, optimize, and simplify many parts of their operations. Applications range from enhancing customer service experiences and envisioning new product designs to facilitating forecasting decision-making processes and empowering non-coders to create software.
While the outcomes of generative AI and other automation tools remain uncertain, their introduction into everyday processes is poised to contribute significantly to tackling complex contemporary challenges.
Benefits of Low-Code and No-Code AI-Driven Automation
Democratizing the automation process and empowering individuals across various departments to create automations without the need for coding, often referred to as “low-code” or “no-code” automation, surfaces many opportunities for organizations. Here are some key aspects of the possibilities and benefits associated with no-code automation:
Rapid Deployment
- No-code platforms enable faster automation deployment, eliminating the need for extensive coding and programming. This allows organizations to respond swiftly to changing business needs.
Accessibility Across Departments
- Non-technical users like business analysts, managers, and subject matter experts can actively participate in the automation process. This fosters collaboration between IT and other departments, ensuring that automation solutions align with business requirements.
Reduced Dependence on IT
- No-code automation alleviates the burden on IT departments, allowing them to focus on more complex tasks. Business users can create and modify automations, reducing the backlog of requests for IT assistance.
Cost Savings
- No-code automation reduces the need to hire specialized coding professionals and reduce headcount in other areas, resulting in cost savings. It also allows organizations to maximize existing talent by enabling non-developers to contribute to the automation process.
Agility and Flexibility
- No-code platforms empower organizations to adapt quickly to changing business conditions. Users can easily modify and iterate on automations without extensive coding knowledge, promoting agility in response to evolving requirements.
Innovation at the Front Lines
- Business users closest to the operational processes can directly contribute to automation, fostering innovation at the organization’s front lines. IT is freed up to perform more complex work, rather than serve as the automation builder and accountability party. This decentralization of automation creation can lead to more creative and contextually relevant solutions.
Scalability
- No-code automation platforms often come with scalability features, allowing organizations to expand their automation initiatives seamlessly as business needs grow. This scalability is essential for long-term success.
Improved Documentation and Transparency
- No-code platforms often include visual interfaces that clearly represent workflows and processes. This enhances documentation and transparency, making it easier for teams to understand and maintain automations.
Faster Return on Investment (ROI)
- With the accelerated development and deployment cycles enabled by no-code platforms, organizations can realize a faster return on their investment in automation technologies.
In essence, no-code automation allows organizations to distribute the power of automation creation across different roles. This approach not only accelerates the implementation of automation but also fosters a culture of innovation and collaboration within the organization.
Building out technology to support accelerated automation is a complex and time-consuming process. Fortunately, some companies offer platforms and integrations to facilitate the process.
How AI-Driven Automation is Helping Organizations Today
Currently, AI-driven automation is profoundly impacting organizations across various industries. It provides multiple benefits that enhance efficiency, decision-making, and overall business performance. There are several ways in which AI-driven automation helps organizations.
To start, the automation of routine and repetitive tasks promises to enhance efficiency by enabling organizations to minimize manual effort and reduce errors. AI algorithms further contribute to efficiency by handling data processing and analysis at a speed and scale that surpasses human capabilities.
Automating routine tasks also increases employee productivity, allowing them to focus on more strategic and creative aspects of their work. AI tools further assist employees in data analysis, research, and decision support.
Using AI tools to analyze extensive datasets and extract meaningful insights allows AI to significantly aid in and improve decision-making processes. The additional incorporation of generative AI into predictive analytics, can give organizations new views on trends and patterns they already follow.
That same process of analyzing data sets to derive insights for internal decision-making is also powering customer interactions. AI-driven chatbots and virtual assistants offer instantaneous and personalized customer support, elevating the overall customer experience. And automating customer queries and issue resolution ensures quicker responses, thereby improving customer satisfaction.
AI can also be used for other customer-facing tasks like creating personalized marketing strategies using its ability to analyze customer behavior. The automation of marketing campaigns ensures targeted and timely communication with customers.
Workflow automation facilitates seamless coordination between different departments and functions, enhancing overall organizational efficiency. By automating repetitive tasks and optimizing processes, organizations achieve significant cost savings in time, headcount, resources, and operational expenses.
AI-driven automation can play a crucial role in resource allocation and utilization efficiency. For example, organizations can accurately monitor inventory and enhance overall supply chain efficiency using AI-driven automation for demand forecasting, inventory management, and logistics optimization.
Lastly, AI is helping businesses discover innovations by generating insights and ideas that drive product development and business strategy. This automation removes the burden of lower-order creative tasks and allows humans to channel their efforts into higher-order creative thinking.
As AI technology progresses, the expanding scope and impact of AI-driven automation presents organizations with unprecedented opportunities for heightened efficiency and innovation. The true potential of AI-driven automation is magnified when the capability to create this automation is extended beyond traditional coding experts. No-code automation platforms will be pivotal, empowering individuals across diverse departments to actively contribute to creating, testing, and implementing automation.
How Einstein for MuleSoft and Flow can accelerate business automation
Salesforce has consistently worked to make business process automation accessible to more users. To do this they have embedded Einstein into Salesforce Flow and MuleSoft Anypoint Code Builder providing customers with low- and no-code automation tools.
Einstein for Flow is integrated into the Salesforce platform, allowing teams to create any automation process without needing code. To start automating a workflow, users describe the flow they would like to make to the Einstein chat prompt or choose from a list in Flow Builder.
From there, the workflow process will be broken down into simple steps, coded, and prepared for visualization in Flow Builder. Business teams can use Flow to set up any automation. Business teams can still use Flow for automations that call external systems once the APIs are built in MuleSoft Anypoint Platform and shared with Flow.
For example, you can remove a human triage step by automating it with Flow. Watch the process in detail in the “Use AI to Accelerate Business Automation with MuleSoft and Flow” webinar.
Drive stronger customer relationships and bring more value to customers by automating workflows with MuleSoft and Flow. Learn more about accelerating your automation with Einstein for Flow.