Generative AI can help ensure that large-scale projects such as the Olympics not only meet but exceed operational expectations while staying within budget. In our discussions with Fortune 100 clients, we at Tiger Analytics have observed a significant interest in GenAI adoption specifically for project management. Infusing GenAI in project management allowed for greater efficiency, and right decision-making, along with improved adherence to project timelines. Here, we explore specific areas where GenAI can be implemented and how project managers can make the most of it.

The GenAI Advantage in Project Management and Beyond

About 67% of AI decision-makers surveyed by Forrester’s May 2024 Artificial Intelligence Pulse Survey said their organization plans to increase investment in Generative AI in the coming year. The tech industry is at the forefront of GenAI adoption, followed by the banking, retail, industrial goods, and healthcare industries. Driving this adoption are the opportunities presented by GenAI, such as transforming customer engagement, boosting productivity, improving competitive agility, reducing tech debt, and optimizing workforces and supply chains.

However, in order for organizations to really reap benefits of GenAI, they must understand its strategic impact and make decisions that balance risk with the right business reward. It is also crucial that organizations prioritize ethical considerations, such as data privacy, transparency, and the potential for bias. Having worked with multiple clients on building and deploying projects, we’ve seen that GenAI has a direct impact on key challenges faced by project managers, such as scope creep, lack of communication, budgeting issues, inadequate resource management, and content creation.

While needs would vary depending on the industry, here are some industry-agnostic GenAI use cases for project management tasks.

A Guide to Integrating GenAI into Project Management

GenAI applications based on task complexity

GenAI can be initiated into project management across three broad complexity levels, ranging from fully automated tasks to advanced decision-making support. As discussed in an earlier article, adopting GenAI isn’t just about improving individual tasks; it equips employees with the tools to excel in their roles, driving greater overall workplace efficiency. Here’s how GenAI capabilities can be applied at each level:

1. Low Complexity Tasks – GenAI can handle repetitive, time-consuming activities that require very low human intervention, freeing PMs to focus on higher-level decision-making.

Examples:

• Obtain summary from customer Master Service Agreement (MSA) and Statement of Work (SOW) to understand specific clauses like penalty, volume discounts, contract to hire, agreed milestones/rate cards etc.
• Create summary from recordings of onsite meetings or meeting notes
• Create lessons learned summary after each release
• Create dashboards (from JIRA boards, efforts/ tasks management)

For instance, we collaborated with a global private equity firm to leverage GenAI capabilities like summarization and insights as part of setting up their Analytics Centre of Excellence (CoE). Adopting GenAI helped them enrich knowledge graph embeddings, enhance operational efficiency, and transform decision-making. Such capabilities can be extended to project management tasks as well.

2. Medium Complexity Tasks – At this level, GenAI complements the work of project managers with partial automation such as providing drafts, analyses, and data sets that PMs can expand upon. These tasks require human oversight, but benefit greatly from GenAI’s data processing power.

Examples:

• Large dataset analysis
• Risk analysis of past projects that are of similar nature
• Cost & schedule analysis and estimation

3. High Complexity Tasks – For complex, strategic tasks, GenAI enhances existing capabilities and supports the project manager’s experience and expertise. It can provide initial insights, while the PM’s judgment shapes final decisions.

Examples:

• Project scope management / business justification
• Project decision making

GenAI applications in the project management lifecycle

The project management lifecycle’s five distinct stages – Initiation, Planning, Execution, Monitoring & Controlling, and Closure – provide a systematic approach to a project. Leveraging GenAI across each stage helps project managers unlock new ways to optimize workflows and improve outcomes. Let’s explore how GenAI can be used in one stage of the lifecycle:

Final Note

GenAI’s impact across industries is undeniable with early adopters reporting quicker and greater returns on investment. It can simplify and enhance project management by improving project planning, task automation, effective resource allocation and risk management, etc. A significant sign of rapid GenAI adoption in this space is that most project management market tools now offer GenAI-based versions. For instance, Atlassian has developed multiple GenAI features as part of Atlassian Intelligence to help derive insights from JIRA data.

That said, it’s important to ensure that the use of GenAI aligns with the organization’s technical and ethical standards, as well as the values of the customer environment. Project managers should be cautious not to share customer-specific data, SOWs, MSAs, or project-related artifacts in public GenAI environments. Additionally, data privacy, security, potential biases, and the limitations of predictive models must be carefully managed. With the right oversight and expertise, project managers can leverage GenAI’s capabilities responsibly, ensuring its full potential is realized.

As GenAI continues to evolve, its applications in project management are also expected to expand and generate more opportunities to bring efficiency and success.

The post From the Olympics to Product Releases, How Project Managers Can Go for Gold with GenAI appeared first on Tiger Analytics.

Three Roadblocks on the Journey from Data-to-Insights

In our work with several Fortune 500 clients across domains, we’ve observed the path to actionable insights extracted from data is hindered by a trifecta of formidable bottlenecks that often prolong time to value for businesses.

• The pressing need for personalized insights tailored to each user’s role
• The escalating complexities of dashboard development, and
• The constant stream of updates and modifications required to keep pace with evolving business needs

As companies navigate this challenging landscape, the integration of Generative AI (GenAI) into BI processes presents a promising solution, empowering businesses to unlock the true potential of their data and stay ahead in an increasingly competitive market.

Challenge 1: Lack of persona-based insights

Every user persona within an organization has different insight requirements based on their roles and responsibilities. Let’s look at real-world examples of such personas for a CPG firm:

• CEOs seek insights into operational efficiency and revenue, focusing on potential risks and losses
• Supply Chain Managers prioritize information about missed Service Level Agreements (SLAs) or high-priority orders that might face delays
• Plant Managers are interested in understanding unplanned downtime and its impact on production

Hence, the ability to slice and dice data for ad-hoc queries is crucial for gaining technical know-how. However, the challenge lies in catering to these diverse needs while ensuring each user gets relevant insights tailored to their roles. Manual data analysis and reporting may not pass the litmus test, as it can be too time-consuming and may not be able to provide granularity as desired by the key stakeholders.

Challenge 2: Growing complexities of dashboard development

Creating multiple dashboards to meet the diverse needs of users requires a lot of time and effort. It typically involves extensive stakeholder discussions to understand their requirements, leading to extended development cycles. The process becomes more intricate as organizations strive to strike the right balance between customization and scalability. With each additional dashboard, the complexity grows, potentially leading to data silos and inconsistencies. Dependency on analysts for ad-hoc analysis also causes more delays in generating actionable insights. The backlog of ad-hoc requests can overwhelm the BI team, diverting their focus from strategic analytics.

Managing various dashboard versions, data sources, and user access permissions adds another layer of complexity, making it difficult to ensure consistency and accuracy.

Challenge 3: Too many updates and modifications

The relentless need to update and modify the dashboard landscape puts immense pressure on the BI teams, stretching their resources and capabilities. Rapidly shifting priorities and data demands can lead to a struggle to align with the latest strategic objectives. Also, constant disruptions to existing dashboards can create user reluctance and hinder the adoption of data-driven decision-making across the organization.

Plus, as businesses grow and evolve, their data requirements change. It leads to constant updates/modifications– triggering delays in delivering insights, especially when relying on traditional development approaches. As a result, the BI team is often overwhelmed with frequent requests.

Empowering BI through GenAI

What if anyone within the organization could effortlessly derive ad-hoc insights through simple natural language queries, eliminating the need for running complex queries or dependence on IT for assistance? This is where the integration of GenAI and NLP proves invaluable, streamlining information access for all key users with unparalleled ease and speed.

At Tiger Analytics we developed Insights Pro, a proprietary GenAI platform to overcome these challenges and deliver faster and more efficient data-to-insights conversions.

In a nutshell, by generating insights and streamlining BI workflows, Insights Pro takes on a new approach. Rather than contextualizing data using data dictionary, it leverages the power of LLMs for data dictionary analysis and prompt engineering, thus offering:

• Versatility – Ensures superior data and domain-agnostic performance
• Contextuality – Comes with an advanced data dictionary that understands column definitions and contexts based on session conversations
• Scalability – Spans across different user and verticals

This democratizes access to data-driven insights, reducing the dependency on dedicated analysts. Whether it’s the CEO, Supply Chain Manager, or Plant Manager, they can directly interact with the platform to get the relevant insights on time and as needed.

Empowering Data-Driven Decision-Making | Applications across various industries

Logistics and Warehousing: AI powered BI solutions can assist in optimizing warehouse operations by analyzing shipment punctuality, fill rates, and comparing warehouse locations. It identifies areas for improvement, determines average rates, and pinpoints critical influencing factors to enhance efficiency and streamline processes.

Transportation: Transportation companies can evaluate carrier performance, identify reasons for performance disparities, and assess overall carrier efficiency. It provides insights into performance gaps, uncovers the causes of delays, and supports informed decision-making to optimize transportation networks.

Supply Chain Management: AI powered BI solution empowers supply chain leaders to identify bottlenecks, such as plants with the longest loading times, compare location efficiency, and uncover factors impacting efficiency. It guides leaders towards clarity and success in navigating the complexities of supply chain operations, facilitating data-driven optimization strategies.

Business Intelligence and Analytics: Analysts are equipped with a comprehensive view of key metrics across various domains, such as shipments across carriers, order-to-delivery times, and modeling to understand influencing factors. It bridges data gaps, simplifies complexities, and offers clarity in data analysis, enabling analysts to derive actionable insights and drive data-informed decision-making.

Undeniably, empowering BI through AI can only be achieved by knocking off time-consuming bottlenecks that hinder data-to-insights conversion.

Tiger Analytics’ Insights Pro also goes a long way to combat other challenges that Generative AI has been associated with at an enterprise level. For instance, it ensures that data Security concerns are uploaded to the GPT server as data dictionaries. It also delivers an up-to-date data dictionary so that new business terms shouldn’t be manually defined in the current session.

Looking ahead, NLP and GenAI-powered solutions will break down barriers to data accessibility, automate the discovery of hidden patterns empowering users across organizations to leverage data insights through natural language interactions. By embracing solutions like Insights Pro, businesses can unlock the value of their data, drive innovation, and shape a future where data-driven insights are accessible to all.

The post Empowering BI through GenAI: How to address data-to-insights’ biggest bottlenecks appeared first on Tiger Analytics.

Chat Intelligence: When Chat Meets GenAI and NLP

CHAT INTELLIGENCE refers to our specialized technology and solutions that leverage NLP and GenAI capabilities. At its core, Chat Intelligence encompasses the use of advanced AI-driven algorithms to enhance chat and messaging systems. These systems can understand, interpret, and generate human-like text, based on natural language input, resulting in more sophisticated and valuable user interactions.

Chat intelligence helps drive incremental business opportunities by identifying:

• New leads for businesses
• Signals from existing customers for additional business opportunities
• Potential customer dissonance triggering retention measures
• Themes for personalized marketing campaigns
• Upsell or Cross-sell opportunities
• Indicators or patterns that lead to fraud
• Customer retention strategies
• Customer sentiments

From Chat Conversations to Business Insights

For businesses aiming to integrate chat intelligence into their operations, the significance of chat mining cannot be overstated. Chat mining, a fundamental aspect of chat intelligence, entails the extraction of valuable insights from chat data. This process involves analyzing text conversations to decipher customer preferences, behaviors, and sentiments, utilizing the extensive data generated from interactions between customers and chatbots or virtual assistants. By converting this data into actionable intelligence, chat mining becomes a critical tool for businesses focused on enhancing customer experience, optimizing operations, and making informed strategic decisions.

Despite its potential, chat mining faces several challenges, particularly when relying on traditional NLP techniques:

• Limited Contextual Understanding: Traditional approaches like TF-IDF and Word2Vec for feature extraction often struggle to grasp the full context of conversations. This can lead to misunderstandings of customer intent and sentiment, impacting the quality of insights derived from chat data.
• High Computational Requirements: Processing and analyzing large volumes of chat data require significant computational resources. Traditional models, while effective for simpler tasks, can become inefficient and costly at scale.
• Evolving Language and Slang: The dynamic nature of language, including the use of slang and new expressions in chat interactions, poses a challenge for static models that are not continuously updated.

Overcoming Challenges with Deep Learning and Ablation Analysis

To address these challenges, there has been a shift towards leveraging the power of deep learning. At Tiger Analytics we use models like the Universal Sentence Encoder (USE) and Bidirectional Encoder Representations from Transformers (BERT). These iterations represent a significant departure from traditional approaches, offering enhanced contextual understanding and reduced computational burdens.

• Deep Learning Iteration-1 (USE Embeddings + Classifier): The first iteration involves using USE embeddings, which provide a more nuanced capture of semantic information in chat conversations. This approach marks an improvement over TF-IDF by incorporating a broader context.
• Deep Learning Iteration-2 (Fine-tuned BERT Model): The second iteration advances further with the adoption of a fine-tuned BERT model. BERT’s ability to understand the bidirectional context of words in sentences significantly enhances the model’s performance in chat mining tasks.

The Crucial Role of Ablation Analysis

Ablation analysis is a methodical approach to improving chat intelligence systems by systematically removing components, such as layers, neurons, or specific features, to study their impact on the model’s performance. This process helps identify which elements are crucial for the success of the model and which might be redundant or detrimental. The analysis provides insights into how different NLP and AI techniques contribute to the system’s ability to understand and generate language, offering a deeper understanding of the underlying mechanisms.

Ablation analysis becomes particularly valuable in refining deep learning models for chat intelligence. By systematically removing or modifying components of these complex models, researchers and developers can:

• Identify Key Features: Determine which features or model components are most influential in understanding and generating chat-based interactions.
• Optimize Model Performance: Enhance the accuracy and efficiency of chat intelligence systems by focusing on essential elements.
• Reduce Computational Costs: Eliminate unnecessary or less impactful components, thereby streamlining the model for better scalability and reduced operational expenses.

Ablation Analysis illustrated through a series of examples

In the first example “Hi, I am considering moving all my accounts held at an outside firm to your firm.”, the indication of the movement of money from external firms is clear and all the three models are able to pick up the signal of an incoming transfer.

In the second example, “Hello, I am considering moving my account to a different firm.”, the TF-IDF model and the USE embeddings-based model were not able to understand the nuances of the sentence. These are the typical false positives that the model struggled to differentiate:

In the third example, “May I get some help. I am looking to open a new account and start contributing to it.”, the TF-IDF and USE model’s output probabilities are below the threshold and hence are lost opportunities. However, the BERT model’s fine-tuning helps rightly identify this as a valid lead. This leads to a higher volume of leads and minimizes missed opportunities.

The journey towards achieving excellence in Chat Intelligence is both challenging and rewarding. At Tiger Analytics, we are committed to leveraging the latest advancements in NLP and AI to offer solutions that meet the unique needs of our clients. Our expertise in chat mining and the strategic application of deep learning models and ablation analysis have enabled us to unlock new levels of efficiency, insight, and customer engagement. As we continue to innovate and explore the vast potential of chat intelligence, we invite you to delve deeper into our findings and methodologies.

For a more comprehensive understanding of we’ve used ablation analysis and fine-tuned BERT models to build a help extract chat intelligence from conversational data, read our whitepaper- How NLP and Gen AI are helping businesses derive strategic insights from chat conversations

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The scope for Generative AI to transform the retail value chain goes beyond forecasting and managing customer demand during major shopping seasons – although those are significant milestones in every organization’s retail calendar. Its real potential lies in tapping into generative capabilities reshaping the entire customer journey.

From sales records to customer preferences, retail brands are data goldmines. By fusing foundational language models with this wealth of information, retailers can harness Generative AI to craft personalized shopping experiences or improve business processes like never before:

• Customer support and assistants through improved LLM-based chatbots
• Intelligent search and summarization for inquiries and sales
• Consistent product descriptions generated through AI
• Synthetic inventory data generation to simulate supply chains
• Streamline the process of product development
• Label generation enhanced accuracy
• Personalized promotions through text and image generation

Building and operationalizing a bespoke solution using foundational AI models requires several components and enablers to be successful. Components for prompt engineering, dialog management, and information indexing are necessary to extract the best out of an LLM. When coupled with various NLP Accelerators such as document parsing, speech-to-text, and text embedding an end-to-end solution can be developed and deployed.

At Tiger Analytics, we’ve worked with various retail clients to elevate retail CX and work productivity and CRM with an AI/ML-driven customer data garden, streamlining and automating targeting models. Here are our observations

Streamlining Product Descriptions for Better Consistency and Cost Savings

Writing product descriptions for the entire catalog of products is a time-intensive and manual activity for most retailers. Add to this, the variations in consistency in terms of tone of writing style across different departments and countries make this a difficult problem to solve. Retailers need to ensure that the descriptions are relevant and concise to facilitate more conversions. They also need to keep the writing consistent across their e-commerce portals, campaigns, and digital content.

Generative AI can make this process smoother while being more cost-efficient. Customized LLMs can be trained to generate automated descriptions based on product attributes and specifications. Content can be standardized to the company’s style and tone for use across media. For retailers with an evolving product portfolio, this becomes a more scalable way to write and maintain product descriptions. Such a solution can be developed by fine-tuning a foundational LLM such as GPT, T5, or BART with annotated product data, product catalogs, and relevant SEO keywords. By incorporating human feedback, the descriptions can be further tailored to specific styles and needs.

Illustration for Consistent Product Description Solution

Customer Support with Better Understanding and Efficiency

The biggest problem with chatbots before LLMs was that they could not converse in natural language. This led to frustrating experiences for users who would eventually give up on using the bot. As many of the bots in the past were not well-linked with human agents, it led to low customer satisfaction and churn.

LLMs are a perfect solution to this problem. Their strength lies in generating natural language conversational text. They are also good at summarizing vast amounts of text into concise and understandable content. To develop a customer assist solution that works, retailers can deploy LLMs in key parts of the process:

1. Converting user speech to text
2. Summarizing the user query
3. Relaying summarized information to the user
4. Helping support agents query large amounts of information and generate concise responses.

LLMs need to be used in conjunction with components such as dialog management and work on top of issues, orders, and product data to deliver contextual responses to user queries. Due to the advanced context retention capabilities of LLMs, conversations can naturally progress with continuity, allowing for in-depth dialogue over an extended interaction and the context of the user’s query can be inferred clearly. This enhances customer interaction dramatically and can make the entire support process both effective and cost-efficient.

Illustration for Customer Assist Solution

Enhanced Sales and Customer Engagement with Virtual Shopping Assistant

Generative AI has the potential to personalize the customer journey across various touchpoints, creating a seamless and engaging experience. Imagine a shopper browsing through an online store, encountering suggestions that not only match their preferences but also anticipate their desires. The assistant doesn’t merely suggest; it understands, learns, and grows with the customer. By leveraging cross-category targeting and Next Best Action (NBA) strategies for existing customers, the assistant becomes a companion in the shopping adventure, guiding with insight and relevance.

Illustration for Virtual Shopping Assistant Solution

Beyond mere navigation and suggestions, the Virtual Shopping Assistant can also be leveraged as a smart chatbot to answer any product-related questions while browsing the website. To bring this vision to life, Generative AI can be customized and fine-tuned using detailed product catalogs, customer interaction data, and behavioral insights. By incorporating human feedback and integrating it with existing systems, the Virtual Shopping Assistant can be molded to reflect the retailer’s brand, tone, and values.

Synthetic Inventory Data Generation Boosting Agility and Insight

Managing inventory data is a complex and time-consuming task for retailers, often fraught with inconsistencies and challenges in scaling. Large Language Models (LLMs) can analyze extensive inventory data, identifying trends and patterns. This allows for the creation of realistic and relevant synthetic data without revealing sensitive information, providing both privacy and comprehensive testing capabilities.

With LLMs, retailers can gain control over the data generation process, enabling augmentation and diverse scenario creation. By fine-tuning LLMs with actual inventory data and incorporating human feedback, retailers can craft a system that aligns with their unique requirements. Generative AI’s ability to produce synthetic inventory data is not just a technological advancement; it’s a strategic asset that empowers retailers to be more agile, insightful, and effective.

Illustration for Synthetic Inventory Data Solution

Quick and Market-Aligned Product Generation

In the realm of retail, manual product development is a time-consuming and resource-intensive process. The challenges extend from heavy reliance on manual efforts by designers and stakeholders to the uncertainty in market success due to fluctuating customer demand, competition, and trends. The future state of product generation, however, offers transformative possibilities. By automating concept creation, design exploration, and prototyping, retailers can accelerate product development. This shift towards data-driven decision-making and key metrics identification further refines design choices and mitigates market risk.

Illustration for Product Generation Solution

The journey from concept to product can be streamlined through AI-driven stages such as generating product concepts, evaluating, refining, and iterating designs, and prototyping and testing. By leveraging customer data and market insights, retailers can create products that truly resonate with their audience. The ability to fine-tune the development process with actual market insights and human feedback aligns product creation with customer demand. This empowers retailers to be more innovative, efficient, and aligned with the ever-changing market landscape.

Generating Labels with Enhanced Accuracy, Brand Consistency, and Compliance

In the current retail landscape, generating labels is a labor-intensive process, marked by time-consuming efforts from graphic designers and product managers. Limited customization, error-prone procedures, and numerous iterations not only hinder efficiency but also pose risks to accuracy and compliance. This complexity impacts both time and flexibility, making label design a challenging task.

The future, however, presents an exciting transformation. Leveraging AI for rapid iterations, customization, and consistency opens doors to significant time and resource savings. The ability to offer scalability for large catalogs, ensure accuracy, maintain brand consistency, and comply with regulations is more than an efficiency gain; it’s a strategic advantage. By automating the design process and focusing on the creative aspects of label design, retailers can elevate their brand’s identity and engage with their audience in a more meaningful way.

Illustration for Generating Labels Solution

Personalized Promotion for Enhanced Customer Engagement

Creating personalized promotions has traditionally been a manual, error-prone process. Manual analysis and segmentation of customer data can lead to limited insights, inefficient promotion design, and static promotions that lack relevance. The challenges in uncovering subtle customer preferences make it difficult to deliver truly personalized experiences.

The future state of personalized promotion, driven by AI, offers a transformative approach. Automated customer segmentation, real-time personalization, and adaptive promotions bring accuracy and dynamism. This shift not only improves efficiency and maximizes ROI but also ensures a seamless and cohesive customer experience throughout the shopping journey. By focusing on real-time insights and multichannel personalization, retailers can connect with customers in more meaningful ways, enhancing engagement and loyalty.

Illustration for Personalized Promotion Solution

The emergence of Generative AI in retail signals a transformative era, offering immense potential to enhance every aspect of the retail value cycle. From creating more engaging customer experiences to optimizing supply chain management, the applications are vast and varied. Retail leaders who leverage these technologies can significantly improve operational efficiencies, personalize customer interactions, and stay agile in a dynamically evolving market. By harnessing the power of Generative AI, retailers are not just adapting to current trends; they are actively shaping the future of retail, paving the way for innovative approaches and sustainable growth in an increasingly digital world. Now is a pivotal moment for industry leaders to explore and invest in these advanced capabilities, ensuring they remain at the forefront of retail innovation and excellence.

The post Beyond Bargains: 7 Powerful Ways Retail Leaders Can Use Generative AI to Level Up Their Retail Value Cycle appeared first on Tiger Analytics.

Fast forward a few months later, and Generative AI has already begun taking over the narrative where digital transformation is concerned.

Today there’s an exciting opportunity for enterprises to augment Discriminative AI with Generative AI. Prompt engineering and fine-tuning of foundational Large Language Models (LLMs) with enterprise data can enable insurance companies to reinvent most parts of their value chain.

NLP + Generative AI = The Barbenheimer effect?

It’s important to note that over time NLP has progressed from simply understanding and analyzing text to generating intelligent and contextually appropriate responses, summaries, etc. Traditionally, it focused on tasks such as text classification, sentiment analysis, and information extraction.

Traditional NLP and Generative AI, each on their own, are capable of providing immense value to insurance companies. But by combining forces, Generative AI and NLP have now enabled machines to create human-like text and generate meaningful responses. Together, they can empower insurance teams to tackle foundational and complex tasks with newfound intelligence.

Enterprises have a huge opportunity to customize the available foundational models to realize value from a broad range of capabilities that Generative AI offers.

At Tiger Analytics, we use the following approaches based on our clients’ use cases:

• Prompt Engineering using Enterprise-grade Generative Models – e.g.: Chain of thought, zero-shot examples, few-shot examples
• Prompt tuning using any of the open-source LLM models – e.g.: Prompt tuning, Prefix tuning, and PEFT
• Fine-tuning an open-source LLM model for a specific task – e.g.: Causal Language Modeling (CLM) and Masked Language Modeling (MLM) on objectives like summarization, code generation, etc.
• Building a foundational LLM model with multiple tasks capability using RLHF loop.

4 Process Wins for the Insurance Industry

We’ve worked on Generative AI projects involving search and summarization, description generation, and next-gen chatbots for various clients. Here are four processes that Generative AI + NLP can help transform:

Process Win #1: Pricing and Underwriting

With automated information gathering and data entry, insurers can now expedite the search process, leading to faster and more accurate access to crucial data points — and it’s all thanks to Generative AI.

Furthermore, Generative AI’s intelligent search and summarization capabilities enhance risk assessment by swiftly analyzing vast amounts of information and extracting key insights. This streamlines the underwriting process while improving decision-making accuracy.

Generative AI also enables automated documentation through description generation, reducing the need for manual report writing, and thereby reducing the risk of human errors. As a result, insurers can make better pricing and underwriting decisions.

Process Win #2: Claims Processing

Traditionally, adjusters would spend hours manually reviewing adjuster notes and other supporting documents, resulting in delays and potential claims leakage. However, with Generative AI, the game changes. Through automated information gathering and data entry, insurers can leverage faster search capabilities to access relevant information swiftly. Market studies indicate that Generative AI can reduce the time spent reviewing claim files to less than an hour.

Generative AI’s intelligent search and summarization capabilities also go a long way to enable adjusters to extract and analyze key insights, minimizing the risk of claims leakage. This, coupled with a sophisticated Next Best Action (NBA) model, helps adjusters make settlement decisions quickly and scientifically. The impact is profound — insurers can now streamline claims processing, ultimately reshaping the claims landscape in the insurance industry.

Process Win #3: Contact Center Operations

Generative AI can help overcome many current challenges such as the limited conversational abilities of chatbots and the need for agents to navigate multiple systems and documents. With the implementation of a conversational agent, a major portion of customer queries can be self-served, resulting in reduced resolution times. Even more complex queries can be seamlessly routed to agents, ensuring personalized customer experiences.

Also, Generative AI can empower agents with intelligent search and summarization capabilities, making sure they can access relevant information and deliver accurate responses.

Process Win #4: Marketing and Distribution

Traditional chatbots often struggle to provide natural language conversations, hindering their ability to answer product queries. But by working with Generative AI, the effectiveness of sales agents can be improved, equipping them with quick access to relevant information and enabling them to provide accurate and personalized responses. Generative AI can help address critical marketing and distribution challenges for enhanced customer engagement.

Generative AI facilitates better explanations of policies and products through automated description generation, reduces administrative burden, and frees up valuable time for sales agents. Ultimately, insurers are equipped with the technology firepower to deliver consistent and contextual customer journeys across touchpoints, creating tailored experiences that align with individual preferences.

Just as the iPhone revolutionized human interaction with technology, Generative AI is poised to revolutionize the insurance landscape by simplifying and accelerating the digital transformation journey for insurers. Paired with NLP capabilities, it will help change the way insurers automate and expedite key workflows, ranging from risk assessment to claims processing and underwriting. Our brave new world is here.

The post AI-Powered Insurance Wins: Unlocking Process Efficiencies with NLP and Generative AI appeared first on Tiger Analytics.

“Hey there, fellow curious minds! Today, I want to take you on an exhilarating journey through the incredible world of Generative AI. From its humble beginnings to its mind-boggling present, this remarkable field of artificial intelligence has revolutionized the way we create, imagine, and dream. As an AI language model myself, I’ve had a front-row seat to witness the awe-inspiring evolution of Generative AI. So buckle up, get ready to be amazed, and let’s embark on a captivating adventure through time to explore the mind-bending capabilities of this cutting-edge technology. Get ready to have your imagination stretched to new limits!”
ChatGPT-4

 
This month, last year –  2022, the world had no idea of the Generative AI (Gen AI) revolution that was about to transform the way content was created and generated. Fast forward to November, Open AI launched the AI chatbot ChatGPT and ushered in a new age of communication technology. 

Over the past decade, we have seen that the advancements in the world of AI have not just evolved but leapfrogged, making it an integral part of our daily lives, from virtual assistants like Siri and Alexa to self-driving cars and drones to generating convincing stories and lifelike images.

So how did it all begin?

The history of AI spans over nine decades, from the early attempts at creating machines that mimic human intelligence to the recent breakthroughs in deep learning and generative models.

During the early days, rule-based or keyword approaches were prevalent, which evolved into more complex machine learning algorithms that can learn from data and improve over time. In recent years, deep neural networks like RNN, CNN, and LSTMs have emerged as powerful techniques for training models to recognize patterns in data. This has led to breakthroughs in areas like computer vision, speech recognition, and natural language processing. 

Major strides were made in the field of Generative AI through robust architectures like transformers which enabled transfer learning. This breakthrough has facilitated the seamless transfer of knowledge from one system to the other. Advances in computing power and the availability of large datasets to train these transformers have made them powerful, enabling the potential to leverage Generative AI in industrial applications. 

Over the years, at Tiger Analytics, we helped companies embrace AI and machine learning capabilities enabling them to find innovative ways to improve business performance, efficiency, speed, and consistency of service. Now with the advent of Generative AI, we are seeing a shift in industry expectations and the evolution of solutions across different use case scenarios. 

Here’s a side-by-side comparison:

AI has continued to transform businesses and the way they operate on a day-to-day basis. Generative AI has now begun rewriting the rules of the game. As companies rush to become early adopters, working with the right team and the right advisors who understand the technology’s potential to deliver value will help shape critical decisions.

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