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Leveraging AI To Transform Actionable Business Strategies Reliably

The way businesses leverage data to inform strategies is undergoing a significant shift. Artificial intelligence (AI) is no longer a futuristic concept; it's a powerful tool actively transforming how companies translate vast amounts of data into actionable insights.

 This article explores how AI is revolutionizing business strategy development, emphasizing reliable data mapping and analysis for optimized decision-making.


From Legacy Systems to AI-Powered Transformation

While the notion of AI in business might seem novel, AI's core functionalities have existed for some time.

 Daniel Ziv, VP for experience management and analytics at Verint, clarifies that AI itself isn't new, but its expanding applications and ability to transform data into actionable strategies are.

One key component of AI is large language models (LLMs), which have been around for a while. However, advancements in natural language understanding and generation over the past 18 months have significantly enhanced their capabilities.

 Ziv highlights how this progress, previously unseen by the public, has garnered increased awareness of AI's potential.

AI's Impact on Business: A Paradigm Shift

Ziv emphasizes a crucial turning point: the growing need and opportunity for automation platforms that organizations can leverage in innovative ways.

 Generative AI, for instance, is rapidly evolving, exhibiting a heightened understanding and proficiency in language comprehension.

Another significant factor driving AI adoption is cloud computing. Cloud platforms offer superior data processing capabilities at lower costs compared to traditional on-premise solutions.

 Previously, deploying AI software involved hardware acquisition, provisioning, software installation, and extensive training – a process that could take months or even years.

 Today, the focus lies on utilizing AI's advancements to swiftly analyze and generate recommendations from massive datasets.

Data Transformation: Unlocking the Power of Insights

Data transformation is a crucial step in AI-powered business strategy development. The approach varies depending on the data type, categorized as structured (numerical data) and unstructured/semi-structured (text, audio, video, etc.).

 Structured data is readily processed by computers due to its numerical nature. However, unstructured and semi-structured data pose a greater challenge.

Fortunately, advancements in generative AI have addressed this disparity. Ziv explains how AI can now handle these complex data formats with significantly improved efficiency.

Refining AI for Business-Specific Insights

Verint's experience exemplifies how AI can empower businesses to effectively utilize their data.

 They've assisted clients in overcoming data accuracy issues. Ziv cautions against using generic AI models trained on general internet data, as they lack the specific behavioral context relevant to individual businesses.

He compares current AI to a young child learning to speak. The AI can understand and respond to language at a basic level, but it lacks the necessary knowledge and experience to provide insightful answers directly related to desired business outcomes.

 Developers are actively working on refining AI to bridge this gap. The solution, according to Ziv, lies in harnessing AI's language capabilities and tailoring them with specific behavioral data unique to a company's customer interactions.

The Path to Predictive Accuracy: A Multi-Faceted Approach

SoundCommerce underscores the importance of a nuanced approach to data utilization for achieving predictive accuracy.

 Unlike traditional data management providers, they offer a no-code environment accessible to all. However, CEO Eric Best acknowledges the challenges associated with data transformation.

The process involves extracting data from various sources, including customer data platforms (CRMs), followed by validation and meaning attribution.

 Accurate data transformation ensures compatibility and facilitates informed decision-making.

Best highlights the challenge of integrating data from multiple sources, such as cash registers, e-commerce platforms, and mobile apps. AI plays a crucial role in unifying these diverse data streams into a consistent format for analysis.

AI Mapping: Natural Language for Technical Tasks

Extracting accurate insights from combined data feeds necessitates describing the data in natural language terms.

 To leverage AI for data mapping, users need to clearly communicate their desired data transformations using everyday language.

The key lies in enabling AI to write the software code required for data transformation. As Best explains, this shift in focus prioritizes proficiency in natural language communication over traditional software engineering skills.

The Future of AI-Powered Business Strategies

While AI empowers businesses with sophisticated decision-making capabilities, cost-effectiveness remains a concern. Best suggests that the practicality of AI for smaller companies hinges on tightly defined use cases.

He emphasizes that the fundamental business questions haven't changed – how to allocate advertising dollars effectively remains a timeless concern.

 What's new is the ability to automate these processes and generate answers at scale, fundamentally transforming how businesses operate in the modern era.


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