Category: News

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.

Generative AI (GenAI) has rapidly evolved from a futuristic concept to a transformative technology with significant implications across various industries. From automating complex workflows to creating new content, GenAI’s capabilities are expanding, promising to enhance productivity and drive economic growth. This article explores the current state of GenAI, its economic potential, and the hype surrounding it, providing a comprehensive view of its future prospects and the implications for businesses.

The Rise of Generative AI

Generative AI refers to a class of artificial intelligence systems capable of creating new content, such as text, images, and even music, based on input data. Unlike traditional AI models designed for specific tasks, GenAI systems learn patterns from vast datasets to generate original outputs. This versatility makes GenAI a powerful tool in various applications, from content creation to complex problem-solving.

The State of AI: Current Capabilities and Limitations

The current state of AI is marked by rapid advancements and expanding capabilities, particularly in generative AI. In 2023, global private investment in AI, especially generative AI, reached unprecedented levels. The U.S. led with $67.2 billion in investments, demonstrating its dominant position in the AI landscape. This surge was driven by significant interest in GenAI, which alone attracted $25.2 billion, nearly nine times the investment of the previous year​ (Stanford HAI)​.

AI technologies, including GenAI, have shown remarkable abilities in natural language processing, image recognition, and automated decision-making. These advancements have led to significant improvements in industries ranging from healthcare to finance. For instance, generative AI has been successfully integrated into various applications, enhancing productivity and driving economic growth across sectors​.

However, there are still several challenges that need to be addressed. One major limitation is the explainability of AI systems. Many AI models, especially deep learning ones, operate as “black boxes,” making it difficult to understand how they arrive at specific decisions. This lack of transparency can be problematic in critical applications, such as lending decisions or criminal justice, where understanding the rationale behind an AI’s decision is crucial.

Another significant challenge is the potential for bias in AI systems. Since AI models learn from existing data, they can inadvertently perpetuate and even amplify biases present in the training data. Ensuring fairness and mitigating bias in AI outputs is an ongoing area of research and development.

Additionally, the computational demands of AI, particularly the need for powerful hardware like GPUs, can be a barrier for widespread adoption. According to AI Index estimates, the training costs of state-of-the-art AI models have reached unprecedented levels. OpenAI’s GPT-4 used an estimated $78 million worth of compute to train, while Google’s Gemini Ultra cost $191 million for compute. As the demand for AI capabilities grows, so does the pressure on hardware resources and amount of new data to train more advanced models, leading to higher costs and potential bottlenecks in development and deployment.

Navigating the Hype

Generative AI is currently at the peak of inflated expectations, attracting significant attention and investment. In 2023, investment in generative AI reached $25.2 billion, more than 9 times the amount invested in the previous year ($2.85 billion) and almost 30 times the amount invested in 2019 ($0.84 billion). This growth is driven by advancements in AI models, infrastructure, and AI-enabled applications across various sectors. (Stanford HAI)

This phase highlights the excitement surrounding the technology but also suggests that there may be a period of disillusionment before GenAI reaches its full potential. The concept of the Hype Cycle helps us understand this journey: initially, new technologies often experience a surge of enthusiasm and inflated expectations, followed by a trough of disillusionment as challenges and limitations become apparent. Eventually, as the technology matures and its true potential is realized, it enters a phase of enlightenment and stable productivity.

Gartner’s analysis suggests that GenAI will reach maturity in 5 to 10 years, indicating that we are now at the peak of expectations. This timeline underscores the importance of managing expectations and preparing for the inevitable challenges that come with integrating new technologies. Understanding the current capabilities and limitations of AI, particularly generative AI, is crucial for navigating this landscape effectively and leveraging its potential for long-term benefits​.

The Importance of Employee Education in AI Integration

As new GenAI applications continue to be released and new AI capabilities are implemented in existing applications, businesses will increasingly need to integrate Generative AI into their workflows, so it’s vital that employees are well trained to use these advanced technologies effectively. Educating employees about AI not only enhances their understanding and acceptance but also maximizes the benefits of AI integration. Proper training helps employees understand how to leverage AI tools to improve their productivity, make data-driven decisions, and automate routine tasks. This leads to a more efficient workflow, reduced operational costs, and a competitive edge in the market. Moreover, continuous education and upskilling initiatives foster a culture of innovation and adaptability, preparing the workforce to keep pace with rapid technological advancements. 

At Altilia, we prioritize user-friendly solutions and comprehensive training programs to empower our clients to fully harness the power of GenAI in their operations. Our goal is to create solutions that are user-friendly and accessible even to “line-of-business” users—those who are experts in their business domain but may not have specific technical training in IT and machine learning. This approach enables even less structured companies in the AI field, which might lack in-house expertise for developing and maintaining models, to fully exploit the potential of GenAI​​ without the need of investing a huge amount in new know-how or softwares.

Generative AI in the Finance and Banking Sector: Applications and Altilia’s Solutions

Generative AI holds significant promise for the finance and banking industry, offering numerous applications that can support the work of consultants and transform operations, enhance customer experiences, and ensure compliance. Here’s a look at the key applications of GenAI in this sector and how Altilia’s solutions align with these opportunities:

1.  Automated customer service: GenAI-powered chatbots and virtual assistants can handle customer queries, manage accounts and provide financial advice, improving customer service efficiency and satisfaction 24/7. Altilia’s GenAI-powered virtual assistants deliver accurate, personalised responses to a wide range of customer queries, improving service levels while reducing operational costs. At the same time, Altilia’s solution supports employees in answering questions about documents and data.
2. Due Diligence, Regulatory and Compliance:  Altilia’s solution , powered by GenAI, can support financial institutions in various regulatory and compliance activities. Here are some examples:
   1. Review of Financial Statements: Analyzing balance sheets, income statements, and cash flow statements to assess financial health.
   2. Know Your Customer (KYC) Procedures: Verifying the identity of clients and assessing the potential risks of illegal intentions.
   3. Regulatory Reporting: Preparing and submitting reports required by regulatory bodies such as the SEC, FINRA, or the FCA.
   4. Audit and Inspection Readiness: Maintaining readiness for audits and inspections by regulatory agencies.
   5. Assessment of Internal Controls: Evaluating the effectiveness of internal controls related to financial reporting and operational processes.
   6. Anti-Money Laundering (AML) Compliance: Ensuring adherence to AML regulations through transaction monitoring and reporting suspicious activities.
   7. Risk Management Processes: Examining the systems in place to identify, measure, monitor, and control various risks (credit, market, operational, etc.).

1. Document Processing and Analysis: GenAI can automate the processing and analysis of various financial documents, such as loan applications, financial statements, and compliance reports, improving accuracy and speed. For example, banks handle thousands of loan and mortgage applications daily, a process that is time-consuming and error-prone due to manual data entry. Altilia’s platform automates document classification and data extraction, feeding this information into systems to support credit scoring. This reduces processing time by 80% and results in cuts of manual effort by over 80%.
2. Personalized Financial Services: GenAI can analyze customer data to offer personalized financial advice and services, such as tailored investment recommendations, savings plans, and credit risk assessments. Altilia leverages GenAI to deliver customized financial services, analyzing individual customer data to provide personalized advice and solutions, thereby enhancing customer engagement and satisfaction.

By addressing these critical areas, Altilia’s GenAI-powered solutions help financial institutions streamline operations, enhance security, comply with regulations, and deliver personalized services, driving growth and innovation in the industry

Conclusion

Generative AI is at the forefront of technological innovation, with the potential to transform industries and drive economic growth. By understanding its capabilities, addressing challenges, and adopting ethical practices, businesses can harness the power of GenAI to stay competitive and innovative. At Altilia, we are committed to leveraging GenAI to enhance document processing and deliver value to our clients.

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 transformative journey together, unlocking the potential of GenAI to propel your business into the future

Imagine a tool that can create almost anything you ask it to: text, images, music and even programming code. Generative AI (GenAI) has emerged not just as a concept, but as a transformative force in the business landscape.

 

What is Generative AI and why is it so special?

Generative AI acts like a super-assistant that doesn’t just perform tasks but does so with a note of creativity. It’s an innovation that creates content, designs and solutions, effectively turning ideas into reality with little more than a prompt.

What sets Generative AI (GenAI) apart is its ability to be a super-assistant in support of human creativity. Unlike traditional AI, which might suggest the next logical data point in a series, Generative AI could imagine an entirely new series. This is because GenAI works by understanding and replicating the complex patterns that make up human-like creations, from artwork to prose. For example, if you ask it to write a poem in the style of Shakespeare, it doesn’t just find existing poems; it generates a new poem that feels like it could have been written by the Bard himself.

In business, this means that Generative AI doesn’t just optimise existing processes; It’s an invaluable tool for streamlining complex tasks and problem solving, unlocking the ability to explore countless scenarios and variations that a human alone could never conceive.

 

Why AI is a Game-Changer for Businesses?

Generative AI is transforming business operations by streamlining tasks and reducing costs. It simplifies knowledge management by using AI to quickly answer employee questions and retrieve information. It automates document creation by using templates to generate contracts and reports with less effort. AI also improves data entry by extracting information from documents and populating databases more accurately and quickly. These improvements enable organisations to do more with fewer resources and less time.

 

The Value Potential

The figures are staggering—generative AI could add between $2.6 and $4.4 trillion to the global economy annually. That’s more than the GDP of many countries, and as GenAI technology advances, its impact is set to increase (source: McKinsey 2023)

 

Which Areas Will Benefit?

GenAI will significantly advance areas like finance, procurement, HR, legal, and customer service. It will also boost data analysis, business intelligence, and office support. These areas, often heavy with documents, will benefit. The solutions offered by Altilia, for example, automate tedious tasks, thereby increasing efficiency. The impact will also be felt across sectors, from banking to technology to healthcare.

 

AI helps complete tasks without replacing jobs

GenAI doesn’t replace jobs. It transforms and enhances them. In fact, AI is really powerful when it performs specific tasks, not entire jobs. At Altilia, we believe that AI can enhance the skills of workers. This is especially true for knowledge workers. It also increases the quality of work and productivity. AI targets repetitive, low-value manual tasks. These tasks provide little value to the organisation. Altilia’s solutions aim to empower employees by streamlining repetitive document-based processing tasks. They free employees from tedious tasks. This leaves more time for creative and social activities.

 

Looking Ahead

The rapid changes brought by GenAI are part of the larger evolution of our world. Companies’ adept at leveraging this technology could gain a considerable edge. However, there are challenges to be addressed, like ensuring responsible AI use and preparing people for the changes it will bring.

Generative AI is opening a realm of possibilities for companies, making it easier to accomplish tasks that were once time-consuming or even impossible. With every technological advancement comes challenges, but the potential to enhance how we work and live is immense.

 

7 Prompts to Streamline Work with AI

In the ever-evolving landscape of professional environments, the role of Generative AI (GenAI) is becoming increasingly integral. From automating dull tasks to fostering creativity, GenAI is not just a futuristic concept but a present reality enhancing our working life. Here’s a closer look at the practical applications of GenAI that are transforming businesses today, exemplifying its versatility and power across various domains:

– Content Creation and Enhancement: GenAI can draft text in any desired style and length.

Example prompt: “Generate a blog post draft on the impact of interest rate hikes on the real estate market, including key statistics and industry expert quotes.“

 

– Query Resolution in Business Operations: Find precise answers to specific business questions.

Example prompt: “Explain the recent amendments to HR policies regarding hybrid work environments and their implications for employees.”

 

– Communication Tone Adjustment: Tailor the tone of workplace communications to fit the intended audience.

Example prompt: “Revise this performance review email feedback to make it more constructive and motivating, focusing on growth and development opportunities.“

 

– Information Summarization: Distil complex documents into easy-to-digest formats.

Example prompt: “Summarise the annual financial report into a concise executive summary for the upcoming board meeting, highlighting the five most significant financial KPIs using bullet points.”

 

– Complex Information Simplification: Break down intricate documents for broader accessibility.

Example prompt: “Simplify the complex legal terms in our customer service agreements for non-specialist comprehension, and include a section with bullet points summarising the most common questions about our customer service.”

 

– Customer Feedback Analysis: Analyse customer interactions for service improvements.

Example prompt: “Analyse and classify the latest batch of customer service tickets into categories of complaint types, and identify the top three areas for service enhancement.“

 

– Software Coding: Generate, translate, and verify code.

Example prompt: “I need a spending tracker app prototype. Can you generate a basic code that works on both iOS and Android using natural language descriptions?“

 

Emerging Applications:

As Generative AI (GenAI) continues to evolve, it’s opening the door to a host of new and compelling applications. These emerging technologies harness the creative power of GenAI to address complex problems and organise unstructured data. A few of the most promising and impactful applications of GenAI for businesses are:

• Intelligent Document Processing (IDP): This involves using AI to read and understand documents. Imagine you have a huge pile of Complex documents like contracts, financial statements, reports as well. IDP acts like a super-smart assistant that can quickly sift through all these documents, figure out what’s important, and even extract the specific information you need. For example, it can help a bank process loan applications faster by automatically extracting applicant details, saving hours of manual work.

• Intelligent Process Automation (IPA): This takes the idea of robotic process automation (RPA) a step further. Initially, RPA was like teaching a robot to do repetitive tasks, such as entering data into a system. But IPA adds a layer of intelligence to these robots. Now they can not only perform tasks, but also make intelligent decisions based on the data they encounter. For example, an IPA system can automatically handle customer service requests by understanding the customer’s problem and either solving it directly or routing it to the right department.

• Managing unstructured data: This is about organising and understanding data that doesn’t fit neatly into tables or databases – think of it as the messy, handwritten notes scattered across your desk. Most of the information we deal with every day, from emails and PDF documents to images and videos, is unstructured. Managing this type of data means using tools and technologies to sift through the clutter, identify the important information and organise it in a way that makes sense for future use. Consider a scenario where a lawyer needs to find specific evidence in thousands of pages of legal documents. Unstructured data management tools can help by quickly finding relevant information and organising it in an easily accessible way, saving hours of manual searching.

 

To Conclude:

In conclusion, Generative AI represents not just a leap forward in technological capabilities, but a transformative shift in how businesses operate, innovate, and compete. As we navigate this exciting landscape, the potential for GenAI to redefine roles, streamline processes, and unlock new realms of creativity is unparalleled. Whether it’s improving customer interactions, streamlining document management, or process automation, the implications for efficiency, productivity, and innovation are vast.

However, harnessing the full power of GenAI requires more than just technology; it demands expertise and vision to integrate these capabilities into the business strategy effectively.

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 transformative journey together, unlocking the potential of GenAI to propel your business into the future.