Gartner mentions Altilia in its 1st market guide on Intelligent Document Processing

How to facilitate the adoption of Large Language Models in pharma and finance

By altilia on November 24, 2022

After the initial enthusiasm about Large Language Models (LLMs) due to their impressive results that made them SOTA approaches for some new and traditional general purposes NLP tasks (e.g. question answering, text classification, information retrieval, token sequence tagging, entity extraction, sentiment analysis, intent detection, word sense disambiguation, POS-tagging), now it’s the time of reality.

Peraphs, LLMs are contributing more than computervision to the new AI spring. LLMs are powerful tools that can create great value in industry and help organizations to streamline business processes, improve operations, save a lot of money, and improve business performances. But, as a recent neural article optimally points out, they must be carefully adopted, furthemore they need to be fine tuned to solve specific business problems in vertical domains.

For this reason in Altilia we have built a platform that helps users to fine tune LLMs and control their results by human-in-the-loop AI, augmented intelligence, and composite and adaptive AI tools.

Users in highly specialized areas like pharma, health, and finance have to face problems cited in the article. In particular, LLMs cannot be used as they are. These models  are trained over general available data, hence specific data programming and fine-tuning techniques are needed to make them working in a trustworthy, explainable way on highly specific, regulated, privacy and security sensitive tasks.

Altilia Intelligent Automation platform leverages all LLMs available in huggingface and provides users with tools that allow to curate and program data set to fine-tune LLMs and apply them to solve any NLP and document processing task at the highest level of accuracy and trustworthiness needed by the users operating in highly regulated environments.

For more information read the following use cases: improving NPL data tapes management; enhancing credit scoring for lending applications; Extract ESG data from multiple sources; data extraction from notes to balance sheets.

By altilia on November 24, 2022

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How Intelligent Automation can impact the workplace

Intelligent Automation is being embraced by many organizations to boost revenues, increase efficiency, and improve customer experiences. As an evolution of Robotic Process Automation (RPA), Intelligent Automation utilizes a Composite AI approach to leverage multiple techniques, such as machine learning (ML), computer vision (CV), and natural language processing (NLP) to handle complex processes and support business decision making. Intelligent Automation has the potential to revolutionize the workplace, not only by freeing up time human workers dedicate to repetitive tasks but also helping companies to set new strategies and to develop new innovative products and services. Unfortunately, this potential is still largely untapped, as most companies have yet to fully embrace it. Many opportunities are still left on the table. Some of the best candidates for automation are business processes dealing with structured data (both digital and non-digital) with clearly defined stages; processes with seasonal spikes that cannot be easily met by a manual workforce (such as policy renewals, premium adjustments, claims payments); processes with strict Service Level Agreements (SLAs), requiring fast turnaround times (like transaction registration, order fulfilment, etc.). Some notable use case applications also include: Contract management: Intelligent automation can help to simplify all the steps needed to draft, send, redline, and execute contracts. Sales processes: Here automation can provide first level assistance to prospects by providing direct answers to FAQs. Quality and security compliance procedures: Intelligent automation can be leveraged to collect and pre-process data to perform initial assessments before involving a human analyst. Finance: It can be used to streamline the procure-to-pay (P2P) process. HR: Many tasks in the Hire-To-Retire (H2R) process can be automated. Customer service: Many contact center tasks can be automated, as virtual agents can handle routine tasks, freeing up human agents to handle more complex customer inquiries, with great benefits for the customer experience. Intelligent Automation has the potential to change the architecture of work by breaking it into smaller tasks and making it more events driven. In the future, fewer tasks will need to be handled manually, leaving workers with more time to focus on digital learning and knowledge work, including learning how to proactively develop solutions using low-code platforms and tools. The use of intelligent automation can boost the efficiency and effectiveness of business processes, enabling companies to meet higher standards of both customer and employee satisfaction. However, as organizations increasingly adopt intelligent automation technologies, they need to be aware of the potential risks and hazards to avoid. One of the main pitfalls is ignoring the importance of change management. This could result in long-term issues if organizations overlook the importance of having their people aligned with their overall goals. Additionally, employees may push back when automating processes that were previously carried out by a team of people. It is important to understand and take into account the concern and perceived lack of control when processes are suddenly automated. Lastly, in order for any automation project to be successful it necessary to set clear goals in advance and to establish key metrics to evaluate the impact of the initiative and to measure the ROI. Biggest challenges Scaling intelligent automation is one of the biggest challenges for organizations: therefore, it is crucial for companies to be clear about the strategic intent behind their initiatives. Success often depends on the ability of the organization to put “people’s needs” first: this means introducing new technologies in a way that is helpful and involves minimal disruption, and addressing real issues related to skills, roles and job content. In other worlds, companies should not approach the problem like a race to be the first to introduce the latest technology and neither as a way to substitute people in managing operations. Intelligent Automation can rather realize its full potential when it implemented as a means to support and augment human employees. This allows a correct balance to be found between automation and manual control, leading to the best results when determining the success of a project. The right approach If organizations take the right approach, employees can also positively embrace automation and the opportunities it creates, rather than resist it. In fact, automation can free up time on repetitive work, helping employees to focus on high-priority tasks and allowing them to move from administrative management to higher-value contributions. Forward thinking organizations will try to shift workers into new roles as the current ones get progressively automated; this way employees can learn about new technologies and how they can be leverage them. With this vision, upskilling programs, both internal and external, will be more and more a differentiating factor for companies to attract and retain the best talents. For more information on how Altilia can help you gain the full benefits of Intelligent Automation, contact us here.

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Taking on the challenge of managing complex Machine Learning Operations

As we explored in our previous blog, demand for AI-based solutions is growing year on year as organizations adopt and experiment with artificial intelligence to drive competitiveness and efficiency in a tough business environment. We examined the key talent and skills gap challenge that companies, particularly small and medium enterprises, are facing as the explosion of AI projects outstrips the supply of qualified data experts required. Now we want to go deep on another significant issue that has been identified by business leaders and consultants as a blocker to growth. And also show how Altilia can offset this challenge. Managing Machine Learning (ML) Operations When companies consider introducing new business process automation solutions, they often underestimate the complexity of managing the lifecycle of an AI project. ML Ops are defined as a set of practices that combine the implementation of Machine Learning, DevOps, and Data Engineering models, with the goal of developing, releasing, monitoring, and the scaling into production high quality ML systems. This is done by leveraging organizational, cultural, and technological aspects that support the governance and automation of the ML model lifecycle. Properly managing ML Ops is a major challenge even for large and structured companies. In fact, according to a recent survey by the Artificial Intelligence Observatory of the Polytechnic University of Milan, out of a sample of 80 large companies operating in Italy that have already started AI projects, 71% say they have not yet introduced ML Ops as a structured practice. Among the stated reasons for this delay stands out the lack of time they devote to AI initiatives compared to traditional business activities (43%) and the lack of internal expertise to manage ML Ops (33%), an issue which brings us back to the challenge discussed in our last blog. Deloitte identified this ML Ops issue in their State of AI report: “Despite evidence that establishing clear processes and redefining roles to deliver quality AI results in improved outcomes, there has been little growth in the market in terms of adopting such practices, according to survey respondents. In both the fourth and fifth editions, just one-third of respondents reported that their companies are always following MP Ops, redesigning workflows and documenting AI model life cycles.” The ML Ops cycle in its complexity could be broken down in three distinctive parts: The Business process analysis/understanding part refers to the mapping of internal processes, systems, and any other key element to understand the "problem" to be solved: such as defining requirements, objectives, key outcomes, and formulating use case hypotheses for ML applications. The Data & Model Preparation part concerns the collection and refinement of data to train the ML model; the selection of the ML model with the best fit to solve the problem; the testing of different algorithms, features and hyperparameters; keeping track of all experiments, and maximizing code reusability. Continuous Operations & Improvement refers to the continuous update of the solution with each product release; the continuous training of the algorithm, with the storage and processing of new data to update the model; the continuous monitoring of the model to keep track of changes in performance and accuracy. How can Altilia help to overcome the ML Ops challenge? Altilia’s approach is offering a comprehensive platform to manage the whole AI implementation lifecycle with an end-to-end approach. The platform is built overtime and improved upon experience by Altilia, to hide the complexity of managing ML Ops, and to simplify the training and the optimization of AI models over time. This eliminates the need to dedicate an internal team (with limited experience) just to maintain an AI implementation project, to monitor its performance over time and to manage its infrastructure. Additionally, the automation challenges faced by businesses are rarely completely unique. Using a platform approach, Altilia can replicate pre-tested use case specific solutions and adapt them to perfectly fit the needs of the customer. This greatly simplifies the process analysis, reducing the uncertainty when defining implementation goals, a leading to a greater confidence about the expected outcomes. Altilia simplifies data and model preparation by giving access to library of generalized pre-built AI models, including Large Language Models (LLMs) that can be trained and adapted, according to the customer’s specific requirements. The training of models is simplified, thanks to our document annotation interface, that allows users to easily generate examples for the system to process. Lastly, Altilia facilitates the monitoring and fine-tuning of AI models to increase their accuracy over time and prevent data drift. Contact Altilia here to learn how our unique AI technology platform can help your organization overcome these challenges.

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How Altilia can help you bridge the AI skills gap

The demand for AI-based solutions in the enterprise is ever increasing as more and more companies view artificial intelligence as a solution to automate core business process, driving operational excellence and accelerating digital transformation. Top-level research from the Artificial Intelligence Observatory of the Polytechnic University of Milan shows that, from a sample of 200 large organizations operating in Italy, 59% have already started executive projects in the AI field, 77% of which are running more than one project in parallel. According to a recent survey by PwC in US, on a sample of 1000 large organizations, 72% have already integrated or are looking to integrate AI technologies into their operations. Full-scale deployment Deloitte’s significant State of AI study found that 79% of business leaders surveyed reported full-scale deployment for three or more types of AI applications – up from 62% in the previous year. And 76% of those leaders expect AI investments to increase in the next year. But not everything in the AI garden is rosy, particularly for small and medium sized organisations. There are many challenges affecting the effective use of artificial intelligence which can hinder the ability to tap into its potential and lead to a growing gap between those companies that have the capability and those who do not. Over two blogs, we will focus on the two big challenges facing organisations planning to use AI – and how Altilia can make the difference. The AI skills shortage The complexity of managing Machine Learning Operations The AI skills shortage McKinsey’s State of AI 2022 Survey called out the difficulties of hiring AI-related roles, with 78% of respondents citing difficulty with hiring AI data scientists, 72% struggling to hire data architects, 70% ML engineers and 69% data engineers. Perhaps surprisingly, they state that finding AI hires is perceived as more difficult than three years ago, with 57% saying that the hiring of data engineers is more difficult, 52% for data architects and 48% of ML engineers. Clearly, demand for top AI talent is outstripping supply. Among the limiting factors, the skills problem emerges as one of the most relevant. In addition to programming and software development skills, the planning and life-cycle management of an AI project, requires highly specialized skills that are still scarcely available in the labour market. These technical skills include: Skills needed to identify and prepare datasets that will be used by AI algorithms. Mathematical and statistical skills that enable the solving of complex problems related to AI applications. Skills needed to implement Machine Learning and Deep learning models (e.g., Supervised & Unsupervised Learning techniques). Skills related to data storage infrastructure, cloud services, and integration of AI systems with other enterprise software and applications. In addition, since AI automation projects are ultimately aimed at boosting operational business processes, it is crucial to create a match between AI skills and all the skills involving the application of specialized domain-specific knowledge, methodologies, and operational techniques for the operation of specific areas, such as Supply Chain, Marketing and Sales. For this reason, a core challenge is to bridge the gap between technical AI skills and domain-specific competences within companies and bringing core AI skills to figures not traditionally related to engineering and software development. How can Altilia help to overcome the skills gap challenge? 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In addition, the Altilia Intelligent Automation platform is designed for to reduce the learning curve for business-domain experts to understand and apply AI algorithms for business automation. In fact, the Altilia Labels module for document annotation, allows business-line users to intuitively transfer their knowledge into algorithms, to perfectly fine-tune the solution according to real business and process needs. The Altilia Reviews module provides a tool to review and validate the results produced by the algorithms, this is a crucial feature to guarantee AI explainability and to allow business-line users to effectively govern the process. Contact us here to discuss how Altilia can support your organization’s drive for business optimization through AI. And read our next blog on the challenge of managing ML Ops.

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