Creating an Enterprise AI Strategy
Enterprise AI is the integration of advanced AI-enabled technologies and techniques within organizations to enhance various business functions. This integration encompasses routine tasks like data collection and analysis and extends to more complex operations such as business process automation, supply chain, finance, marketing, customer service and risk management. At its core, enterprise AI is typified by AI software tools that leverage cutting-edge methodologies, including machine learning, natural language processing (NLP) and computer vision. These technologies empower organizations to achieve process automation in various use cases, streamline intricate business functions, automate repetitive tasks and make the most of the data they accumulate. Companies can extract valuable insights about key performance indicators (KPIs) and refine their business strategies by using AI to analyze this data. However, the journey towards digital transformation through enterprise AI has challenges. Implementing these systems requires substantial investments in technology infrastructure and skilled personnel. TekMetrix aims to reduce implementation costs while transforming the digital landscape with the power of AI, giving a strategic and significant business case opportunity.
Creating an AI strategy is a complex process that requires careful planning and execution. Below are minimum steps to create an effective AI strategy:
- Define goals
- Identify the business problems that you want to solve with AI. Determine the specific outcomes that you want to achieve, such as reducing costs, improving customer satisfaction, or increasing revenue
- Assess data
- Evaluate the quality and quantity of your data. Determine whether you have the necessary data to train your AI models. If not, identify the sources of data that you need to acquire
- Select AI tools
- Choose the AI tools that are best suited for your business needs. Consider factors such as ease of use, scalability, and cost
- Build AI models
- Train AI models using your data and AI tools. Test your models to ensure that they are accurate and reliable
- Deploy AI models
- Integrate your AI models into your business processes. Monitor the performance of your models and make adjustments as needed.
- Evaluate your results
- Measure the impact of your AI strategy on your business outcomes. Identify areas for improvement and refine your strategy accordingly
We use the methods of “Capability Management” as a framework to define Enterprise AI strategy and architecture for data science initiatives. It is an approach that organizes groups of capabilities that may be implemented to drive value and improve an organizations competitiveness. Capability Management" strives to manage groups of analysis capabilities within an organization to ensure its competitive position in the industry and its ongoing profitability and continued existence.
Capability Groups can be defined as:
- Essential Capabilities
- Enabling Capabilities
- Complementary Capabilities
Essential capabilities are identified as distinct analytic groups that constitute competitive advantage for the firm. Analytics for these groups must maintain continuous improvement. They provide sustained long term organizational learning for improved productivity and competitiveness. Enabling capabilities are necessary but not sufficient for the company to distinguish themselves in the marketplace. Analytics that enable capabilities tend to fall in operational categories, business processes that are required, that must be improved but which cannot be used as competitive advantage. Complementary Capabilities are defined as those which add value to essential capabilities but can be easily reproduced by the firms competitors.
Complete Digital Transformation Foundation and Roadmap
