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Saturday, March 29, 2025

Generative AI: From Experimentation to Enterprise-Level Value Realization

March 29, 2025

Generative AI (Gen AI) is transitioning from the proof-of-concept (PoC) phase to measurable enterprise-level value. However, according to Accenture’s report Making Reinvention Real with Gen AI, while 36% of companies have successfully scaled Gen AI solutions, only 13% have achieved enterprise-wide impact. This gap stems from inadequate data preparedness, incomplete process redesign, lagging talent strategies, and insufficient governance. This article explores how businesses can transition Gen AI from experimentation to large-scale enterprise adoption and provides actionable solutions.

Five Key Actions for Scaling Gen AI at the Enterprise Level

Accenture’s research identifies five key imperatives that help businesses overcome the challenges of Gen AI adoption.

1. Lead with Value

To drive transformation, companies must focus on high-impact business initiatives rather than isolated AI experiments.

Case Study: Ecolab
Ecolab implemented a “Lead to Cash” end-to-end optimization strategy, leveraging AI agents to automate order validation, credit checks, and invoice processing. This not only enhanced customer and sales representative experiences but also unlocked new revenue opportunities.

2. Reinvent Talent and Ways of Working

Gen AI is more than just a tool—it is a catalyst for transforming enterprise operations. However, Accenture’s report highlights that companies invest three times more in AI technology than in workforce training, hindering progress.

Case Study: Accenture’s Marketing & Communications (M+C) Team
Accenture’s M+C team deployed 14 specialized AI agents to optimize marketing processes, reducing internal communications by 60%, increasing brand value by 25%, and improving operational efficiency by 30% through automation.

3. Build an AI-Enabled, Secure Digital Core

Merely adopting AI is insufficient—businesses must establish a flexible, AI-powered data and computing infrastructure to enable large-scale deployment.

Case Study: Sempra
Sempra modernized its digital core through cloud architecture, a data mesh framework, and AI governance, improving data analysis efficiency by 90% and enhancing both customer experience and security.

4. Close the Gap on Responsible AI

AI governance is not just about compliance—it is essential for long-term value creation.

Case Study: A Leading Bank
A global bank implemented AI governance frameworks, including an AI Security Questionnaire, reducing legal review times by 67%, improving credit assessment efficiency by 80%, and saving over $200 million annually in operational costs.

5. Drive Continuous Reinvention

Gen AI transformation is an ongoing process, requiring an agile organizational culture where AI is embedded at the core of business operations.

Case Study: A Leading Electronics Retailer
This retailer used AI to enhance customer service, achieving a 35% improvement in voice interaction accuracy, a 70% increase in automated customer service responses, and reducing average chat handling time by 38 seconds.

How Enterprises Can Accelerate Gen AI Adoption at Scale

1. Executive Leadership and Sponsorship

According to Accenture, companies where CEOs actively lead AI adoption are 2.5 times more likely to achieve success. Strong executive commitment is crucial.

2. Elevate AI Literacy

Boards and senior executives must develop a deeper understanding of AI to make informed strategic decisions and avoid technology-driven misinvestments.

3. Redesign High-Value Processes

Businesses should focus on cross-functional process optimization rather than siloed implementations. Human-AI collaboration should be leveraged to delegate repetitive tasks to AI agents while allowing employees to focus on creative and strategic work.

4. Establish a Robust Data Foundation

2.9 times more successful enterprises emphasize a comprehensive data strategy, underlining the importance of data governance, quality, and accessibility.

Challenges and Considerations: Avoiding Pitfalls in Gen AI Transformation

1. Reliability and Limitations of Research

Accenture’s study, based on 2,000+ AI projects and 3,450 C-level executive surveys, provides clear causal insights. However, the following limitations should be noted:

Enterprise Size Suitability: The strategies outlined in the report are primarily designed for large enterprises, and mid-sized firms may need tailored approaches.
Lack of Failure Case Studies: The report does not deeply analyze AI adoption failures, potentially leading to survivorship bias.
Technical Challenges Not Fully Explored: Issues such as model selection, data security, and AI generalization remain underexplored.

2. Future Outlook

Small Language Models (SLMs) will become mainstream, enabling more domain-specific AI applications.
AI Agents will achieve large-scale adoption by 2025.
Companies with strong continuous reinvention capabilities are 2.1 times more likely to succeed in AI-driven business transformation.

Conclusion and Strategic Recommendations

Key Takeaways

The biggest barrier to Gen AI adoption is not technology but talent, processes, and governance.
The 2.5x ROI gap stems from whether companies systematically execute the five key action areas.
Enterprises must act swiftly—delaying AI adoption risks losing competitive advantage.

Final Thought

The journey of Gen AI transformation has just begun. Companies that successfully bridge the gap between experimentation and enterprise-wide adoption will secure a sustainable competitive edge in the AI-driven era.

IBM's Text-to-SQL Generator: How Generative AI is Revolutionizing Enterprise Data Insights and Queries

August 25, 2024

IBM recently launched a text-to-SQL generator that has made significant strides in handling complex database queries, ranking first on the BIRD benchmark. This solution, based on IBM's Granite code model, is part of IBM's broader effort to integrate generative AI into data services to help enterprises extract fresh insights from large databases.

As the volume of enterprise data surges—from website clicks to sales reports—companies are collecting and storing more data than ever before. However, the tools for searching across databases, data warehouses, and data lakehouses, and transforming this information into useful insights, have not kept pace with the data's growth. Many companies fail to fully utilize their data because employees either can't find the information they need or can't translate their questions into the code required to unlock the answers.

Generative AI is poised to simplify this process. Large language models (LLMs) are removing key barriers that currently make it difficult to search, retrieve, and transform tabular data. SQL is the dominant language for interacting with databases, yet within any given enterprise, only a limited number of individuals understand how large databases are structured and can query them in SQL. This effectively restricts who can access the data to uncover insights that could improve business operations.

To make enterprise data more accessible to a broader range of users, IBM and other tech companies have focused on teaching LLMs to write SQL. In a recent milestone, IBM's Granite code model topped the BIRD leaderboard, which measures how well LLMs can parse a natural language question and translate it into SQL to run on real data and answer the question.

IBM's text-to-SQL generator still has a long way to go. Despite being the top performer on BIRD, it answered only 68% of questions correctly, compared to the 93% accuracy achieved by engineers who participated in the test. However, considering the rapid progress LLMs have made in other programming tasks, such as refactoring COBOL code into Java, the gap between AI and human-generated SQL may soon narrow.

In BIRD's benchmark for code execution speed—measuring the computational resources required to run the AI-generated SQL against the database—BIRD evaluators scored IBM's solution at 80, just below the 90 scored by volunteer engineers, while other AI systems scored 65.

IBM's SQL code generator is just one of several technologies that IBM researchers are developing to help enterprises find, retrieve, transform, and visualize their data. IBM has already rolled out other LLM-powered components that enrich structured data with descriptions and business terminology, making database tables and columns easier to locate. These technologies were recently integrated into IBM's Knowledge Catalog and watsonx.data products.

“We're on a mission to drive AI into the entire data services pipeline,” said Lisa Amini, a research director at IBM who led the team developing the data enrichment technologies and SQL generator. “The features we're developing can help data stewards and engineers be more productive, and enable data and business analysts to reach insights faster.”

IBM researchers have designed a conversational graphical user interface (CGUI) that allows data engineers, stewards, and analysts to interact with their data through conversation. The CGUI combines the personal touch of an AI chat interface with the intuitive nature of a web-based GUI, helping users more easily interact with structured data and explore results.

In conclusion, IBM's text-to-SQL generator and its underlying Granite code model bring innovation to enterprise data services, enabling companies to more effectively extract valuable insights from vast amounts of data. This not only enhances data analysis efficiency but also opens up new avenues for non-technical users to access data. With IBM's continued innovation in generative AI and LLMs, we can expect even more powerful tools for data interaction and analysis, further driving transformation in enterprise data utilization.

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