Strategic AI-First Business Transformation & Optimization 

For any business to stay relevant and thrive given the swift pace of change and disruption in the Smart Technology Era, it needs to be transformed into a smart-data- driven business and have increasingly more real-time intelligence on all aspects of its internal operations, customer needs and impact, and competitive and collaborative forces in the ecosystem in which the business operates. The better a company is able to mine all available internal and external data across its operations, value chain, customers, and ecosystem to create real-time dynamic simulation models of all aspects of its business, the better it would be able to optimize the business over short, medium, and long-term windows and adjust its course where required. This is relevant across all industries.

CORTEX aiOPTIMIZE is the core solution in the CORTEX AI Library that addresses this need and consists of the following components:

Smart Data-driven Business Transformation

  • Define Smart Data & AI journey roadmap (Game changer and quick wins use cases, future state capability scenarios, benefits and roadmap)
  • Implement Smart Data & AI Transformation Catalysts to accelerate path to value generation
  • Regular assessment of Smart Data & AI maturity from a Strategy, Technology, People, Data and Process perspective (maturity states vary from ad hoc, opportunistic, repeatable, managed, and optimized)

Digital Business Modelling and Optimization

  • Develop dynamic simulation models of all aspects of the business across its operations, value chain, customers, and the competitive and collaborative forces in its ecosystem.
  • Strategic business optimization over short, medium and long-term window periods

Across industries, organizations are assessing ways and means to make better business decisions utilizing such untapped
 and plentiful information. That means as the Smart Data and AI technologies evolve and more and more business use cases come into the fray, the need for groundbreaking new approaches to data infrastructure, computing (both in hardware and software), AI tools and platforms, processes, organizational alignment and roles, are needed. As enterprises look to innovate at a faster pace, launching innovative products and improve customer services, they need to find better ways of managing and utilizing data both within the internal and external firewalls. Organizations are realizing the need for and the importance of scaling up their existing data management practices and adopting newer information management paradigms to combat the perceived risk of reduced business insight. So an organization’s ability to analyze that data to find meaningful insights is becoming increasingly complex.

Many of the current success stories with Smart Data & AI have come about with companies enabling analytic innovation and creating data services, embedding a culture of innovation to create and propagate new database solutions, enhancing existing solutions for data mining, implementing predictive analytics, and machine learning techniques, complemented by the creation of skills and roles such as data scientists, AI or machine learning engineers, data science developers, big data architects, data visualization specialists, and data engineers, among others. These enterprises’ experiences in the Smart Data & AI landscape are characterized by innovation, acceleration, and collaboration.

Another key aspect of leveraging Smart Data & AI is to also understand where it can be used, when it can be used, and how it can be used. Some examples of value drivers include the following strategic and efficiency drivers:

Strategic drivers

  • Generate new opportunities: Through exploratory analysis uncover hidden patterns and generate new business opportunities
  • Proactive decisions: Through predictive analytics forecast customer and market dynamics, gain operational insights
  • Faster decisions: Speed up strategic decision making, provide more frequent and accurate analysis (e.g., real-time analytics dashboard, intelligent virtual assistants and advisors)
  • Better decisions: Estimate impact using cross-organizational analysis, quantify impact of decisions.

Efficiency drivers

  • Reduce costs: Focus on continuous improvements items, reduce cots on people, process, infrastructure & tools that does not enhance an agile and smart data-driven business
  • Increase automation: Reduce efforts needed to extract, consolidate and produce reports, innovate automation options
  • Improve capabilities: Complement or retool skills of current analysts to emphasize problem solving and recommendations, develop data driven decision-making culture
  • Eliminate redundancy: Eliminate redundant tools, data stores and processes, focus on consolidation and continuous improvements
  • Improve processes: Standardize metrics and stream line processes