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The state of AI adoption in businesses: Trends and Insights

By altilia on August 7, 2024

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.

By altilia on August 7, 2024

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A 5 steps guide to implementing AI solutions in your organization

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.: “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. 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 Analyze business pain points: Start by identifying critical inefficiencies. For Bank X, the repetitive nature of manual analysis was a prime candidate for automation. Set measurable KPIs: Goals must be quantifiable to assess success. Bank X focused on two: Time reduction and the accuracy improvement in data extraction. 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: Extracting key metrics: e.g.: revenue, debts, and cash flow indicators. Ensuring data consistency: Validating and cleaning extracted data to ensure accuracy. Building the infrastructure: Leveraging cloud solutions and advanced Optical Character Recognition (OCR) tools for scalability. Gather all the data in one place: To create an efficient AI solution, data must be easy to access.  Approach breakdown 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. Clean and validate the data: High-quality data is crucial. Processes like deduplication, normalization, and error-checking help prevent downstream issues. 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: 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. Training and Fine-Tuning: Historical financial reports were used to train the AI, teaching it to identify patterns across balance sheets and income statements. Testing and Validation: Outputs were rigorously validated by human reviewers to minimize inaccuracies. Approach breakdown 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. Train and validate: Use historical data to “teach” the AI. Human oversight ensures that the AI output aligns with business standards. 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 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. 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. 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. 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. Monitor for bias and hallucinations: Examine outputs for accuracy, fairness, and relevance. Address any biases introduced by the data or inconsistencies in responses. 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: Deployment: The AI solution connected to real-time data feeds, automating the analysis of incoming financial reports. 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. 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.

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Why your business needs more than a general purpose AI

Why your business needs more than a general purpose AI

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.

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Why One Size Doesn't Fit All in Business Automation

Why One Size Doesn't Fit All in Business Automation

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: 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. 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? 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. 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. 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.

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