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AI and Digital Transformation: 4 Ways to Greater Automation
The initial phase of digital transformation is still an ongoing process for many enterprises, centering around the digitalization of products, services, and business processes. The second phase uses AI to enhance the quality of decision-making, strengthen the relationships with customers, and optimize organizational productivity.
While various enterprises are at various stages of maturity of digital transformation, many companies have already been experimenting with AI separately to discover how it could benefit the business in the second phase of digital transformation.
One of the crucial reasons for the second phase of digital transformation’s unsymmetrical results is the understanding or lack of knowledge of what Artificial Intelligence can do. There has been a general illusion that artificial general intelligence (AGI) can solve any issue when truly artificial narrow intelligence (ANI) is the ultra-modern.
The digital transformation journey has crossed from digitization to digitalization. It also covers different data technologies across various industry verticals. To the extent that data-driven innovations are helping to manifest several advances in digital technologies as actionable, the next boundary in re-molding enterprises is AI transformation. Progression in AI is forcing digital enterprises toward becoming intelligent enterprises. AI technologies are already revolutionizing not just how we recognize and do business, but also the business environment and its’ overall landscape.
Business intelligence is going far beyond dashboards. AI and machine learning are becoming a much more user-friendly process for inexperienced workers as augmented analytics are embedded into platforms.
Organizations are struggling to get their data management and machine learning practices up. This is where augmented analytics are arriving at the rescue. What’s more, it could also help with utilizing machine learning for production purposes, which has been a problem for many enterprises.
The benefits of this have the potential to extend far beyond business intelligence. The passion for implementing AI in enterprises was high at the initial stage. By adding automation to parts of business operations, such as data pipelines and data management, augmented analytics can be one of the elements of the solution to acquiring AI into enterprise production.
Automation has moved past the workforce to white-collar tasks that are repetitive. Just like with some robotic process automation (RPA) tools and chatbots, several automated systems are not necessarily “intelligent” because they are inevitably programmed. i.e., a given input produces a given output. AI enlarges the scope of what automated systems can do and moves enterprises to the second phase of digital transformation.
More than half of organizations acknowledge not having a formal customer engagement program established. Because of this, those organizations had no grasp on the number of customers they’d actually lost in a one year period. On the other hand, most customers expect a consistent experience wherever they engage. Digital transformation using AI can help optimize customer engagement by dynamically aligning the website content with the customer’s preferences.
AI solutions and tools help analyze large datasets in real-time, plan production efficiently, balance supply and demand gaps, schedule factory activities effectively, and develop error-free SCM plans and strategy. AI can also help estimate the market requirement and manage production accordingly to avoid overproduction or shortage of products, either of which would result in loss.
The future of connected digital transformation includes more IoT and industrial IoT devices and the coordination of AI.
IoT and IoT devices are already offering businesses the clarity at the edge, which they lacked before. When combined with AI, the sensors are helping to change the ways companies operate to yield optimization.
Enterprises with and without AI-enabled digital transformation models may adopt some AI by default because it has been embedded in the software tools, applications, and platforms they already utilize. Data scientists should aim to solve complex issues and bring more value from AI in the second phase of digital transformation. Simultaneously, citizen developers (power users) can tackle easier problems, such as optimizing business processes and tasks within their department.
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