Boost Enterprise Productivity
with Generative AI
eBook
Image created by Generative AI
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
11 eBook | Boost Enterprise Productivity with Generative AI
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.
About Sigmoid
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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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