Author: altilia

In the financial sector, where large volumes of unstructured data and complex workflows are the norm, AI solutions can offer huge benefits: from automating manual processes to enhancing decision-making and ensuring compliance.

However, companies often face a critical choice when deciding to implement an AI solution for the first time. Generally, the decision is choosing between two distinct approaches: a “quick win” solution, which is relatively easy to deploy but offers limited long-term benefits, or a more structured, comprehensive solution, which requires greater effort and resources to implement but promises a significantly higher return on investment. Let’s dive deeper into each option and their implications.:

1. “Quick Wins” or Human Demanded Solutions:
   These are ready-to-use AI tools that operate based on explicit user input. They are fast to implement, cost-effective, and deliver immediate productivity boosts but limited to a specific task.
   • Pros: Quick setup, user-friendly, minimal data requirements.
   • Cons: Limited flexibility, lack of contextual understanding, not suitable for complex tasks.
2. Data Demanded Solutions:
   These leverage vast and diverse datasets to create tailored, strategic AI applications. While they take longer to implement, they provide advanced capabilities like predictive analytics and adaptive workflows.
   • Pros: High adaptability, personalized solutions, strategic business impact.
   • Cons: Requires high-quality data, longer development time, and greater investment.

Is it possible to combine the advantages of both approaches? Maybe it is.
In this article, we’ll explore a structured, five-step process for successfully implementing an AI solution that balances the immediate gains of “quick wins” with the enduring value of long-term strategic initiatives. To illustrate this process, we’ll follow a real-world example: a financial institution, “Bank X,” leveraging AI to automate its risk analysis and streamline operations.

 

Step 1: Define Your Objectives

Bank X, a fictional major financial institution, struggled with inefficiencies in evaluating credit risk. Analysts manually processed financial statements from various companies, often taking up to 3 days per report. Just this first step is creating a significant bottleneck in the whole process:

• Errors due to manual data extraction.
• Limited resources to allocate in the financial statement analysis, 
• Little time for complex but high-value tasks.

To solve these challenges, Bank X set a clear goal: Automate the extraction and analysis of financial statement data to reduce processing time and to improve the accuracy.

Approach breakdown

1. Analyze business pain points: Start by identifying critical inefficiencies. For Bank X, the repetitive nature of manual analysis was a prime candidate for automation.
2. Set measurable KPIs: Goals must be quantifiable to assess success. Bank X focused on two: Time reduction and the accuracy improvement in data extraction.
3. Evaluate use cases: While chatbots for customer support can be considered “quick wins”, Bank X pursued a data-driven AI approach for a lasting impact on risk management.

By defining precise objectives, Bank X clarified the scope of the project and the target outcomes, moving to the next phase: understanding the data required to meet these goals.

 

Step 2: Manage and prepare your data

The next challenge for Bank X was its large repository of unstructured data: PDFs, CSVs, and PowerPoint presentations filled with financial statements and scattered across different databases. Turning these documents into structured, machine-readable formats required:

1. Extracting key metrics: e.g.: revenue, debts, and cash flow indicators.
2. Ensuring data consistency: Validating and cleaning extracted data to ensure accuracy.
3. Building the infrastructure: Leveraging cloud solutions and advanced Optical Character Recognition (OCR) tools for scalability.
4. Gather all the data in one place: To create an efficient AI solution, data must be easy to access. 

Approach breakdown

1. Identify relevant data sources: Begin with a thorough audit of existing data—structured (databases) and unstructured (PDFs, reports). For Bank X, financial reports were the primary data source.
2. Clean and validate the data: High-quality data is crucial. Processes like deduplication, normalization, and error-checking help prevent downstream issues.
3. Infrastructure readiness: Robust infrastructure, such as a cloud platform, ensures the AI system can process large volumes of documents efficiently without worrying of the maintenance or updates of the infrastructure .

By transforming unstructured financial data into clean, structured formats and a robust infrastructure, Bank X created the necessary foundations for the next step: training the AI model to analyze this data effectively.

 

Step 3: Select and train your model

With a structured dataset in place, Bank X implemented an AI solution combining:

1. Choosing the right technology: A pre-trained OCR model for extracting text across different file formats and a fine-tuned AI model for summarizing key financial metrics.
2. Training and Fine-Tuning: Historical financial reports were used to train the AI, teaching it to identify patterns across balance sheets and income statements.
3. Testing and Validation: Outputs were rigorously validated by human reviewers to minimize inaccuracies.

Approach breakdown

1. Choose the right AI model:
   • Pre-trained models (like OCR) to have a generic tool that can be used immediately.
   • Fine-tuning custom models are best suited for specific business requirements such as summarizing financial reports from a specific format.
2. Train and validate: Use historical data to “teach” the AI. Human oversight ensures that the AI output aligns with business standards.
3. Iterate and improve: Continuously test and refine the model to ensure optimal accuracy and reliability.

 

Step 4: Test your model thoroughly

Even the most advanced AI models can produce errors or unexpected results if not rigorously tested. Testing ensures the model’s accuracy, reliability, and scalability, reducing risks before deployment. A robust testing process uncovers weaknesses, such as biased outputs or performance bottlenecks, allowing adjustments that ensure the solution works effectively in real-world conditions.

Best practices for testing an AI model

1. Start with small, controlled data sets: Use a smaller, representative subset of your data for initial tests to quickly identify potential issues without overwhelming the system.
2. Evaluate against benchmarks: Compare the model’s performance to established metrics, such as:
   • Accuracy: The proportion of correctly predicted outcomes compared to the total number of predictions, indicating overall performance.
   • Precision: The proportion of true positive results among all positive results predicted by the model, highlighting how relevant the predictions are.
   • Recall: A metric used to evaluate the performance of a machine learning model, especially in classification tasks. It measures the model’s ability to correctly identify all relevant instances (true positives) from the dataset, showing how well the model identifies relevant instances.
   • Latency: The time it takes for the model to process an input and produce an output, reflecting the system’s responsiveness.
3. Incorporate real-world scenarios: Simulate actual business use cases, such as processing financial statements with varying formats and quality, to evaluate performance in realistic conditions.
4. Stress-test for scalability: Assess how the model performs under heavy workloads, such as analyzing hundreds of reports simultaneously, to ensure it remains reliable as usage scales.
5. Monitor for bias and hallucinations: Examine outputs for accuracy, fairness, and relevance. Address any biases introduced by the data or inconsistencies in responses.
6. Involve human oversight: Have subject-matter experts review test results to spot nuanced errors and provide feedback for fine-tuning.

Now, the trained and tested AI model produced accurate financial insights that could now be integrated into Bank X’s risk management workflows.

 

Step 5: Integrate and monitor the solution

With the AI model ready, Bank X integrated it into their existing risk management systems:

1. Deployment: The AI solution connected to real-time data feeds, automating the analysis of incoming financial reports.
2. Performance Monitoring: KPIs were tracked to ensure continuous improvement:
   • Processing time was reduced and now it’s no more an operative bottleneck.
   • Data accuracy was consistently high.
3. Team Training: Analysts were trained to leverage AI outputs, shifting their focus to interpreting complex cases.

Approach breakdown

To successfully deploy the solution, it was essential to ensure a seamless integration with existing systems, minimizing any potential disruptions to established workflows. Once implemented, performance metrics must be continuously monitored, focusing on real-time KPIs such as accuracy, processing speed, and user adoption rates, to evaluate the solution’s effectiveness. Additionally, adopting a framework of continuous learning allowed models to be updated regularly with new data, ensuring they remain adaptable to evolving business requirements. Finally, training and empowering teams is crucial; providing tailored programs helps align AI capabilities with daily workflows, fostering greater user adoption and maximizing the solution’s impact

 

Conclusion

Implementing AI solutions requires a structured and connected process: setting clear goals, managing high-quality data, training robust models, and ensuring a seamless integration with existing systems. As shown in the case study of Bank X, this approach can significantly enhance operational efficiency, reduce manual workload, and improve decision-making. However, such transformations come with challenges.

AI implementation can be costly, requiring investment in infrastructure, expertise, and continuous improvement. Moreover, many organizations lack the internal resources or specialized knowledge to develop and maintain an AI solution entirely in-house.

This is where Altilia comes in. Altilia’s Smart knowledge platform is designed to overcome these barriers, offering:

• Ease of integration: A fast and adaptable AI solution tailored to the client’s specific needs.
• Scalability and flexibility: A platform capable of growing with your organization and accommodating new challenges.
• Advanced document processing capabilities: Seamlessly processes large volumes of unstructured documents, such as contracts, financial statements, and reports, extracting valuable insights and automating workflows to save time and reduce errors.
• End-to-end support: Altilia’s dedicated team of consultants will guide you through every step of the implementation journey—defining objectives, preparing data, training models, and ensuring seamless integration into your existing systems.

With Altilia, your organization can unlock the full potential of AI without the usual complexity, achieving tangible results quickly and efficiently. Contact us today for a consultation and start your journey toward smarter, more efficient operations.

Overcoming the biggest challenges in intelligent document processing

We’ve all been there: urgently uploading a complex document to ChatGPT or another general purpose AI solution, hoping for that magical moment of instant understanding. You paste in a financial report, a lengthy contract, or a multi-page compliance document, and wait with anticipation. The response comes back – and can I trust this answer?

Sure, these tools might summarize a few points or extract basic details, but can they guarantee the accuracy and completeness of the analysis? And when you’re handling high-stakes documents like contracts or compliance reports, where even a single overlooked clause or misinterpreted detail could lead to serious consequences, “good enough” simply doesn’t cut it.

Most GenAI tools today are like eager but inexperienced junior assistants. They’ll give you a quick overview, highlight a few things, but miss the connections that an experienced professional would catch. They’ll read a loan application, but might overlook that crucial footnote that changes everything. They’ll scan a compliance document, but could miss the intricate relationships between different sections that truly tell the full story.

For businesses handling critical documents day in and day out, relying on “probably correct” results is a risk you can’t afford. What you need is a solution that doesn’t just glance at the words but truly understands the context, the relationships, and the implications within your documents. A solution like Altilia, designed to not only extract data but to deliver comprehensive insights with the precision and reliability you can trust.

The daily struggle with documents

General purpose GenAI apps face three key challenges when processing documents that either effect the accuracy in the scanning of the document, the efficiency in the elaboration of the info or doesn’t adapt effectively to the complexity of the multitude layout of different documents:

The context problem

Imagine evaluating a customer’s creditworthiness by examining individual transactions in isolation, without piecing together their full financial story. Traditional GenAI tools often work this way, fragmenting documents into smaller sections and processing them independently. This approach overlooks the critical connections between sections, like how repayment terms align with income statements or how hidden clauses in one part of a document might contradict terms in another. For financial documents where every detail matters, this lack of contextual understanding can result in costly errors—such as misjudging a customer’s true ability to repay a loan or missing compliance risks buried in the fine print. Accurate decision-making demands a solution that understands the full picture, not just isolated fragments.

The structure challenge

Most GenAI apps read documents sequentially, like a straight line of text, missing the crucial hierarchy and organization that gives financial documents their meaning. Imagine reading a financial statement without distinguishing between headers, footnotes, and main entries – that’s essentially what happens when GenAI ignores document structure.

The connection gap

In financial documents, information often relies on definitions or conditions stated elsewhere in the document. General purpose GenAI struggles to make these connections, potentially missing critical dependencies in complex financial agreements or regulatory documents.

How Altilia transforms document processing

The Altilia platform collects, processes, and centralizes all kinds of data, both structured and unstructured. It automates complex business processes and provides advanced knowledge management tools. It uses integrated AI technologies to add value through automation, synthesis, and content generation. Here’s how it works:

Step 1: Document intake

The platform accepts any document your financial institution handles. This includes scanned loan applications, pptx, xls, and digital compliance reports. It’s like having a universal document translator at your disposal.

Step 2: Smart processing pipeline

This is where the magic happens. The platform processes documents through four key stages:

• First, it reads all text, even from scanned documents which is the most challenging format for the general purpose AI tools due to the quality of the scan.
• Then, it maps the document’s structure. It understands how its parts relate (header, paragraph, notes, etc.). It’s like creating a detailed outline of a complex financial report.
• Next, it builds a complete understanding of the document’s structure. It’s like creating a map of where the information is.
• Finally, it analyzes any charts, graphs, or images, ensuring no information is missed

Step 3: Knowledge creation

All this information goes into the business’ knowledge base. Here, documents are organized, linked, and made available. Every employee can access the needed information. However, not everyone should see everything. So, we can create different access levels for each employee.

Step 4: Practical application

This is where businesses can get advantage of Altilia’s platform to make the next step into the automation of their workflow.

• Automated processing: The system can automatically handle routine document tasks, like processing loan applications or checking compliance. It will report the client’s credit score and a summary of the key points in their financial records.
• Smart assistance: AI-powered tools help staff quickly find and use information across the business’s entire knowledge base. By simply typing a question, such as “Who is the financial advisor of company XYZ?“, the search function can rapidly locate the information.
• Custom automation: Create specialized workflows for your specific needs, no technical expertise required.

Real benefits for financial institutions

This approach transforms daily operations in several ways. Here some examples:

• Faster processing: What used to take hours of manual document review can now be completed in minutes
• Better accuracy: With a comprehensive understanding of every document, your team catches critical details that might have been overlooked before
• Enhanced compliance: The system helps ensure regulatory requirements are met consistently
• Improved decision-making: With better access to information, your team can make more informed decisions quickly

Making it work in your business

The best part about Altilia’s solution is how easily it fits into your existing operations:

• No technical expertise needed: The platform is designed to be used by financial professionals, not tech experts
• Seamless integration: It works alongside your existing systems without disrupting current processes
• Scalable solution: Whether you’re processing hundreds or thousands of documents, the platform grows with your needs

Beyond traditional document processing

While most GenAI platforms struggle with document complexity, Altilia offers a fresh approach to information processing. Traditional platforms treat documents as separate fragments, reading text mechanically and missing critical connections. They also require extensive technical expertise to customize.

Altilia completely transforms this approach. It creates a comprehensive, intelligent map of each document instead of fragmenting information. This is like having a super-smart analyst who not only reads text but also truly understands its intricate relationships, structure, and context.

Unlike general purpose solutions, which see isolated data, Altilia sees a living network of knowledge. It preserves document hierarchies, enables real-time cross-referencing, and turns unstructured information into actionable insights. Unlike traditional solutions that demand complex technical skills, Altilia provides a no-code, user-friendly environment that any business professional can navigate.

In essence, Altilia is not just processing documents – it’s reimagining how businesses understand and leverage their information. It’s not about faster data extraction, but about generating deeper, more intelligent insights.

Imagine you need to renovate an apartment. You’ve got your trusty hammer in the toolbox, and it’s served you well for hanging pictures and minor repairs. But now you need to fix a leaky pipe, repaint the living room, and rewire an electrical outlet. Suddenly, that reliable hammer seems completely inadequate. You quickly realize that to tackle these diverse tasks, you need a variety of specialized tools – a wrench for the pipe, brushes for painting, and a voltage tester for the wiring job.

Don’t worry, Altilia is not rebranding itself as a DIY startup, but this scenario perfectly illustrates the current state of AI in business. Many companies, eager to exploit the power of AI, are reaching for a single, all-purpose AI solution, much like grabbing that trusty hammer for every home repair. They’re turning to impressive, general-purpose AI models like GPT4 or Claude, expecting these tools to solve all their business challenges. While these models are undoubtedly powerful and versatile, they’re not optimized for every specific business need.

The limits of a universal AI agent of the Universal AI Agent

GPT4 and Claude, developed by OpenAI and Anthropic respectively, are remarkable achievements in natural language processing. They can generate human-like text, answer questions, and even assist with simple coding tasks. Many businesses have integrated these models into their operations, using them for customer service chatbots, or general data analysis.

However, these general AI models have limitations when it comes to understanding specialized business-domain information. For instance, a financial institution using GPT4 for customer inquiries might find it struggles with industry-specific terminology or regulations or to retrieve correctly the info from documents. The reality is that while these general AI models are impressive, they’re not designed to handle the diverse and specific challenges that different businesses face. Each industry, each company, has its own unique processes, data types, and objectives. A one-size-fits-all AI agent often falls short in understanding these nuances.

The Advantage of Specialized AI Agents

This is where specialized AI agents come into play. By focusing on specific tasks, these agents can befine-tuned to understand the unique nuances and technicalities of your own organization and/or industry, work with your unique data formats, and align with each of your particular business processes’. 

The result? Greater accuracy, improved efficiency, and better integration with your existing workflows. By focusing on creating multiple, specialized AI agents rather than relying on a single, generic solution, businesses can:

• Address specific challenges more effectively, improving problem-solving capabilities
• Improve accuracy in task completion, reducing errors and enhancing quality
• Enhance efficiency in specialized processes, saving time and resources
• Better integrate AI with existing workflows, minimizing disruption and maximizing adoption
• Maintain their unique competitive edge by leveraging AI in ways specific to your own business model

This specialization allows businesses to use AI not just as a general tool, but as a set of expert assistants, each trained in a specific area of the business. It’s the difference between having a general handyman and a team of specialized craftsmen – each brings expert knowledge to their specific domain.

 

None knows your business better than you do

While AI technology itself can be replicated, the real competitive advantage lies in how that technology is applied to your specific business context. Altilia helps you build a lasting competitive advantage by creating custom AI agents that integrate perfectly with your unique business processes. Think of it as your personal AI factory, producing solutions specifically tailored to your company’s operations and needs

How Altilia improves business’s workflow

Altilia’s platform offers two main features that optimize work and data management by leveraging information from diverse sources scattered throughout the entire business knowledge ecosystem:

1. AI Assistants for Employees: These intelligent co-pilots are optimized for your company’s specific needs, drawing insights from a vast array of sources. Unlike general AI models, these assistants are trained on your company’s data repository, including documents, databases, and digital assets spread across various departments and systems (e.g. mails, CRM databases, Google Drive, …). They assist your staff in their daily tasks by providing a unified view of the information they are looking for, enhancing productivity and decision-making. For example, in a customer service context, these assistants could instantly access and synthesize information from product manuals, internal wikis, customer interaction logs, and even email threads to provide representatives with holistic, context-aware solutions tailored to your specific offerings and policies.
2. AI Robots for Process Automation: AI Robots automate processes that would otherwise be done manually by processing data from multiple sources across your organization. For instance, in a legal firm, an AI robot could be set up to automatically analyze incoming legal documents via mail while cross-referencing them with historical case files, legal databases, and regulatory updates scattered across various systems. It can then categorize cases, extract key information, identify precedents from past cases stored in different repositories, and even generate draft responses by combining relevant information from multiple sources. This level of intelligent automation goes far beyond what a general AI model could offer, as it’s specifically designed to understand, integrate, and work with diverse legal documents and processes spread throughout the organization’s knowledge base.

Why choose Altilia’s Smart Knowledge Base?

1. Simplifying Custom AI Creation: we all know that good data is the key for a quality output. That’s why Altilia’s platform makes your company data “AI-ready” through a sophisticated process. This includes OCR (Optical Character Recognition) analysis for document scanning and layout processing. By preparing your data in this way, Altilia ensures that it’s in a format that’s easily interpretable by AI agents. This is crucial for businesses dealing with large volumes of unstructured data, such as scanned documents or handwritten notes.
2. Integrated Development Environment: The platform offers a simplified environment to develop AI applications that fit your specific knowledge needs and data types. This environment is designed to be user-friendly, allowing even those without extensive IT expertise to participate in the development process. You have the flexibility to create these AI agents yourself or have Altilia’s experts create them for you, ensuring that the resulting agents are perfectly aligned with your business objectives.
3. Adaptability: Unlike off-the-shelf solutions, Altilia’s platform works with your specific data types, understands and fits your unique processes, and can be continuously refined to meet evolving business needs. This adaptability ensures that your AI solutions grow and evolve with your business, rather than constraining your processes to fit a generic AI model.

 

Conclusion

As we return to our home improvement analogy, it’s clear that just as a well-equipped toolbox is essential for tackling diverse home repairs, a suite of specialized AI agents is crucial for addressing varied business challenges. The one-size-fits-all approach, while tempting in its simplicity, often falls short in delivering the specific solutions that businesses really need.

In the race to adopt AI, it’s crucial to remember that true power comes from specialization and customization. While general AI models like GPT4 and Claude have their place, they shouldn’t be seen as the end-all solution for business AI needs. Instead, they should be viewed as starting points – impressive technologies that demonstrate the potential of AI, but which need to be refined and specialized to truly transform business operations.

Altilia’s platform empowers businesses to move beyond generic solutions and create AI agents that truly understand and enhance their processes. As you consider integrating AI into your business, think beyond the one-tool approach. Consider how specialized AI agents could improve specific areas of your operations, much like how a particular tool can be used in renovating an apartment.

Altilia makes it easy to create AI tailored to your needs. It’s time to move beyond the myth of the universal AI agent and embrace the potential of specialized AI solutions.

Harnessing the power of unstructured information means taking full advantage of up to 80% of a company’s digital assets. Data Manager Online, a leading Italian technology publication, recently featured Altilia in an article exploring innovative solutions for dealing with unstructured data. 

In the article “Unstructured Data? Altilia has the solution“, Massimo Ruffolo, CEO and Founder of Altilia, gives a glimpse into a future where advanced AI doesn’t just process documents – it turns them into actionable insights, transforming the way companies across industries manage, interpret and use their most valuable resource: information.

Key Insights from the Article

1. 80% of business data is unstructured: Unstructured data makes up the vast majority of an organization’s information assets and is growing exponentially, and managing this vast amount of information is a significant challenge facing every modern organization.
2. AI transforms documents into actionable insights: Altilia’s technology, including Generative AI, enables more sophisticated data control and automated document creation through ‘active insights’, turning raw and unstructured information into valuable knowledge that can be shared across the organization.
3. Built to adapt to the needs of any industry: Customized AI assistants are designed to adapt to the unique needs of specific industries – from banking to pharmaceuticals – the platform addresses each domain-specific knowledge and process-specific requirements to ensure optimal performance.
4. Humans and AI: Altilia’s reinforcement learning approach allows humans to review the actions of AI assistants and provide feedback to train them for future processes. This continuous learning enables the AI system to improve its performance over time.
5. One Platform to Rule Them All: The single solution approach simplifies the management of unstructured data for organizations, eliminating the need for multiple tools and streamlining operations.

 

As the digital landscape evolves, Altilia promotes an ethical and responsible approach to AI adoption, contributing to the future of work by embracing technological revolutions in Italy.

Click here to read the full article (in Italian).

Introduction:

With an explosive annual growth rate of 37.3% (Grand View Research), AI is reshaping the way industries operate and innovate. From global tech giants to highly regulated sectors, businesses are racing to exploit AI’s potential. But this AI race isn’t just about releasing the most technologically advanced solution; it’s a dance between innovation and responsibility.

This article takes you on a journey into the AI-driven business world, exploring how companies are moving from AI experiments to deployments, the rise of open-source models, and the push for customized AI solutions. We’ll uncover why the most regulated industries are becoming AI pioneers and how businesses are addressing consumer concerns while pushing the boundaries of what’s possible.

The Point of View of Consumers:

Let’s start from the pain points: how consumers see AI. While the adoption of AI solutions from businesses accelerates, consumers approach the integration of AI in business with a mix of curiosity and caution. While many appreciate the enhanced personalization and efficiency that AI can bring, concerns about data privacy, job displacement, and misinformation persist. 

Interestingly, a survey made by forbes points out that 65% of consumers say they’ll still trust businesses that use AI, indicating a general acceptance of this technology. 

Despite AI’s potential, many people are concerned about the technology. To build trust and maintain good relationships with customers, companies using AI must openly address these concerns. Successful AI development and adoption requires carefully balancing innovation with ethical considerations.

 

The AI Use Cases Driving Adoption:

As artificial intelligence rapidly transitions from buzzword to business assets, organizations are strategically identifying the most impactful areas for AI integration. The landscape of AI implementation is diverse, reflecting the technology’s adaptability to various industry challenges and operational needs. While the potential applications of AI are vast, certain use cases are emerging as clear frontrunners in the race for digital transformation. A recent IBM study sheds light on the AI priorities of today’s businesses, revealing a focus on enhancing operational efficiency, bolstering security, and augmenting decision-making processes. Let’s explore the top 5 AI applications:

1. Automation of IT processes (33% of surveyed companies)
2. Security and threat detection (26% of surveyed companies)
3. AI monitoring or governance (25% of surveyed companies)
4. Business analytics or intelligence (24% of surveyed companies)
5. Automating processing, understanding, and flow of documents (24% of surveyed companies)

 

The Shift from Experimentation to Production:

The State of Data + AI report made by Databricks reveals a significant shift from AI experimentation to production. Remarkably, there’s been an 11x increase in AI models deployed into production compared to last year. Organizations have become 3x times more efficient at deploying models, indicating a maturing AI landscape.

This increased efficiency is largely due to the emergence of data intelligence platforms, which provide a unified environment for the entire AI lifecycle – from data preparation to model deployment and monitoring.

 

The NLP Revolution:

Natural Language Processing (NLP) has emerged as a transformative force in AI applications, revolutionizing how machines understand and interact with human language. At its core, NLP is the technology that allows AI systems to read, decipher, understand, and make sense of human languages in a valuable way. Its popularity relies on its wide-ranging applications across industries and its ability to bridge the gap between human communication and computer understanding.

The State of Data + AI report reveals that NLP is not just growing—it’s dominating. With 50% of specialized Python libraries used in AI applications associated with NLP, it has become the most utilized and fastest-growing machine learning application. This surge in adoption is driven by NLP’s versatility and its potential to solve complex, language-related challenges.

1. In healthcare, NLP is accelerating clinical research by analyzing vast amounts of medical literature and patient records, leading to faster drug discovery and more personalized treatment plans. 
2. Financial institutions are leveraging NLP for sentiment analysis of market reports, automated trading based on news, and enhanced customer service through sophisticated chatbots. 
3. Retailers are using NLP to analyze customer reviews, improve product recommendations, and create more intuitive voice shopping experiences.

The power of NLP lies in its ability to make sense of unstructured data—like emails, social media posts, customer feedback, and recorded conversations—which constitutes up to 80% of enterprise data. By turning this unstructured information into structured, actionable insights, NLP is enabling businesses to tap into previously underutilized data sources, leading to better decision-making and more personalized customer experiences.

Moreover, advancements in NLP, particularly with the rise of transformer models like BERT and GPT, have dramatically improved machines’ ability to understand context and nuance in language. This has opened up new possibilities for more natural human-computer interactions, from more accurate machine translation to AI-powered content creation and, as NLP continues to evolve, its integration with other AI technologies like computer vision and sentiment analysis is creating even more powerful tools for businesses. 

 

Open-source LLMs:

The adoption of open-source Large Language Models (LLMs) is rapidly gaining momentum in the business world. According to the State of Data + AI report, 76% of companies using LLMs are choosing open-source options, often alongside proprietary models. This shift is driven by several key factors:

1. Customization and Control: Open-source LLMs allow businesses to fine-tune models to their specific needs and industry requirements.
2. Cost-effectiveness: These models offer a more affordable solution, especially for smaller businesses or those new to AI.
3. Transparency and Trust: The ability to inspect the code builds trust, crucial for regulated industries.
4. Rapid Innovation: Open-source communities drive fast-paced improvements and new features.
5. Flexibility in Deployment: On-premises or private cloud deployment options offer greater control over data and compliance.

The report also reveals a preference for smaller models, with 77% of users choosing LLMs with 13 billion parameters or fewer (GPT4 has 1.76 trillion parameters). This indicates a focus on balancing performance with cost and latency.

Highly regulated industries are the unexpected AI Pioneers:

Contrary to expectations, highly regulated industries such as Financial Services and Healthcare are at the forefront of AI adoption. Financial Services leads in GPU usage, with an 88% growth over six months, indicating a strong commitment to LLM applications. Meanwhile, Healthcare & Life Sciences are among the top adopters of foundation model APIs, leveraging AI for everything from drug discovery to patient care optimization. The reasons are of course mixed:

1. Data Advantage and Necessity: These industries possess vast amounts of valuable data and face complex challenges that AI is well-suited to address.
2. Risk Management and Compliance: AI offers powerful tools for enhancing risk assessment, fraud detection, and streamlining compliance processes, which are critical in regulated environments.
3. Competitive Pressure and Customer Expectations: The potential for AI to provide a competitive edge and meet increasing demands for personalized, efficient services is driving adoption.
4. Resources and Impact Potential: These industries often have the financial capacity to invest in AI, and the potential impact of AI in these sectors (e.g., improved financial advice, more accurate medical diagnoses) is significant.

What is “RAG” and why businesses are using it:

Let’s start from the definition: Retrieval augmented generation (RAG) is a GenAI application pattern that finds data and documents relevant to a question or task and provides them as context for the LLM to give more accurate responses.

Businesses are increasingly focused on personalizing an AI to their specific needs. RAG’s techniques allow businesses to create AI systems that truly understand and operate within their specific contexts, driving innovation and competitive advantage across various industries.This is evidenced by the staggering 377% year-over-year growth in vector database usage, which is crucial for RAG applications (Databricks, 2024). This surge in adoption is driven by several benefits:

1. Enhanced Accuracy: RAG allows businesses to augment LLMs with their own proprietary data, leading to more accurate and contextually relevant outputs.
2. Reduced Hallucinations: By grounding LLM responses in verified information, RAG significantly reduces the risk of AI hallucinations, increasing reliability.
3. Real-time Knowledge Integration: RAG enables the integration of up-to-date information without the need for constant model retraining, keeping AI responses always updated.
4. Cost-Efficiency: Compared to fine-tuning large models, RAG offers a more cost-effective way to customize AI outputs for specific business domains.
5. Improved Compliance: For regulated industries, RAG provides better control over the information sources used by AI, aiding in compliance efforts.
6. Scalability: As businesses grow, RAG can easily incorporate new data sources, allowing AI systems to evolve with the company.
7. Preservation of Proprietary Knowledge: RAG allows companies to leverage their own data assets without exposing this information during model training.

To know more about how Altilia is leading the development and implementation of personalized RAG solutions, read this article.

 

Conclusion:

The experimentation of AI in businesses is well underway, with applications spanning from improved efficiency to enhanced customer experiences. While challenges remain, particularly around consumer trust and ethical considerations, the potential benefits of AI are tangible.

As we move forward, it’s crucial for businesses to embrace AI innovation responsibly. By addressing consumer concerns, prioritizing transparency, and leveraging AI’s capabilities ethically, companies can harness the transformative power of AI to drive growth and create value in the AI-driven economy of the future.

Imagine writing an email in 20 seconds instead of 20 minutes, completing hours of research in a fraction of the time, or automatically receiving a full summary of a long meeting. These scenarios showcase the potential of AI assistants, or copilots, integrated into your workflow. With these tools, you simply tell the copilot what to do, right in the flow of your work.

But is this too good to be true?

 

What is an AI copilot? 

You might be familiar with copilots in the context of aviation, assisting the captain during flights. Recently, the concept of a “copilot” has gained more traction in the realm of artificial intelligence (AI). Imagine incorporating the generative AI technology from apps like ChatGPT, Gemini, or Claude into your daily workflow. That is your AI copilot. At its core, an AI copilot is an AI assistant designed to help you complete routine tasks more efficiently. Using large language models (LLMs), it facilitates natural, human-like conversations, assisting users with a wide range of tasks. Examples include the AI copilot developed by Microsoft for its Office suite.

 

How does an AI copilot work? 

AI copilots are powered by fundamental components known as copilot actions. A copilot action can cover a single task or a collection of tasks specific to a particular job. These tasks might include:

• Updating a CRM record.
• Generating product descriptions using existing CRM data.
• Composing messages to customers.
• Handling various use cases.
• Summarizing transcripts for a live service agent.
• Highlighting the most relevant information from meeting notes.

 

These tasks can be “invoked” or executed in any order, autonomously managed by the AI copilot. The ability to understand requests, devise a plan of action, and carry out the necessary tasks is what sets these systems apart. The AI copilot learns and improves with each action, becoming more capable over time. When combined, these actions enable your copilot to perform a vast array of business tasks. For instance, an AI copilot can assist a service agent in quickly resolving a customer overcharge issue or help a lawyer to spot the right strategy to use.

 

The challenges of implementing a copilot

Despite their promise, many businesses are struggling to implement and use copilots efficiently. Why is this happening?

1. The Data Dilemma

At the heart of the challenge lies data quality. Many organizations find their existing data outdated, inconsistent, or inaccurate. This leads to AI assistants providing unreliable or outdated answers. For instance, an AI tool might deliver 2023 data when asked about 2024 figures, or fail to correctly identify a company’s executive team.

A high-profile example is McDonald’s collaboration with IBM to automate ordering using AI. The project, tested in 100 restaurants, was eventually abandoned due to the inherent difficulty of Ai in understanding voice commands (therefore, the input data). This decision highlights the gap between the technology’s potential and its current limitations.

2. The Costly Clean-up

To address these issues, companies are embarking on extensive data clean-up efforts. This process involves validating and refining incoming data, creating records and databases free of contradictions or duplicates. While necessary, this task is proving more time-consuming and resource-intensive than anticipated.

3. Reliability and learning curve

AI sometimes makes things up, and we call this “hallucinating.” This happens because of how AI is built. AI learns to write by predicting what words should come next, based on what it has seen before. It doesn’t really understand what it’s saying – it’s just making good guesses. Think of it like a very advanced autocomplete on your phone, but for whole sentences and ideas. Sometimes, this leads to mistakes or false information.

Even as GenAI gets better, it will probably still make these mistakes sometimes. That’s why it’s important for people to double-check what AI produces to make sure it’s correct and makes sense.

Another hurdle is the complexity of effectively “prompting” these AI assistants. Users often struggle to provide sufficient context for their queries, leading to suboptimal responses. Even sophisticated tools like Microsoft’s copilot don’t inherently know which data sources to prioritize for specific questions.

4. Lack of scalability

AI co-pilots are designed to act as personal productivity assistants. However, they aren’t well-suited for industrial applications. In industrial settings, processes need to be carried out on a large scale, with reliable results and minimal human supervision. Co-pilots, as they currently exist, don’t meet these requirements for industrial use, where efficiency and consistency at scale are crucial

 

Streamlining AI Implementation

To face these challenges, new solutions are emerging. copilots solutions like Altilia’s are designed to address the core issues that afflict traditional copilots implementations:

1. Harmonization and Data Quality Improvement:

Altilia’s platform excels in managing diverse, unstructured data from various sources automatically, significantly reducing the need for manual preparation and ensuring seamless data integration. The platform classifies content, extracts precise data and metadata, and maintains relevance without constant intervention, ensuring high-quality data is readily available.

2. Smart Data Organization for Smarter AI Assistants

By organizing data into comprehensive knowledge graphs, Altilia makes it easier to create enriched records that AI assistants can access and utilize for pertinent, up-to-date information, thereby enhancing their accuracy and efficiency.

3. Improved reliability by Business-Specific Customization

The platform trains AI models to learn domain specific knowledge, within unique business contexts. This allows AI assistants to answer company-specific questions, with superior relevance and effectiveness, ensuring that each AI assistant is tailored to meet the distinct needs of the organization.

4. Result Transparency

Altilia accelerates the implementation of AI assistants in the workplace, providing a transparent platform that always allows to review answers and results, tracing back data in the context of its original source, promoting trust and understanding among users.

5. Scalability for enterprise applications

Altilia’s solution manages entire business processes from beginning to end. Altilia’s Co pilot is both reliable and flexible, allowing for easy control and monitoring of results and responses generated by AI models. This approach facilitates the seamless integration of AI into large-scale operations, addressing the need for dependable automation in complex business environments.

 

Looking Ahead

It’s clear that AI copilot features and capabilities will continue to expand. We’re moving from manually entering data and clicking through screens to simply making requests in natural language, with copilots promptly retrieving relevant information from business meetings or internal documents

While implementing AI work assistants has proven more challenging than expected, solutions addressing data quality, structure, and relevance offer a path forward. As these technologies evolve, we can anticipate more seamless integration of AI assistants, leading to the productivity gains and insights that businesses are eagerly awaiting.

In the ever-evolving landscape of digital business, data has become the new currency. Yet, not all data is created equal. While structured data has long been the cornerstone of business analytics, an ocean of unstructured data remains largely hidden. This is where Artificial Intelligence (AI) is making a difference, offering new ways to extract value from this digital goldmine.

The Unstructured Data Difficulty

Imagine for a moment the volume of information that flows through a modern enterprise: countless emails, social media interactions, customer service calls, images, videos, and documents. This is the realm of unstructured data, and it’s growing at an astronomical rate. According to Gartner, a staggering 80% to 90% of data generated and collected by organizations is unstructured, with its volume expanding many times faster than structured data.

The challenge lies not just in the volume, but in the nature of this data. Unlike structured data that neatly fits into predefined database fields, unstructured data is a mix of formats and sources. It’s the difference between a meticulously organized filing cabinet and a room full of scattered papers, photos, and recordings. This lack of inherent organization has historically made unstructured data a huge challenge for traditional analytics tools.

Enter Artificial Intelligence

This is where AI emerges as a game-changer. Advanced machine learning algorithms and natural language processing capabilities are enabling businesses to sift through vast quantities of unstructured data, uncovering patterns, insights, and actionable information that were previously hidden from view.

Altilia is pioneering this field, offering solutions that transform how businesses handle unstructured data. Altilia’s platform represents a leap forward in our ability to extract meaningful information from unstructured documents. It’s not just about converting text to digital format; Altilia’s IDP solution can understand context, categorize information, and even make inferences based on the content they process.

Solving the Unstructured Data problem

Altilia’s platform combines various AI technologies to tackle complex document workflows. This innovative approach allows businesses to automate the ingestion and analysis of various document types, extracting relevant information and even flagging potential issues or inconsistencies.

What sets Altilia apart is its focus on accessibility and continuous improvement. Their no-code platform democratizes AI technology, making it accessible to users without technical backgrounds. Furthermore, their “human-in-the-loop” continuous learning ensures that the system keeps improving over time, adapting to new document types and evolving business needs.

The Power of IDP in Action

Imagine a banking institution processing loan applications. Traditionally, this would involve manual review of numerous documents, a time-consuming and subject to errors process. With Altilia’s IDP solution, the bank can automate this process, handling a wide range of document formats and integrating seamlessly with existing enterprise systems. This not only speeds up the process but also enhances accuracy and compliance.

But the applications go far beyond banking. In the legal sector, Altilia’s AI can filter through vast databases of case law, identifying relevant precedents. In customer service, it can analyze call transcripts and chat logs to identify common issues and measure customer sentiment. The possibilities are virtually endless.

The Business Impact

The impact of these capabilities on business operations and decision-making cannot be overstated. By leveraging Altilia’s IDP platform, businesses can:

1. Enhance Efficiency: Automating complex document workflows dramatically reduces manual labor and accelerates decision-making processes.
2. Improve Accuracy: AI-powered document processing minimizes human error, ensuring more reliable data extraction and analysis.
3. Scale Operations: Altilia’s platform can handle large volumes of documents efficiently, allowing businesses to scale their operations without proportionally increasing costs.
4. Drive Innovation: By uncovering hidden patterns and correlations in unstructured data, businesses can spark new ideas for products, services, or process improvements.

The Road Ahead

While the potential of AI in managing unstructured data is immense, implementation requires careful consideration. This is where Altilia’s ethical AI practices come into play, ensuring that businesses can harness the power of AI responsibly and sustainably.

As we stand on the brink of a new era in data analytics, one thing is clear: the ability to effectively harness unstructured data will be a key differentiator for businesses in the coming years. Altilia’s IDP platform is not just a tool for efficiency; it’s a gateway to a new world of business intelligence.

Conclusion

The organizations that can successfully navigate this new landscape – leveraging AI to turn the chaos of unstructured data into a wellspring of actionable insights – will be well-positioned to lead in their respective industries. As the volume and variety of unstructured data continue to grow, so too will the importance of advanced IDP solutions like Altilia’s.

The future of business intelligence is here, and it’s powered by AI. With Altilia’s innovative approach to IDP, businesses have a powerful ally in their quest to unlock the full potential of their unstructured data. By embracing these technologies, companies can not only keep pace with the data revolution but stay ahead of the curve, turning information into insight, and insight into action.

We are thrilled to announce our recognition as a Major Player in the IDC MarketScape for Unstructured Intelligent Document Processing (IDP) software 

This recognition highlights our software’s ability to streamline business operations by effectively managing complex unstructured document workflows, particularly in sectors like finance and public administration.

From the beginning, we have focused on enhancing organizational knowledge management with a solution that processes both structured and unstructured data, acting as a co-assistant to employees. Our Human-in-the-Loop (HITL) AI approach ensures continuous model training and transparency, providing highly accurate and reliable results.

With a dedicated team of over 50 professionals, including scientists, researchers, and software engineers, we are committed to democratizing the use of AI to help enterprises automate document-intensive business processes. This acknowledgment by IDC MarketScape reaffirms our position as a leader in the ever-evolving landscape of Intelligent Document Processing technology.

Our platform is designed for quick deployment and intuitive workflow design, making it suitable for organizations of all sizes and across various industry verticals looking to implement unstructured IDP. 

As we celebrate this recognition, we remain dedicated to shaping the future of document processing by bringing cutting-edge solutions to the forefront of the IDP market. Our goal is to offer organizations unparalleled efficiency, automation, and knowledge management capabilities.

 

What is Unstructured IDP ?

Unstructured Intelligent Document Processing (IDP) refers to a class of software technologies that leverage a combination of traditional and generative AI (GenAI), advanced analytics, and business rules to automate the classification, extraction, analysis, and validation of data from unstructured, semi-structured, and structured document formats. These technologies are designed to handle the high variability, inconsistent formats, and mixed elements (e.g., text, tables, charts) characteristic of unstructured documents, making the data within these documents actionable and integrated into business workflows.

 

About IDC MarketScape:

IDC MarketScape vendor assessment model is designed to provide an overview of the competitive fitness of ICT (information and communications technology) suppliers in a given market. The research methodology utilizes a rigorous scoring methodology based on both qualitative and quantitative criteria that results in a single graphical illustration of each vendor’s position within a given market. IDC MarketScape provides a clear framework in which the product and service offerings, capabilities and strategies, and current and future market success factors of IT and telecommunications vendors can be meaningfully compared. The framework also provides technology buyers with a 360-degree assessment of the strengths and weaknesses of current and prospective vendors.

Introduction

Artificial Intelligence (AI) is no longer a futuristic concept; it’s a present reality reshaping the modern workplace. Generative AI (GenAI), in particular, holds the promise of augmenting human work by automating routine tasks and enabling employees to focus on more impactful activities. This transformation is supported by recent research from Deloitte, which highlights the profound impact AI can have on workforce dynamics.

The Role of GenAI in the Workplace

Generative AI (GenAI) refers to advanced algorithms capable of creating content, analyzing data, and automating repetitive tasks. Unlike traditional AI, which makes predictions based on data to perform specific tasks and rules-based decisions, GenAI can generate new content and insights by learning patterns from data, making it a more versatile tool in a wider range of industries and use case applications.

Current Applications

In recent years, artificial intelligence has evolved into a suite of powerful technologies that offer significant competitive advantages to businesses across various industries. As companies rapidly adopt AI to meet their business objectives and stay ahead of competitors, many are uncertain about the outcomes and the level of acceptance these tools will receive from their employees. While employers are enthusiastic about the opportunities AI presents, the potential impacts on employee experience and trust cannot be overlooked.

Benefits of GenAI for Employees

• Increased Efficiency

One of the primary benefits of GenAI is its ability to streamline processes, reducing the time employees spend on repetitive tasks. This increased efficiency allows businesses to achieve more in less time, freeing up resources for strategic initiatives.

• Focus on High-Impact Tasks

With GenAI handling routine tasks, employees can dedicate more time to high-impact activities such as strategic planning, creative problem-solving, and innovation. For instance, customer service representatives can focus on complex queries that require a human touch, while automated systems handle routine inquiries.

By automating routine activities such as data entry, report generation, and basic customer service inquiries, GenAI allows businesses to operate more efficiently. GenAI not only accelerates workflows but also minimizes human error, ensuring more consistent and reliable outcomes

Case Studies

HSBC has leveraged AI to identify potential money laundering activities by analyzing transactional patterns, customer behavior, and risk indicators. This AI-driven approach has enabled the bank to flag suspicious transactions more effectively, reducing the number of alerts that require investigation by over 60%

Bank of America has utilized AI to forecast the likelihood of companies defaulting by analyzing diverse data sources, including financial statements, credit histories, and market trends. This AI-powered model has improved the accuracy of lending decisions and enhanced the bank’s ability to manage credit risk effectively

Trade-offs in the Use of GenAI

A recent report of Deloitte points out that while the benefits of GenAI are significant, implementing these systems is not without challenges. Organizations must invest in the necessary infrastructure, train employees to work with AI systems, and manage the change process effectively.

1 – Creative Inspiration vs. Diligence

As human beings, we value original thinking to solve problems, to understand each other better, and ultimately to improve our society. Without new ideas, we would settle for the status quo and abandon a core part of our human identity: the pursuit of progress. 

AI as an ‘idea sparker’ could enable employees to create multiple versions of their work in parallel and uncover perspectives they may not have thought of themselves. In the Deloitte’s report, over 69% of executives said they believe AI will improve employee creativity to some extent, with AI sparking new ideas and inspiration that will improve the quality of work. However, there is also a fear that an over-reliance on AI will sacrifice accuracy and thoroughness, with 42% of conversations citing concerns about a decline in work quality. From this place of uncertainty, leaders have an opportunity to redefine creativity in the workplace while maintaining human rigor by setting boundaries for the use of AI.

At Altilia, we agree GenAI can be a catalyst to spark new ideas. However, we believe a potential decline in work quality can only be a concern if AI is seen as a replacement for human effort. In our approach, GenAI is meant to complement and assist human work, not to replace it.

Successful GenAI implementations should foster a “collaborative” approach, where AI generates intermediate inputs that are meant to be verified, expanded and enhanced by human experts.

When used correctly, Generative AI organizes data and information, enabling humans to make informed decisions more efficiently. This partnership enhances work quality and fosters an environment where creativity and diligence thrive.

2 – Efficiency vs. Inclusivity

Businesses are eager to leverage AI to expedite routine tasks and remove the administrative burden for their employees. While leaders are optimistic about efficiency gains, nearly a third of conversations cited in the report concern the bias and inclusion challenges of AI, suggesting that the risk of further embedding systemic bias tempers their excitement. Biases emerge wherever humans go. Unchecked, our narrow reference of personal experience will build unconscious bias into everything we create. Given AI’s pace of evolution and “black box” decisioning processes, leaders are concerned that AI will entrench existing biases with no opportunity to reroute:

To mitigate the risk of bias, Businesses should focus on empowering employees to effectively use AI and identify bias while also promoting open dialogue on how AI is used within their organizations.

Secondly, there is a need for transparency and trustworthiness in the AI output. This can be translated in the concept of “explainability”—the ability for end users to understand how and why an AI model generated a particular response.

Explainability enables the implementation of human oversight mechanisms to verify the correctness and impartiality of AI outputs. Therefore, ensuring explainability should be a primary driver in the design of GenAI-based applications.

In the context of Intelligent Document Processing, where Altilia operates, we achieve explainability by allowing users to easily trace back to the sources of information underpinning the AI model’s responses. This approach ensures that users can trust the outputs and maintain control over the decision-making process, ultimately enhancing both the reliability and the acceptance of GenAI solutions in the workplace.

3 – Personalization vs. Data Privacy

GenAI enables highly personalized experiences for customers and employees by analyzing vast amounts of data to tailor interactions and recommendations. However, this level of personalization often requires extensive data collection, raising concerns about data privacy and the potential misuse of sensitive information. 

At Altilia, we prioritize data privacy by ensuring our AI models operate within a closed and protected environment. This means that the data used is fully compliant with data processing regulations, as it is not accessed or shared through third-party APIs. Our approach guarantees that both input and output data remain within the company’s control, avoiding the risk of customer data being processed by external entities (for example 3rd API-based GenAI services like OpenAI.)

This closed environment enhances compliance with various data protection regulations and ensures that only company-generated data is used. By relying solely on internal data, our models deliver more accurate and consistent responses, further strengthening data security and regulatory adherence.

Future Outlook 

The workplace implications of AI are profound, offering both opportunities and challenges. GenAI has the potential to enhance productivity, allowing employees to concentrate on high-impact tasks. According to the Deloitte report, AI integration in the workplace will continue to grow, with more businesses adopting GenAI to stay competitive. This trend will likely lead to new job roles focused on managing and enhancing AI systems.

At Altilia, we believe that successful AI applications in the near future will not only focus on efficiency and process automation but also on enriching companies’ informational assets and stimulating creativity. We see GenAI as an assistant that increasingly supports knowledge workers by taking on more operational roles, such as information reprocessing and synthesis. Meanwhile, human knowledge workers will remain at the forefront of decision-making and strategic activities.

This collaborative relationship will help companies build greater trust and exercise more control over AI-enhanced processes, while also making it easier for the workforce to accept GenAI as an opportunity rather than a threat. By positioning AI as a supportive tool, we can foster an environment where AI drives innovation and efficiency, ultimately leading to more dynamic and successful business operations.

If you’re eager to find a solution for your business to streamline processes, reduce the time spent on tasks, and drive growth, we’re here to guide you through every step. Book a free consultation with an Altilia expert today, and let’s embark on this journey together, unlocking the potential of GenAI to bring your business into the future.