Boost Enterprise Productivity

Boost Enterprise Productivity with Generative AI

eBook

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1. Introduction 03

2. Generative AI is a productivity accelerator 03

3. Generative AI: Inflection point for business adoption of AI 05

3.1 Indicative generative AI use-cases in various industries 05

3.2. Value delivered by generative AI in data engineering 06

4. Applying generative AI to solve critical business problems 08

4.1. Conversational AI tool with semantic search for brand health tracking 08

4.2. Smart Q&A bot for precise enterprise knowledge retrieval 09

4.3. Creative generation engine for marketing campaign optimization 10

5. Conclusion 11

Table of Contents

1. Introduction

2. Generative AI is a productivity accelerator

ChatGPT has woken up the world to the transformative potential of generative AI, causing a sudden emergence of this technology as a critical strategic consideration for business. According to a 2023 KPMG survey, 77% of business executives globally expect generative AI to have the largest impact on their businesses out of all emerging technologies.1 Many organizations are seeing generative AI as a disruptive tool that can unlock new productivity frontiers with a multitude of applications, ranging from content creation to task automation and personalization.

In this whitepaper, we explore how generative AI can drive value across business functions and industries. We also look at how organizations can embrace this new technology and accelerate their generative AI journeys.

Generative AI is a subset of artificial intelligence that generates new data, content, or information based on different inputs, unlike traditional AI which mainly classifies, predicts, or optimizes. Generative AI tools could be multimodal, capable of creating text, images, audio, videos, code, simulations, and more, drawing from their training on existing content and subsequent fine-tuning.

Core to generative AI are foundation methods that fall under the umbrella of deep learning. Unlike previous deep learning models, these advanced models can process extremely large, diverse unstructured datasets and perform more than one task. The primary goal of generative AI is to produce meaningful and novel outputs. It is particularly useful in creative tasks, recommendations, and data augmentation. Generative AI models learn from large datasets to understand underlying patterns, structures, and relationships and then use this knowledge to generate new data that conforms to these learned patterns.

The following table summarizes a few popular generative AI applications today and some indicative examples of popular tools available in the market:

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Area of application Applications of generative AI

Indicative examples of tools

Text

• Text summarization

• Language translation

• Content generation

• Autocomplete and predictive text

• Question answering

Jasper, Copy.ai, Magic Wrote, Rytr, Spellbook

Audio

• Voice cloning and conversion

• Speech synthesis & transcription including text-to-speech (TTS)

• Sound effects

• Audio enhancement

• Music composition

Sonix.ai, Amper, Harmonai, Soundraw, Riffusion

Image and video

• Image generation

• Image super-resolution

• Image in-painting

• Content moderation for image/ videos

• Video generation and enhancement

• Deepfake detection

Midjourney, Stable Diffusion, Adobe Firefly, Dall-E, Craiyon

Coding

• Code generation and completion

• Code quality/refactoring

• Code documentation

• Code translation

Github Copilot, CodeWP, Codex, Tabnine, Hugging Face

Search

• Semantic search

• Personalized recommendations

• Sentiment analysis

Perplexity AI, Komo AI, Neeva Multi-On, Consensus

Chatbots

• Voice assistants and conversational AI Customer support bots

• Personal assistants

• Language support

Bard, ChatGPT, Bing, Pi, YouChat, Claude, Meta AI

Fig.1 Top generative AI applications with examples of off-the-shelf tools

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Industry Indicative use-cases

CPG

• Personalized purchase experience • Targeted marketing campaigns and advertising • Content generation • Demand forecasting • Product design and development

Finance and Banking

• Fraud detection and prevention • Customer service and personalization • Cybersecurity • Portfolio management • Compliance and risk analysis

Industrial and Automobile

• Inventory management • Production process optimization • Price forecasting of raw material • Predictive maintenance • Generative design components

Healthcare and Biopharma

• Drug discovery and development • Clinical trial optimization • Medical imaging • Analyzing and summarizing medical/ EH records

Insurance

• Underwriting automation • Fraud prevention • Personalized customer service • Portfolio risk assessment

Media and Communications

• Automated and interactive content generation • Automated audio, image video production • Content planning and scheduling • Localization and translation • Content archiving and retrieval

3. Generative AI: Inflection point for business adoption of AI Generative AI presents a significant opportunity for businesses. Many forward-thinking companies are already venturing into generative AI initiatives. When effectively deployed, this technology can evolve into a competitive edge for enterprises, offering substantial benefits that companies can harness. Its potential to foster innovation, enhance efficiency, and boost productivity resonates strongly across various sectors and industries.

3.1. Generative AI is poised to create maximum impact in multiple industries across a variety of use-cases.

Fig.2 Indicative generative AI use-cases in various industries

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According to McKinsey’s latest report, banking, high tech, and life sciences are among the industries that may experience substantial revenue impact from the adoption of generative AI. For instance, in banking, full implementation of the technology across various applications could add up to $200 billion to $340 billion annually. Similarly, the retail and consumer packaged goods sectors could also see an increase in productivity by 1.2 to 2.0% of annual revenues, or about $400 billion to $660 billion additionally.2

A recent study by IDC found that the global datasphere is expected to grow from 64.2 zettabytes in 2020 to 180 zettabytes in 2025.3 The explosion of AI applications happened with the introduction of math accelerators such as graphics processing units (GPUs), digital signal processors (DSP), field programmable gate arrays (FPGA) and neural processing units (NPUs), which drastically sped up data processing over CPUs. Unlike CPUs, these accelerators could process hundreds or thousands of threads in parallel.

Simultaneously, researchers gained access to vast amounts of training data through cloud services and public data sets; which further increased and improved the computational capabilities of LLMs.

Given its robust data processing prowess, generative AI represents a significant advancement over traditional data engineering methods. It introduces innovative capabilities with the potential to transform end-to-end data integration and management processes such as:

Generative AI algorithms can be used for seamless data integration, identifying relationships, mapping schemas, matching entities, deduplication, and harmonizing formats to create a unified data view. They can also facilitate real-time data integration through continuous processing of incoming data, granting data engineers deeper insights and enabling precise, timely decision-making.

Intelligent data integration

3.2. Value delivered by generative AI in data engineering

Source: Statista, Bernard Marr & Co.

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Generative AI can automate the entire data transformation process, encompassing data shaping, cleaning, structuring and transformation based on specific rules and algorithms. This reduces the manual intervention, accelerates data preparation, and ensures consistent, quality data fit for business requirements.

Generative AI enhances data access with intuitive user friendly interfaces and natural language processing capabilities. This allows business users to independently analyze data, thus promoting a data-driven culture across the enterprise.

Generative AI can improve data governance by analyzing dataset content, lineage and structure. It captures metadata and profiles data, generating descriptive summaries, quality metrics, and visual data representations. By analyzing the features and relationships within the data, generative AI models can categorize and segment datasets, ensuring data remains well-documented and traceable throughout its lifecycle.

Generative AI can automate the generation of workflow or workflow templates by training on historical data and workflow patterns. It can also assist in optimal task scheduling within data orchestration workflows. By analyzing error logs and historical data, generative AI models can identify common errors and offer recommendations to handle and recover from failures.

Automated data transformation

Enhanced data access

Improved data governance with metadata

Efficient data orchestration

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4. Applying generative AI to solve critical business problems

4.1. Conversational AI tool with semantic search for brand health tracking

Approach

Features

Many organizations already recognize generative AI as a powerful tool that can accelerate growth, enhance processes, and unlock new opportunities without drastic restructuring of business models. A global study by IDC reveals that nearly 80% of the 900 global executives surveyed have a high or significant level of trust in generative AI’s potential to benefit their company’s future offerings and operations.4

Here are a few use cases where Sigmoid made a significant impact in helping various companies achieve business growth through generative AI capabilities:

An NLP-based semantic search tool was developed to discover and assess what target consumers are saying on social media about brands, products or companies.

This tool was designed to extract data from various social media platforms and to distinguish relevant conversations from noise using keyBERT and similarity Tweet binning techniques.

User feedback was captured to identify relevant discussion drivers. The feedback could also be provided by users through voice commands. Tag generators were used to generate tags for tweets and the sentiment for each discussion driver was then analyzed. Key metrics were monitored and positive trends were alerted.

• Extensive data coverage

• Enhanced contextual search

• Trend capture and early alerts

• Granular drill-down views into multiple product lines and geographies

• Continuous monitoring and feedback for more accurate sentiment analysis

A leading healthcare company wanted to monitor relevant conversations on social media platforms and understand the underlying sentiment. They aimed to gain insights into upcoming trends.

However, social media listening poses various challenges. The platforms are noisy, and it is difficult to capture sentiments accurately. Exposure to content from competing brands on social media can cause rapid changes in consumer preferences. Existing tools may not provide a granular view across multiple geographies and product lines.

Situation

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4.2. Smart Q&A bot for precise enterprise knowledge retrieval

Approach

Features

We created an extractive question-answering AI bot, designed to provide precise answers to user questions, focusing on retrieving information from the enterprise’s knowledge corpus.

This self-service QnA bot delivers precise and contextually relevant answers from the provided documents, scanning them to find relevant information that directly addresses the user’s query. The tool was structured with four modules namely

1. Document selection and preprocessing

2. Query processing

3. Document searching and retrieval

4. Answer extraction

We utilized multiple open-source libraries to extract text from various file formats including PDFs, audio files, URLs, and more.

• Engaging, human-like responses to questions posed in conversational language

• Swift extraction of insights and trends from documents

• Intuitive self-service interface equipped with user-friendly tools, including a no-code setup to seamlessly integrate with current systems

A leading pharma company aimed to quickly analyze and summarize documents, whether they be legal documents, research papers, or technical manuals across multiple formats to extract the most important insights in a fraction of usual time. This would enable them to streamline internal enterprise work activities and improve efficiencies.

However, concerns about exposing sensitive data, and potential inaccuracies, posed challenges. Moreover, many available tools were not programmed to analyze and provide answers from diverse formats or information sources.

Situation

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4.3. Creative generation engine for marketing campaign optimization

Approach

Features

We designed a creative generation engine capable of producing high resolution customized visual assets based on specific prompts.

By offering multiple variations of product images, we enhanced personalization, increasing the potential for user engagement.

The tool uses campaign performance data, creative images and creative performance metrics. It leverages ranking and selection techniques to prioritize and generate the top creative for each campaign.

• Generate creative options with different product placements

• Create personalized ads to move away from the traditional mass-targeting approach

• In-built image editor with a prompt-based editing mechanism

• Creative insights dashboard to track performance along with a detailed performance driver analysis

• Compatibility with campaign automation platforms for easy uploading and user-control

A personal care brand aimed to launch targeted marketing campaigns featuring personalized content and creative images. The main objective was to run high-performing ads within the target audience for the entire line of products.

To address the need to optimize marketing budgets, tight timelines, and the challenge of personalizing ads for every consumer, the brand sought a tool to optimize their campaigns for a high ROI.

Situation

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5. Conclusion Generative AI stands at the forefront of technological advancements, offering transformative solutions across various industries. The potential of generative AI to drive value, foster innovation, and enhance efficiency is undeniable. However, to transition from mere experimentation to a potent catalyst for business growth with a substantial return on investment, organizations must navigate a myriad of challenges and be vigilant in addressing the associated risks.

This journey involves pinpointing opportunities within the organization, establishing a clear governance and operating models, efficiently managing third-party relationships (e.g., cloud and language model providers), tackling multiple risks, assessing the impact on people and technology, while striking a balance between short-term gains and the essential long-term foundations for scaling.

In conclusion, generative AI is not just a powerful tool but also a catalyst for change. It holds the promise of reshaping industries, redefining business models, and reimagining the future. Organizations that approach generative AI with a clear vision, a sense of responsibility, and an openness to exploration will undoubtedly lead the way in this exciting new era of innovation.

References: 1. Generative AI: From buzz to business value by KPMG

2. The economic potential of generative AI: The next productivity frontier by McKinsey & Company

3. Worldwide IDC Global DataSphere Forecast, 2022–2026: Enterprise Organizations Driving Most of the Data Growth

4. The Possibilities and Realities of Generative AI by IDC, sponsored by Teradata

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Learn more about our pre-built solutions to drive higher business efficiencies.

Visit www.sigmoid.com

About the author:

Aleksandar Lazarevic, PhD is a consulting partner at Sigmoid. He is an award-winning AI / Data Science executive with over 24 years of experience in applying AI and data analytics in various industries ranging from healthcare, smart manufacturing, computer security, food, banking, credit and insurance. He has previously led many analytics initiatives at Hello Fresh, Stanley, Black & Decker, Aetna, Travelers and Raytheon. He is also the founder of AI&DA Insights, an analytics consulting company. Over the last several years, he has driven over $600 million in value for many Fortune 500 companies by focusing on discovery of business opportunities, seamless technical delivery and building high performing teams. He has been continuously recognized for his scientific and leadership work in this area, recently voted a Top 10 Data and Analytics Leader for North America.

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The future of innovation and productivity is evolving faster than ever before, and generative AI is leading the way. Our vast experience in data engineering and enterprise grade AI solutions empowers businesses with data platforms that form the bedrock for developing and deploying generative AI capabilities at scale.

We have developed several pre-built generative AI solutions that are helping businesses across customer operations, marketing, software engineering and HR leading to cost savings, operational efficiencies, and increased revenue.

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Sigmoid enables business transformation using data and analytics, leveraging real-time decisions through insights, by building modern data architectures using cloud and open source. Some of the world’s largest data producers are engaging with Sigmoid to solve complex business problems. Sigmoid brings deep expertise in data engineering, artificial intelligence, and DataOps.

Sigmoid helps CPG companies improve marketing measurement and optimization, enhance demand forecasting accuracy, and save costs through inventory planning with data engineering and AI solutions. We help CPG enterprises define analytics strategy, accelerate cloud data modernization and enable the integration of data from multiple sources while improving data quality.