Author: altilia

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.

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.

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.

1. The AI skills shortage
2. 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?

Altilia prioritizes AI democratization as the core of its approach: for this reason, the Atlilia Intelligent Automation platform is designed to maximize intelligent process automation capabilities while minimizing the complexity of managing AI algorithms for the end user.

• Thanks to the use of pre-built algorithms, based on transformer models, users can leverage AI capabilities even without the in-depth mathematical and statistical knowledge related to AI applications.
• Thanks to the use of a no-code visual interface for document annotation and review, users can autonomously prepare the dataset that will be used by AI algorithms, even without the technical skills required to implement machine learning or deep learning models.
• Altilia’s IPaaS cloud infrastructure, equipped with pre-built software connectors, greatly simplifies software interoperability, and removes the complexity of managing computing resources and data storage.

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.

As 2022 is coming to an end, we want to take a step back and have a look at the state of the industry of AI for business automation.

McKinsey has just published their “State of Artificial Intelligence 2022” report, based on a survey of from 1,492 participants representing the full range of regions, industries, company sizes, functional specialties, and tenures.

The report demonstrates the growth and importance of AI to organizations, with a set of companies seeing the highest financial returns from AI continuing to pull ahead of competitors.

The results show these leaders making larger investments in AI, engaging in increasingly advanced practices known to enable scale and faster AI development, and showing signs of faring better in the tight market for AI talent.

There were two key takeaways from this important report:

1. AI adoption is increasing, and the market is growing.
2. Emerging AI leaders are reaping the rewards.

AI adoption is increasing, and the market is growing

Global AI adoption is increasing: companies adopting AI in at least one business area increased by 2.5 times since 2017. The average number of AI capabilities used by organizations has increased from 1.9 in 2018 to 3.8 in 2022.

Investments in AI have also increased: % of companies whose digital budget went for more than 5% to AI increased from 40% in 2018 to 52% in 2022.

Most used embedded capabilities:

• Robotic Process Automation 39%
• Computer Vision 34%
• Natural Language Processing 33%
• Deep Learning 30%
• Knowledge Graphs 25%
• Reinforcement Learning 20%
• Transformers 11%

Altilia is exceptionally well positioned, since its offer is strong for both widely adopted embedded capabilities (RPA, Computer Vision, NLP) as well as new emerging capabilities whose value is not yet fully understood by the market but will become critical in the coming years (Knowledge Graphs, Reinforced Learning, Transformers).

With companies starting to understand the cross-functional potential of AI automation application, this also puts Altilia in a good position as the Altilia Intelligent Automation platform is designed to let customers leverage the same infrastructure to implement multiple use cases. This helps to increase the ROI of the platform over time.

Emerging AI leaders are reaping the rewards

McKinsey recognizes a group of “AI high performers”, a group of companies that are being particularly effective with the implementation of AI projects. Companies whose EBIT have increased more than 20% from the use of AI (8% of total respondents).

These high performers are more likely than others to follow ‘frontier’ core practices that unlock value:

• Adopting practices for AI development at scale (AI Ops).
• Embracing modular AI architecture to rapidly accommodate new AI applications.
• Introducing quality control procedures to provide high-quality data to feed AI algorithms.
• Leverage emerging low-code and no-code programs to speed up the creation of AI applications.

These AI high performers are also more likely than other companies to report that they engage in practices to mitigate AI related risks, testing the validity of models and monitoring them over time for potential issues.

Altilia is again well positioned since Altilia Intelligent Automation is designed to let customers ‘outsource’ the complexity of AI Ops, a topic we will investigate further in a subsequent blog post.

Our platform has a modular architecture and it allows to simplify the deployment of multiple AI applications, and it’s also designed as a low-code/no-code platform, to simplify the creation of AI applications.

It is important to understand that Altilia Intelligent Automation combines the use of Large Language Models (LLMs) with no-code technology, and this constitutes a breakthrough.

LLM are already popular and publicly discussed, but those are general purpose technology that doesn’t perform very well for specific business applications.

By utilizing Altilia’s no-code approach to AI models training, these ‘general purpose’ technologies can be fine-tuned by enterprises, to turn them into a ‘domain-specific’ technology.

This will allow businesses of all sizes to experience the benefits of LLM technology in their specific business applications, allowing them to obtain returns on AI investments, comparable to the “high performers” mentioned by McKinsey.

The magical promise of artificial intelligence is that it learns as it goes along and develops additional data insights that enable your organization to make rapid progress.

Now Adaptive AI has been identified by Gartner as one of the top 10 emerging trends for 2023, taking AI capabilities to the next level where it is able to absorb learnings even as it’s being built.

They estimate that by 2026, enterprises that have adopted AI engineering practices to build and manage adaptive AI systems will outperform their peers in the operationalizing AI models by at least 25%.

In a recent Gartner article, they write: “Adaptive AI brings together a set of methods (i.e. agent-based design) and AI techniques (i.e. reinforcement learning) to enable systems to adjust their learning practices and behaviors so they can adapt to changing real-world circumstances while in production.”

What is Adaptive AI?

So, what is Adaptive AI and how does it differ to our current understanding of how AI works?

The key is that Adaptive AI can revise its own code to adjust for changes that weren’t known or predicted when the code was first written, enabling adaptability and resilience to be built into the design so that it can react immediately to changes.

It means that the “learning” phase of a traditional AI system can be bypassed so that the AI is effectively learning whatever is happening.

The value of operationalized AI lies in this ability to rapidly develop, deploy, adapt and maintain AI across different environments in the enterprise.

AI models with this self-adaption built in can develop quicker and with less errors. It creates a faster and more superior user experience by adapting to changing real-world situations.

Altilia at the forefront

Altilia is at the forefront of this approach to optimizing artificial intelligence to take it to the next level.

In our platform, we use reinforcement learning to improve the accuracy of our machine learning models over time.

Also, as a method, we have designed a human-in-the-loop feedback cycle that allows users to trace back the extracted data points to the original source (i.e. the exact position within the document).

This allows them to validate data, and the resulting feedback is taken into consideration to re-train the AI model. In this way the model can improve their accuracy over time and prevent data drifting.

It also means that if the format or layout of the processed documents is gradually modified over time, the algorithm is capable of adapting without the need to refactor our solution.

Why not schedule a demo of Altilia’s ground-breaking AI intelligent document processing solutions and see how it can revolutionize your organisation’s use of your data?

You can sign up here for your demo. 

HyperAutomation (HA) is rapidly moving from just being an interesting proposition for highly innovative projects and applications to becoming a critical business need for organizations across any industry.

Today, and in the coming months, the threat of possible recession scenarios, demands businesses make greater efforts to improve their cost efficiency, agility, productivity, and innovation-based resilience.

According to a recent forecast by Gartner, by 2025 the HA software market is expected to reach a market value of nearly $860 billion worldwide, with a Compound Annual Growth Rate (CAGR) of 12.3%.

Massive impact

In this scenario, the impact of Intelligent Document Processing (IDP) platforms that are capable of integrating hybrid AI approaches based on computer vision, Neuro-linguistic Programming (NLP), knowledge representation, rule-based methods, and other AI technologies, will be massive.

These platforms will accelerate digital transformation across most verticals, with highest adoption rates in industries like financial services (banking, insurance, investing etc.), telecommunications, media, utilities, manufacturing, and services.

Use case applications will provide:

• Improved personal and organizational efficiency,
• Improved user interactions and satisfaction of both customers and employees,
• More efficient and agile work methods,
• Improved Robotic Process Automation (RPA) application potential for processes requiring human cognitive capabilities,
• Enhancements and performance improvements for pre-existing software solutions and systems within the organization, like RPA, ECM, ERP, CRM.

Companies are increasingly experimenting and implementing solutions based on Intelligent Document Processing (IDP) and Intelligent Process Automation (IPA) approaches and tools.

In parallel, we are witnessing a growing interest in Hybrid and Composite AI solutions that enable close interactions between machine and human intelligence, by combining semantic and ML approaches with no-code/low-code and conversational user interfaces (CUIs).

This paradigm shift is enabling organizations to gradually change their approach for the automation of operational and decision-making tasks, involving business users more closely in the process.

At this rate, in the next 3 to 5 years we expect some of the most advanced IDP capabilities to become a standard market requirement.

This includes:

• The capability to ingest and process documents and contents from several different unstructured sources and in various formats.
• Semantic document and data indexing capabilities.
• Interfacing and interoperating capabilities with other applications in the customer’s software ecosystem.
• The ability to manage end-to-end business processes automation as a comprehensive suite or in tandem with other RPA solutions

Additionally, in the companies where the RPA revolution is already taking place, we expect progress to move forward only as these solutions will become increasingly integrated with IDP platforms, in order to unlock the untapped potential of automating processes involving the handling of unstructured data present in documents, texts and other unstructured sources.

These are exactly the kind of capabilities Altilia is pushing forward with the Altilia Intelligent Automation™ platform, setting the bar for end-to-end IDP platforms technological advancements.

Over the next 3 to 5 years, Altilia will continue to address the increasingly complex HyperAutomation needs of organizations, acting as a strategic partner to facilitate customers’ strategic investments in HA/IPA/IDP initiatives, from the simplest tactical implementations to the more disruptive and valuable innovations.

For more information on how HyperAutomation can transform your business, contact Altilia here .

As we have seen, HyperAutomation (HA) is becoming an important strategic trend for organizations seeking to rapidly identify and automate the latest possible number of business processes to drive efficiency, agility and productivity.

The HA label, as identified by Gartner, brings together a range of AI and automation technologies – and, most importantly, a determination to break away from a siloed approach to address multiple labour-intensive workflows holistically.

Typical use case applications for Intelligent Document Processing (IDP) and Intelligent Process Automation (IPA) technologies can be found in both front and back office processes of organizations of any size in any industry, including Public Administration.

Some typical use cases include the following:

• Banking and Insurance customer onboarding operations that require the reading of ID documents, financial documents and income statements.
• File verification and validation in Public Administration, requiring data extraction from very large and varied sets of documents.
  In the field of legal contract management, support for terms and clauses interpretation and verification.
• Automated reading and classification of contents from regulatory compliance reports for Risk Management and Internal Audit purposes.
• Automating procurement and sales operations processes, like reading and extracting data from orders, payments, contracts, invoices and bills, is becoming an increasingly common practice.

Some even more advanced use case scenarios include:

• Automated documents and images processing for insurance claims management.
• Automated data collection from balance sheets, notes to balance sheets, business plans and financial statements for credit scoring purposes in the mortgage and lending management process.
• Automating ESG (Environmental Social & Governance) company profiling, based on the information and data extracted from multiple sources, like annual reports, sustainability reports, websites, news, etc.
• Supporting NPL (Non-Performing Loan) and UTP (Unlikely To Pay) portfolio evaluation, by automating data extraction and collection from complex documents for the effective assessment of distressed credit.
• Automated reading of medical records and clinical trial results for patient management and new drugs development in the Healthcare and Pharma industries

Typically, the end-goal of these applications is to drastically reduce the manual work time required to read and interpret documents, to cut down data entry operations and to enable automatic verification procedures in support of sophisticated operational and decision-making processes.

A comprehensive IDP solution such as the Altilia Intelligent Automation™ platform can bring important advantages both in terms of efficiency and cost reduction, leading to end results often exceeding a 100% ROI, based on the total costs of ownership.

This is possible as an organization gains the ability to:

• Speed-up business processes by up to 10 times.
• Upscale document processing capabilities, according to real data volumes and business needs.
• Reduce manual errors and oversights.
• Drastically reduce the manual work (often by more than 80%).
• Improve employee satisfaction, by reducing the time spent on tedious and repetitive manual tasks (like data entry, document reading and inspection).
• Improve customer satisfaction as processes are handheld more quickly and accurately.
• Increase overall margins, as the improved access to data and information allows business leaders to make more informed and timely decisions.

For Altilia’s customers, an IDP platform represents a strategic investment to meet all their HA needs arising over time, allowing them to both scale up existing use cases to meet increasing business demand, as well as creating and deploying new domain-specific use cases with a unified approach.

To learn more and have a chat with Altilia’s automation experts, contact us here.

Rende/Milan, July 25th, 2022. Altilia, the Italian deep-tech company has been included in the first Gartner® Market Guide report on Intelligent Document Processing, published in February 2022 (1).

Altilia Intelligent Automation™ has been included among other representative vendor’s solutions. In another 2021 Gartner report (2) Altilia was listed among the representative IDP software provider among other companies competing in a fast-growing market.

In that same report, outlining the IDP competitive landscape, Gartner underlined the role of Unstructured data specialists.

“These are new providers in the IDP space, mostly sprung up in the last five years or so. They are usually venture capital (VC)-funded companies. Their key value proposition is that they have an AI-led approach to focus primarily on semistructured and unstructured data (often a strong focus on handwritten documents) that is their core value proposition. They mostly partner for OCR engines”. (2)

Defining the IDP market, in its first report specifically focused on Intelligent Document Processing solutions Gartner states that;

“Intelligent document processing (IDP) solutions extract data to support automation of high-volume, repetitive document processing tasks and for analysis and insight. IDP uses natural language technologies and computer vision to extract data from structured and unstructured content, especially from documents, to support automation and augmentation”.(1)

According to this Gartner’s February 2022 report, Altilia currently mostly serves industries : “Banking, insurance, utilities, retail, securities” and list most commonly process documents for Altilia: “Annual/sustainability/interim/financial reports, balance sheets, Invoices, orders, Social media posts, news, bank statements, paychecks”

Gartner lists ‘Establishing a Marketplace as a Viable Channel in Their Go-to- Market’ as one of the competitive trends about IDP Competitive Landscape

The report observes “early signs of establishing the marketplace as a channel as part of the go-to-market (GTM) approach of the IDP vendors”.(2)

Altilia is among a small numbers of vendors developing their own marketplaces.

This means transitioning from the traditional approach (bespoke project with project and implementation times and costs) to a ‘out of the box’ approach with bundled solutions ready to use.

Altilia’s team of 40 Scientists, Engineers, Innovators, Growth Hackers are building a no-code SaaS platform, that democratizes the adoption of AI, hyperautomation and decision intelligence for large corporations and SMEs in Finance and a number of other industries. This platform will be available also as a tool from the Altilia.ai website and will be exploitable from day 1 with a minimum of training without any knowledge of AI.

Reference

1. Gartner “Market Guide for Intelligent Document Processing Solutions” Shubhangi Vashisth, Anthony Mullen, Stephen Emmott, Alejandra Lozada, February 2022

2. Gartner, “Competitive Landscape: Intelligent Document Processing Platform Providers,” Arup Roy, Arthur Villa, Cathy Tornbohm, Soyeb Barot, November 8, 2021.

GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved. Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.