Self improving document processing
Altilia is recognized as Major Player in 2023-2024 IDC MarketScape Worldwide Intelligent Document Processing Vendor Assessment
AltiliaAltilia is recognized as Major Player in 2023-2024 IDC MarketScape Worldwide Intelligent Document Processing Vendor Assessment
Altilia
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 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. 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. 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. 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. 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).
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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: Automation of IT processes (33% of surveyed companies) Security and threat detection (26% of surveyed companies) AI monitoring or governance (25% of surveyed companies) Business analytics or intelligence (24% of surveyed companies) 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. 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. Financial institutions are leveraging NLP for sentiment analysis of market reports, automated trading based on news, and enhanced customer service through sophisticated chatbots. 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: Customization and Control: Open-source LLMs allow businesses to fine-tune models to their specific needs and industry requirements. Cost-effectiveness: These models offer a more affordable solution, especially for smaller businesses or those new to AI. Transparency and Trust: The ability to inspect the code builds trust, crucial for regulated industries. Rapid Innovation: Open-source communities drive fast-paced improvements and new features. 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: Data Advantage and Necessity: These industries possess vast amounts of valuable data and face complex challenges that AI is well-suited to address. Risk Management and Compliance: AI offers powerful tools for enhancing risk assessment, fraud detection, and streamlining compliance processes, which are critical in regulated environments. 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. 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: Enhanced Accuracy: RAG allows businesses to augment LLMs with their own proprietary data, leading to more accurate and contextually relevant outputs. Reduced Hallucinations: By grounding LLM responses in verified information, RAG significantly reduces the risk of AI hallucinations, increasing reliability. 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. Cost-Efficiency: Compared to fine-tuning large models, RAG offers a more cost-effective way to customize AI outputs for specific business domains. Improved Compliance: For regulated industries, RAG provides better control over the information sources used by AI, aiding in compliance efforts. Scalability: As businesses grow, RAG can easily incorporate new data sources, allowing AI systems to evolve with the company. 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.
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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.
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