As part of Solutions Review’s Premium Content Series—a collection of contributed articles written by industry experts in enterprise software categories—Uri Haramati, the founder and CEO of Torii, outlines some of the factors that keep IT leaders up at night.

While it may be true that as many as 99 percent of IT professionals are happy with their jobs, that doesn’t mean it’s a field devoid of work stress and pressing concerns. The specific reasons may change, but the underlying issues facing IT departments have not—security, costs, efficiency, and staying ahead of the tech curve. (Or, at the very least, remaining up to date).

The truth is, with greater technological power comes greater responsibility—on the part of IT. An excellent example of this applies to the proliferation of cloud-based software, or SaaS. While these tools have become indispensable, especially in our modern workplace, which has become increasingly digital and siloed, the drawbacks are well known. 

For one, the more apps that trickle through your organization, the larger the attack surface for malicious actors. When you consider the average desk employee is using 11 apps (up from six in 2019), it’s no surprise that IT leaders have a lot to stress over regarding SaaS management, spending, user authorizations, security keys, and the like.  

The SaaS proliferation at your organization may not even fully account for shadow IT—the unsanctioned apps that employees purchase or license for free outside of the tech team’s purview. The emergence of shadow IT, or distributed SaaS adoption, across departments is fueled by a younger, tech-savvy workforce that knows what software they need to succeed and how to get it. With that, let’s delve into three SaaS-related issues keeping IT departments up at night. 

62% of IT leaders: Cybersecurity threats pose a danger to long-term success 

Still top of the list, but just barely (we’ll get to that later), IT leaders remain vigilant about security threats across the board. Regarding remote, cloud-based workplaces born out of the pandemic, IT leaders are especially conscious of the increased threat that shadow IT brings and the lack of preparedness to combat its risks.

As noted above, the broader the attack surface, the more susceptible businesses are to cyber threats. This may be why 68 percent of survey respondents said they prefer a centralized IT department to obtain full transparency and control of SaaS management. Gartner reported organizations that fail to centrally manage SaaS lifecycles are five times more susceptible to a cyber incident or data loss. However, it’s important to remember that centralized models can hinder scalability for IT teams that are stretched thin and can’t assess and manage their SaaS ecosystem effectively.  

75% of CIOs: Inability to identify wasted spend is a threat to company success 

Hot on the trail of cybersecurity threats (essentially tied with it) is spending concern: 61 percent of survey respondents cited the inability to identify wasted cloud app spending. More telling, though, is that 75 percent of CIOs recognized this concern, compared to 66 percent of VPs, 53 percent of directors, and just 35 percent of managers.  

The data clearly indicates that the higher you are on the IT food chain, the more worried you are about wasted spend. While that may seem obvious, it’s a stark difference from other IT concerns we surveyed (security, outdated technology), where alignment was much tighter across roles. Notably, CIOs are aware of the blind spots created by shadow IT and have read enough reports about how much it is affecting their company’s bottom line.  

According to Gartner, SaaS apps cost businesses $1,040 annually per employee, compounded by app providers gradually raising prices against a challenging macroeconomic climate (IT departments stand to overspend on SaaS by at least 25 percent through 2027.)  As a result, IT leaders understand the importance of overseeing and streamlining their total SaaS stack, particularly about two key indicators—software redundancy and underutilization.  

73% of CIOs: Lack a strategic framework for app rationalization and license usage 

While distributed SaaS adoption across business units is difficult to corral, the benefits of building self-sufficiency and workplace culture are real. The question then becomes, if employees continue to acquire apps that count toward a company’s SaaS stack, what is the best method to ensure costs, security, and anything else keeping IT folks up at night, are kept in check?  

By acknowledging that shadow IT exists, as well as the financial ramifications that follow, IT leaders are in a position to address these concerns through the utilization of SaaS management tools. The issue is 73 percent of CIOs feel they lack the strategic framework to rationalize their organization’s app usage. Under this umbrella are many oversights, including redundancies (e.g., identifying and reducing apps that have duplicate functionality), unused and underutilized licenses, licenses belonging to former employees, the value of premium vs standard licenses, and so on.  

By utilizing tools such as SaaS management platforms that centralize visibility into all sanctioned and unsanctioned apps—as well as their users, usage, costs, and licenses—under one roof, IT leaders can make informed decisions and, most importantly, act on them expeditiously. Beyond just deleting unused apps and licenses or eliminating redundancies, the data provided by these tools can also help IT leaders better negotiate future license agreements based on real-time data.  

Solutions like these can also solve the scalability issue that plagues centralized IT models. With built-in automation, they can tackle repetitive yet crucial tasks (e.g., onboarding and offboarding employees from apps, identifying shadow IT usage) while allowing IT leaders to maintain a 360-degree view of their app ecosystem while freeing up their time. So, before you lose any more sleep, consider how your team can more easily get a grip on your SaaS management. If 99 percent of IT pros are, in fact, happy at work, let’s do everything we can to keep it that way.

The post IT Leaders: What Keeps You Up at Night? appeared first on Best BPM Tools, Vendors, Software and BPMS.

The editors at Solutions Review have compiled this example low-code automation platform RFP template to help your organization select the best enterprise software for its business needs.

The rapid evolution of technology has brought a new era of business automation. Gone are the days when businesses relied heavily on manual labor to conduct their day-to-day operations. Today, low-code automation solutions have emerged as a viable option for companies looking to streamline their processes, reduce costs, and improve efficiency. However, choosing the right low-code system for your business can be daunting. One way to simplify the process is by using a request for proposal (RFP) template to solicit proposals from potential vendors.

In addition to simplifying the vendor selection process, an RFP template can offer other benefits. For example, it can help you standardize your requirements and evaluation criteria, making comparing proposals from different vendors easier. It can also help ensure you don’t overlook critical requirements or considerations when evaluating solutions. With that in mind, the Solutions Review editors have compiled an example low-code automation platform RFP template to help you and your team get started.

A Low-Code Automation Platform RFP Template Example

The post A Low-Code Automation Platform RFP Template Example appeared first on Best BPM Tools, Vendors, Software and BPMS.

As part of Solutions Review’s Premium Content Series—a collection of contributed articles written by industry experts in enterprise software categories—Dr. Stefan Sigg, the Chief Product Officer at Software AG, outlines how data can help alleviate some of the challenges involved in multi-cloud integration projects.

Most businesses find themselves managing multi-cloud environments, regardless of whether it was in their plans. Through mergers and acquisitions, legacy data and infrastructure requirements, and historical decisions, organizations see their critical business information siloed on different clouds. The irony of the situation is that this data isolation invalidates one of the original promises of cloud computing: that information can be accessed from anywhere. 

The problem has grown enough that eight out of 10 enterprises say managing multi-cloud environments is one of today’s biggest challenges. According to the 2023 Flexera State of the Cloud report, managing these bespoke services becomes more complex as organizations consume more services from multiple cloud vendors. Having valuable data fragmented across the locations makes it difficult to ensure that it’s consistent and up-to-date, leading to missed business opportunities and bad customer experiences. 

The problem certainly isn’t going away, and the rapid rise of the adoption of generative AI for business applications will only create further complexity for the data landscape as organizations scramble to adopt the latest and hottest in tech. It takes massive amounts of data to train the large language models required by generative AI, and a lack of sufficient data can lead to inferior models. 

The need to navigate the barriers of multi-cloud integration and ensure enterprises stay intrinsically connected makes it critical to get data out of its depository and keep it moving. Putting data in motion begins with integration platforms that can be designed once and deployed anywhere using an iPaaS (Integration Platform as a Service) model.

These iPaaS services integrate data between various apps, clouds, and databases not designed to talk to each other. The best are vendor-agnostic, allowing businesses to use the cloud providers that best match their needs and goals. The real business value, however, comes as the platform makes this data flow seamlessly along the business processes as they jump from one app to the other in an IT landscape. 

Behind the scenes, integration connectors work the real magic—linking applications, databases, SaaS platforms, and more. Connecting those systems also unlocks powerful data analytics, which opens the path to automating complex workflows. Even simple workflow automation can get tricky as more parties and systems get involved, so it’s essential to have reliable connectors as the foundation. 

To show how multi-cloud integration might work in the real world, consider a company working to transform itself from a traditional manufacturer into a digital business. It’s not unusual to have years of work and millions of dollars invested in core business technologies that have accumulated over time. And multiple stakeholders—plant managers, production executives, engineers, and quality control experts—rely on all kinds of data created by various systems to keep things running smoothly.

For that reason, companies can’t tear out one infrastructure and replace it with another. Smart integration provides a better path forward. The first step would likely be standardizing internal processes and IT systems to eliminate wasted effort and increase efficiency. Even a tight focus on one company division could result in streamlined processes via an integrated architecture. 

Once the platform is established, it can form the basis for all new projects and enable them to start from a clean slate. In addition to efficiency gains, new revenue streams from innovative digital services are possible with a more connected enterprise. 

Integration strengthens a company’s digital backbone—the fundamental, structural support for all its systems, apps, networks, and employees—an important aspect of technology modernization, a top priority for enterprise CIOs as they manage multi-cloud environments and look to extend their IT portfolios. The speed of business today means that companies with outdated and siloed data will almost certainly lag behind their competitors. The more connected a company can make its infrastructure through integration, the more future-proofed it is.

The post Putting Data in Motion: Taking the Challenge Out of Multi-Cloud Integration appeared first on Best BPM Tools, Vendors, Software and BPMS.

As part of Solutions Review’s Premium Content Series—a collection of contributed articles written by industry experts in enterprise software categories—Frank Laura, the Chief Technology Officer at EngageSmart, explains why CIOs and CTOs should consider working with an AI implementation team when working with the technology.

I have spent over 17 years working as a CIO or CTO for major companies, and during that time, I have seen multiple novel technologies significantly shift the corporate landscape. With each, companies have rushed to take advantage of the innovation—which, predictably, has led to mixed results. Each organization’s approach to implementation determined whether a given tech shift was beneficial to a company and whether the rollout was smooth or rocky. 

IT leaders across industries are now striving to adapt to the game-changing technology of AI as quickly, powerfully, and safely as possible. In the face of this technological disruption, one of the essential pieces of advice I can offer for leveraging AI at your organization is this: Build the best dedicated cross-department working group you can for the task. 

Team Members To Include in an AI Implementation Working Group 

An interdisciplinary working group can help a CTO or CIO make informed decisions about AI implementation that are more likely to stand the test of time and benefit the entire company—not just its technology. Creating a group representing all necessary considerations means soliciting input from diverse stakeholders: legal, IT, compliance, security, sales, marketing, HR, and others, depending on the specific tool being considered and the company’s industry and goals.  

Human resources is perhaps a less obvious group to draw expertise from, but their involvement is critical. All new tools implemented have implications for the teams using them, whether it’s a simple shift in a protocol or a need for further training and continuing education. In the case of AI, pop culture conversation has raised understandable concerns about how AI tools will affect the jobs of those in specific roles, such as marketing and administrative work. Involving HR in the working group will help create the necessary lines of communication between your organization’s decision-making bodies and your team members.  

Taking a Measured Approach

While some CTOs and CIOs may be unsure where to begin integrating AI functions into their organization’s workflow, my experience has shown me that ignoring their possibilities could spell obsolescence. While the initial buzz of cloud software, which was discussed with similar fervor in recent years, has given way to a steadier pace of implementation as new uses are explored, it’s become clear that companies that failed to consider its services have been left behind. AI’s even more meteoric rise suggests that the consequences of hesitation may be even greater. 

At the same time, moving too quickly without proper understanding can lead to problems of its own. As it stands, individual employees of an organization are likely to experiment with AI-powered tools for daily tasks. For example, maybe they’re using a free version of a tool to compose emails or parse reports. By charging headlong into the promise of new tech, these likely well-meaning team members may inadvertently give away potentially proprietary business information to be integrated into those tools’ data sets and learning models. This is another reason it’s essential to involve different teams within the organization—including legal and HR—in establishing guardrails and guidelines around how AI is used in the workplace. 

Encouraging an Informed Implementation

Everyone who touches AI should understand what it can and cannot do. The ability to deliver on AI’s promise is contingent on knowing what is possible today and what is still hypothetical or a misunderstanding of AI’s functions. Pop culture has led to much hype and misinformation around AI and its capabilities. While a CTO or CIO might take the ability to understand evolving technology for granted, some of the working group’s other members can likely benefit from succinct explanations to help them do their jobs as effectively as possible.

It’s wise to include a team member who can provide accessible information for everyone else involved in the decision-making process. If everyone operates with the same understanding of AI, whether a generative language model or simply process automation, your guideline rollout will be more efficient. Establishing a common language, beginning with the decision-making process, can help.  

The AI Sandbox 

Without encouraging you to skip ahead in your company’s AI journey, some IT leaders have ventured to create secure, contained “sandboxes” for experimenting with AI. These highly controlled spaces with restricted access can help team members to test the limits and benefits of these new tools in a setting with closely monitored fences around it to help protect your company’s data and information.  

If in doubt, your organization should consider relying on a simple rule when approaching AI: do not use any company information or assets while interacting with AI without explicit approval from the cross-department working group. In my opinion, a measured response to new technology is the most responsible one. A knowledgeable AI implementation team with perspectives from different areas of expertise and an overarching goal of moving the company forward will help your organization leverage AI safely. 

The post Why Your Company Should Consider an AI Implementation Working Group appeared first on Best BPM Tools, Vendors, Software and BPMS.

The editors at Solutions Review have curated this list of the most noteworthy WorkTech news from the week of June 30th. This round-up covers announcements and updates from companies like Jitterbit, IBM, Mendix, and more.

Keeping tabs on the most relevant ERP and BPM news can be time-consuming. As a result, our editorial team aims to summarize the week’s top headlines in the enterprise technology marketplace. The Solutions Review editors will compile a weekly round-up of vendor product news, mergers and acquisitions, venture capital funding, talent acquisition, and other noteworthy WorkTech information. With that in mind, here is some of the top WorkTech news for June 30th.

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Top WorkTech News From the Week of June 30th, 2023

Apptio Announces a New Integration with Oracle Cloud Infrastructure (OCI)

Apptio—a technology spend and value management company—has announced its latest integration with Oracle Cloud Infrastructure (OCI). The new integration combines Apptio’s cloud cost management expertise with OCI’s service to help businesses understand, visualize, and optimize their cloud-based environments through direct access to Apptio’s Cloudability product suite. The integration is scheduled to be completed in late 2023.

Read on for more.

Boomi Releases its Platform on the AWS Marketplace

Boomi—an intelligent connectivity and automation solution provider—has announced that the Boomi platform is now available as an intelligent automation solution in the AWS Marketplace. Now that Boomi’s Integration Platform as a Service (iPaaS) platform is on the Marketplace, AWS customers can use Boomi’s low-code platform to accelerate automation initiatives by connecting and integrating data sources, systems, and applications across the enterprise.

Read on for more.

Fly.io Raises $70 Million in a Series C Funding Round

Fly.io, a tech startup offering a platform for running full-stack apps, has raised $70 million in a Series C funding round, bringing the company’s total valuation to $467 million. The funding was led by EQT Ventures with additional participation from existing investors Andreessen Horowitz, Dell Technologies Capital, and Intel Capital. Fly.io plans to use the new capital to continue to develop its platform, invest in advanced storage capabilities, expand security features, and more.

Read on for more.

IBM Reveals Plans to Acquire Apptio

IBM has entered into a definitive agreement with Vista Equity Partners to acquire Apptio—a financial and operational IT management and optimization (FinOps) software provider—for $4.6 billion. The acquisition will help IBM advance its IT automation capabilities, expand its hybrid cloud investment, and enable enterprise businesses to deliver deeper business value across their technology investments. The transaction is expected to close in the latter half of the year.

Read on for more.

Jitterbit Releases New Updates for its Low-Code Application Development Platform

Jitterbit, a low-code automation solution provider, has announced the latest version of Vinyl, its low-code application development platform. The updated version introduces new features to expedite enterprise application development and streamline developing and adding elements. New features include updates to the Live Designer editor tool, security improvements, “advanced intelligent wizards” to automate developer workflows faster, and infrastructure and hosting enhancements.

Read on for more.

Mendix Announces the General Availability of Mendix 10

Mendix—a Siemens business and global provider of enterprise application development solutions—has announced the general availability of Mendix 10, the latest release of its low-code platform. The new update introduces multiple features to help users meet the demand for software, prepare for composability, and more. Updates include expanded AI and machine learning capabilities; new tools for fostering business-IT collaboration; more cloud deployment options; a streamlined development experience; and a revamped approach to governance and control.

Read on for more.

The Project Management Institute and Oracle Detail a New Collaboration

The Project Management Institute and Oracle Construction and Engineering have announced a new collaboration that will enable them to support professionals in the construction and asset-intensive sectors. The partnership will integrate Oracle resources into The Project Management Institute’s existing Construction Professional in Built Environment Projects (PMI-CO) coursework and certification program. Additionally, the companies will participate in joint events, roundtables, programs, and webinars.

Read on for more.

Expert Insights Section

Watch this space each week as the Solutions Review editors will use it to share new articles from the Expert Insights Series, Contributed Shorts videos, Expert Roundtable videos, event replays, and other curated content to help you gain a forward-thinking analysis and remain on-trend with changing industry expectations. All to meet the demand for what its editors do best: bring industry experts together to publish the web’s leading insights for enterprise technology practitioners.

The Best AI Certification Programs for Business Managers

As Artificial Intelligence (AI) technology continues to become a juggernaut presence in nearly every industry, there’s never been a better or more critical time to outfit yourself with an up-to-date understanding of the technology. With that in mind, we’ve compiled this list of the best AI certification programs business professionals can take to improve their expertise, broaden their skills, stay on top of the newest emerging trends and tools in the AI landscape, and advance their careers.

Read on for more.

As part of Solutions Review’s Premium Content Series, Mike Bradford, the Strategic Business Development Director at DELMIA, gives an in-depth review of what a Manufacturing Execution System (MES) is, explains why it’s an essential tool for manufacturers to consider, and outlines some of the critical differences between Manufacturing Operations Management (MOM) and MES solutions.

Read on for more.

Harnessing Industry-Specific AI Models to Improve AI Accuracy, by Assaf Melochna of Aquant

As part of Solutions Review’s Premium Content Series, Assaf Melochna, the President & Co-Founder of Aquant, explains how companies can use industry-specific AI models to improve the accuracy of their AI efforts. In the article, Melochna explores industry-specific AI models’ ability to learn over time, adapt to real-world feedback, leverage subject matter expertise, integrate multiple data sources, improve accuracy, and more.

Read on for more.

For consideration in future WorkTech news round-ups, send your announcements to wjepma@solutionsreview.com.

The post Top WorkTech News From the Week of June 30th: Updates from Jitterbit, IBM, Mendix, and More appeared first on Best BPM Tools, Vendors, Software and BPMS.

The editors at Solutions Review have compiled a breakdown of the differences between Robotic Process Automation (RPA) and Business Process Management (BPM) technology solutions.

In today’s rapidly evolving technological landscape, businesses need tools to help them enhance their operational efficiency and productivity. Robotic Process Automation (RPA) and Business Process Management (BPM) have emerged as some of the most effective and widely adopted approaches to achieving these business objectives. While both RPA and BPM focus on process optimization, they differ in their fundamental principles and implementation.

With that in mind, the Solutions Review editors have compiled definitions for RPA and BPM technologies and provided a simple rundown of what makes them different but equally valuable solutions in an enterprise’s evolving tech stack.

Defining Robotic Process Automation (RPA)

Robotic Process Automation, or RPA, refers to using software robots or intelligent automation tools to automate repetitive and rule-based tasks within business processes. These bots are designed to execute specific, structured processes (i.e., manual data entry, transaction processing, data extraction, data manipulation, etc.) with speed, accuracy, and consistency, freeing human workers from mundane and time-consuming activities. Additionally, RPA solutions typically automate selected tasks or subprocesses to optimize day-to-day operations.

Defining Business Process Management (BPM)

The concept of business process management has been around for centuries, first utilized during the industrial revolution. The dawn of computer technology brought the framework for BPM as we know it today, which now encompasses the identification, design, execution, monitoring, and continuous improvement stages of business processes to help businesses achieve and maintain operational excellence. With BPM solutions, organizations can analyze workflows, eliminate bottlenecks, enhance experiences, find ways to strengthen internal collaborations, streamline processes, align those processes with strategic goals, and ensure adherence to any relevant business rules or regulations.

4 Key Differences Between RPA and BPM

While RPA and BPM share the common goal of process improvement, there are fundamental differences between the two approaches. Here are four of the most notable distinctions:

Focus and Scope

RPA primarily targets task-level automation, focusing on specific repetitive activities within an existing process or system. It uses tactical automation to address particular pain points, reduce unnecessary human intervention, increase accuracy, and enhance speed. On the other hand, BPM takes a broader approach and concentrates on end-to-end process management that addresses the entire lifecycle of a process, including its design, execution, monitoring, and optimization.

Automation Capabilities

RPA tools focus on automating structured, rules-based, and repetitive tasks. These tools can emulate human actions interact with user interfaces, and work across applications, making them suitable for automating manual tasks in data entry, reconciliation, and report generation. Meanwhile, while capable of automating tasks, BPM platforms also encompass other aspects of process improvement. Standard BPM functionalities model processes, define rules, monitor performance, and enable process orchestration and integration.

Integration and Process Complexity

RPA is known for its ease of implementation, as it can automate tasks without significant changes to existing systems or infrastructure, which makes it a versatile choice for companies that need to optimize processes that span multiple systems. In contrast, BPM focuses on end-to-end process optimization and often requires integration with the systems, databases, and APIs it interacts with to streamline workflows and ensure seamless data exchange.

Process Analysis and Continuous Improvement

BPM places a strong emphasis on process analysis, modeling, and optimization. It employs techniques and tools like process mapping, value stream analysis, real-time monitoring, and business rules management to identify inefficiencies, bottlenecks, and opportunities for improvement. While RPA can provide insights into a company’s processes, it aims to automate specific tasks instead of analyzing and optimizing broader process flows.

Final Thoughts

Every business, regardless of size or industry, has a series of tasks, events, and decisions that move work from start to completion through business processes. Business Process Management (BPM) solutions provide a holistic approach to the optimization and automation of those processes, while Robotic Process Automation (RPA) takes a more granular approach, dealing with discreet, repetitive tasks. They work alongside each other and can even co-exist in the same technology platform, but they are built for different purposes and, as such, cannot entirely replace the other.

NOW READ: The Top Training Courses for Business Process Managers

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As part of Solutions Review’s Premium Content Series—a collection of contributed articles written by industry experts in enterprise software categories—Assaf Melochna, the President & Co-Founder of Aquant, explains how companies can use industry-specific AI models to improve the accuracy of their AI efforts.

Artificial Intelligence has permeated various industries over the last decade. But powerful open foundational models, like ChatGPT, have recently increased the adoption of the technology, encouraging more business leaders and CIOs to invest and revolutionize how businesses operate. However, as AI systems gain popularity, there is a growing concern about AI hallucinations, where AI models generate inaccurate or misleading outputs. To mitigate these challenges, enterprises and vendors combine generative AI technology with industry-specific AI programs. 

Over the next few years, AI is expected to become a commodity with two distinct categories: horizontal and vertical. The former, horizontal, will be more ubiquitous and generalized in its applications (like ChatGPT or Bard AI). And the latter, vertical, will be more specialized and tailored to specific industries (like Service Co-Pilot or 6sense). Eventually, all organizations will need to use some form of AI to stay competitive. 

These vertical, purpose-built AI models are a bit more sophisticated and equipped to learn, adapt, and leverage a wide range of data sources, including insights from subject matter experts. When leveraged alongside generative AI models, they can help businesses generate content and automate tasks quickly and accurately. We’ll explore the pivotal role played by industry-specific AI models in mitigating the risks of AI hallucinations, focusing on their learning capabilities, adaptability, data integration, and utilization of expert knowledge.

Learning Over Time

Industry-specific AI models possess the capacity to learn continuously, refining their understanding and performance over time. Unlike traditional models with fixed rules and predefined workflows, these AI programs are designed to evolve through iterative learning processes. By ingesting vast amounts of data, they become adept at identifying patterns, correlations, and contextual nuances specific to their respective industries. This continuous learning enables AI systems to improve accuracy and make more informed predictions.  

Adaptability and Real-World Feedback

One key advantage of industry-specific AI models is their ability to adapt and adjust based on real-world feedback and company-specific information. These models are not limited to rigid algorithms or predefined rules but can incorporate new data and insights into their decision-making processes. By embracing feedback from users, customers, and employees, these AI systems can identify areas of improvement and refine their algorithms accordingly. This adaptability ensures that the models remain relevant, up-to-date, and aligned with the dynamic needs of the industry.

Integration of Multiple Data Sources

Hallucinations occur because of the amount and quality of the data leveraged in LLM models. To combat hallucinations effectively, the AI model must consume the appropriate data and information specific to the use case or problem you’re trying to solve. Industry-specific AI models use generative AI to pull information from the correct structured and unstructured data sources to gain a comprehensive understanding of the company’s intricacies.

These AI programs obtain a holistic view of the industry landscape by assimilating data from critical, nonpublic sources such as customer feedback, employee notes, sales records, market trends, and research publications. This integration of data sources helps minimize biases, enhance prediction accuracy, and reduce the risk of hallucinations caused by the abundance of data.

Leveraging Subject Matter Expertise

Recognizing the critical role of human expertise, industry-specific AI models actively seek to convert the knowledge of subject matter experts into actionable data. Aquant’s internal research reveals that incorporating human expertise is indispensable, as 30 percent of solutions to service problems are derived from expert-provided data rather than historical service records.

By tapping into the insights and experience of domain experts, AI models achieve more personalized and reliable results. This collaboration between AI and human expertise helps improve accuracy by grounding the models in real-world understanding, addressing context-specific challenges, and ensuring the AI’s outputs align with industry standards and best practices.

Industry-specific AI programs have emerged as powerful tools to optimize how workforces interact with AI technology and counteract the occurrence of AI hallucinations. ChatGPT and other generative models elevate domain-specific AI by generating easily understood and actionable outputs. Its ability to learn over time, adapt based on real-world feedback, integrate multiple data sources, and leverage subject matter expertise contributes to producing more accurate and reliable outputs. These AI models offer more comprehensive and nuanced results by mining structured and unstructured data and incorporating human insights. As businesses continue to harness AI’s potential, industry-specific AI programs are crucial in ensuring the trustworthy and effective deployment of AI technologies across various sectors.

The post Harnessing Industry-Specific AI Models to Improve AI Accuracy appeared first on Best BPM Tools, Vendors, Software and BPMS.

The editors at Solutions Review have compiled this list to spotlight some of the best AI certification programs that business professionals and managers should consider enrolling in. Solutions Review participates in affiliate programs. We may make a small commission from products purchased through this resource. 

As Artificial Intelligence (AI) technology continues to become a juggernaut presence in nearly every industry, there’s never been a better or more critical time to outfit yourself with an up-to-date understanding of the technology, how it works, why it works, and what it can do for your business. However, finding the best AI certification programs can be a challenge, especially considering how many there are to consider.

With this in mind, we’ve compiled this list of the best AI certification programs and training courses that business professionals and managers can take to improve their expertise, broaden their skills, stay on top of the newest emerging trends and tools in the AI landscape, and advance their careers. 

The Best AI Certification Programs for Business Managers

Course Title: AI for Business Leaders

OUR TAKE: This Executive Program will help business professionals master the foundations of AI technology to better and more strategically implement the technology into their company.

Platform: Udacity

Description: Business leaders who enroll in this in-depth training program will develop and fine-tune the foundational technical knowledge of machine learning (ML) and artificial intelligence (AI) they need to apply those concepts to companies across industries, drive corporate growth, maximize business efficiency, and enhance experiences. Alongside the program’s in-depth training materials and practical case studies, students will also be given a capstone project, where they will put their skills to the test and build an ML/AI-backed strategy that can be integrated into a business.

Course Title: Artificial Intelligence Nanodegree

OUR TAKE: Expand your knowledge of fundamental AI algorithms and concepts by enrolling in this comprehensive Nanodegree Program taught by industry experts Peter Norvig, Sebastian Thrun, and Thad Starner.

Platform: Udacity

Description: This three-month certification program will introduce active and aspiring AI professionals to foundational concepts like optimization algorithms, pattern recognition, Bayes Networks, Hidden Markov Models, automated planning, and more. The course is taught by Peter Norvig, a Director of Research at Google; Thad Starner, the Contextual Computing Group (CCG) director at Georgia Tech; and Sebastian Thrun, the Founder of Udacity itself. Students can expect to complete the Nanodegree in approximately three months if they commit 12-15 hours to the program weekly.

Course Title: Become an AI Product Manager

OUR TAKE: Learning to evaluate the business value of an AI product can be daunting, but this proven Nanodegree program can help you quickly equip yourself with the skills you need to get started.

Platform: Udacity

Description: Offered in collaboration with Appen, a machine intelligence company, this Nanodegree Program will teach students how to evaluate the business of AI products, build their familiarity with fundamental AI concepts, improve their ability to scope and develop a data set, and more. Some of the subjects and skills covered in the course include creating data sets, developing a business case for AI applications, building models with Google AutoML+, and evaluating the results of a model. The course ends with a capstone project where students develop a business proposal for an AI solution.

Course Title: Become a Computer Vision Expert

OUR TAKE: With this program, you’ll learn to master various computer vision skills, write programs that analyze images, implement feature extraction, recognize objects using deep learning models, and more.

Platform: Udacity

Description: Professionals with experience using Python, statistics, machine learning, and deep learning can enroll in this course to kickstart their computer vision skills and expertise. Topics covered include object tracking, localization, SLAM, image processing essentials, neural networks, machine learning frameworks, statistical modeling, applying deep learning architectures to computer vision tasks, and building automatic image captioning applications. Business professionals new to the world of “deep learning” can also use Udacity’s Deep Learning Nanodegree Program to expedite their skills development.

Course Title: Columbia Engineering Online Artificial Intelligence (AI) Program

OUR TAKE: If you’re looking for a comprehensive, rigorous training program, this eighteen-month program from Columbia University is for you.

Platform: edX

Description: Learn how to develop AI-powered systems, solutions, and services by enrolling in this in-depth executive education program. The non-credit, non-degree course focuses on providing learners with the insightful instruction, discussion, and peer collaboration they need to bolster their skills in a lasting, meaningful way. During the eighteen-month program, students will learn all about machine learning, algorithms, neural networks, deep learning, natural language processing (NLP), computer vision, robotics, and more. Alongside the program’s various assessments, exercises, quizzes, and projects, the course also includes a three-day “immersion” on campus in New York City involving presentations, working sessions, and networking events.

Course Title: Professional Certificate in Fundamentals of Google AI for Web-Based Machine Learning

OUR TAKE: Learn what artificial intelligence technology can do by enrolling in this course from some of Google’s AI experts.

Platform: edX

Description: Students that enroll in this self-paced certification course from Google will improve their familiarity with subjects and tools like machine learning, applied AI, the TensorFlow.js library AI programming, Linear Regression, Convolutional Neural Networks, object detection, natural language processing, and more. These insights will also help students learn how to write custom models from a black canvas, use industry-standard models for various services, convert Python models to the TensorFlow.js format, and see firsthand how ML, AI, and deep learning fit together.

Course Title: Professional Certificate in Foundations of AI

OUR TAKE: Deepen your understanding of AI, its applications, use cases, and its effect on our lives by enrolling in this certification course from the experts at IBM.

Platform: edX

Description: This self-paced certification course is taught by Rav Ahuja, the Global Program Director of the IBM Skills Network, and Antonio Cangiano, a software developer and technical evangelist at IBM. During the course’s three-to-four-month program, students will learn what AI can do, how to get started with the technology by using the IBM Watson solution, and use Watson to build, analyze, deploy, and monetize chatbots. There are three courses included in the program, which all include hands-on interactions with various AI environments and applications.

Course Title: Professional Certificate in Applied AI

OUR TAKE: This course acts as a more in-depth alternative to the “Foundations” program above, as it includes those three courses alongside three additional courses that go deeper into the world of AI technology.

Platform: edX

Description: For professionals looking for a proper deep-dive into AI technology, its use cases, and some of the tools available in the marketplace, this self-paced, three-to-six-month program fits the bill. Students will learn about the applications of tools like IBM’s Watson AI, Python, and OpenCV. Some of the skills students will put into practice include creating customer support chatbots, deploying speech-enabled virtual assistance to Facebook, creating interactive vision web applications, deploying those applications to the cloud, and continuing to explore concepts around machine learning, deep learning, neural networks, and more.

Course Title: Professional Certificate in Computer Science for Artificial Intelligence

OUR TAKE: Offered through Harvard University, this professional certificate will help equip you with a broad, thorough understanding of computer science, programming, and artificial intelligence applications.

Platform: edX

Description: Business professionals and aspiring software developers that complete this course from HarvardX will leave with a meaningful understanding of machine learning libraries, artificial intelligence principles, and the concepts that act as the foundation of modern artificial intelligence. The course uses a mixture of live lectures and lessons with hands-on projects to educate learners on the theories behind graph search algorithms, optimization, classification, reinforcement learning, standard programming languages like Python, and more.

Course Title: CertNexus Certified Artificial Intelligence Practitioner Professional Certificate

OUR TAKE: With this five-course series, you can earn a career credential that demonstrates your AI expertise to prospective employers, increasing your chances of landing the job you want.

Platform: Coursera

Description: This program is geared toward intermediate-level professionals looking to master strategies for implementing AI, solving business problems, and demonstrating those skills to employers. Students will learn about the issues AI/ML can solve, how to identify the right technology for each issue, use ML algorithms, build multiple models to solve problems in a workflow, and explore some of the advanced algorithms used in machine learning and deep learning use cases. The course uses a flexible schedule that allows students to take the courses at their pace.

NOW READ: The Top Training Courses for Business Process Managers

The post 10 of the Best AI Certification Programs for Business Managers appeared first on Best BPM Tools, Vendors, Software and BPMS.

The editors at Solutions Review have curated this list of the most noteworthy WorkTech news from the week of June 23rd. This round-up covers announcements and updates from companies like OutSystems, IFS, Unanet, and more.

Keeping tabs on the most relevant ERP and BPM news can be time-consuming. As a result, our editorial team aims to summarize the week’s top headlines in the enterprise technology marketplace. The Solutions Review editors will compile a weekly round-up of vendor product news, mergers and acquisitions, venture capital funding, talent acquisition, and other noteworthy WorkTech information. With that in mind, here is some of the top WorkTech news for June 23rd.

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Top WorkTech News From the Week of June 23rd, 2023

Fortis Acquires SmartPay, an Embedded Payment Solution Provider

Fortis, a commerce technology provider, has acquired SmartPay, an embedded payment solution provider with solid connections in the enterprise software and ERP ecosystems. The acquisition will provide Fortis with a team of integrated payments professionals and help the company strengthen its ability to serve scaling businesses in complex businesses. Mike Sheffey and Nate Schloss, the SmartPay founders and former managing partners, will join Fortis’ leadership team as part of the acquisition.

Read on for more.

IFS Expands its FSM Capabilities with Acquisition of Poka

IFS has announced its acquisition of Poka, a Quebec-based provider of a connected worker platform. Poka provides factory and field workers in the manufacturing industry with a hub of operational knowledge to help workers improve how they learn, share, and collaborate on the production floor. The acquisition will allow IFS to extend its capabilities, digitally connect workers across the end-to-end value chain in its core industries, and equip its factory and field worker users with the resources they need to work smarter and safer while boosting productivity and efficiency.

Read on for more.

Mendix Details the New AI Capabilities Coming in Mendix 10

Mendix, a modern enterprise application development solution, has announced details on the new AI and machine learning capabilities coming to its platform when Mendix 10 releases later this month. The new functionalities will expand the scope of Mendix’s AI-enabled application development, allow enterprises to seamlessly integrate AI use cases with low-code applications, streamline the deployment of AI-enhanced low-code applications, and more. These features will be available when Mendix 10 becomes available on June 27th.

Read on for more.

Cognizant Extends its Relationship with Orkla

Cognizant has announced that it has extended its relationship with Orkla, an industrial investment company. The two companies have partnered on several digital transformation projects across markets since 2013. As part of their extended partnership, Cognizant will help Orkla IT move toward a “future operating model” that represents a modern, agile way to deliver IT services to clients, enabling greater capacity and flexibility in managing projects and resources. The enhancements will also allow the companies to innovate on their SAP and infrastructure services.

Read on for more.

OutSystems Announces Its Roadmap for Generative AI Capabilities

OutSystems, a global application development solutions provider, has announced several new AI features, including the general availability of a new connector for Azure OpenAI and information on its roadmap for a suite of generative AI Capabilities. The Azure OpenAI connector, built jointly with Microsoft, will make it easier for users to build AI-powered applications with low-code. Meanwhile, OutSystems’ generative AI roadmap includes an AI-powered app editor that offers ongoing suggestions, instant app generation using conversational prompts, an extensive ecosystem of connectors, and real-time, full-stack visual representations of app changes.

Read on for more.

Unanet Introduces New Accounts Receivable Automation Functionalities

Unanet—a project-based ERP and CRM provider for architecture and engineering (AE) industries—has announced several new automated accounts receivable (AR) functionalities to help AE businesses streamline, accelerate, and error-proof manual processes. The new features are available on Unanet’s AR Automation platform, formerly Unanet Pay, and will help companies to reduce time-consuming processes (i.e., printing and mailing invoices), get deeper visibility into collections, accept digital payments, and improve the overall accuracy of collections activities, resulting in increased cash flow, faster invoice-to-cash cycles, and more.

Read on for more.

Expert Insights Section

Watch this space each week as the Solutions Review editors will use it to share new articles from the Expert Insights Series, Contributed Shorts videos, Expert Roundtable videos, event replays, and other curated content to help you gain a forward-thinking analysis and remain on-trend with changing industry expectations. All to meet the demand for what its editors do best: bring industry experts together to publish the web’s leading insights for enterprise technology practitioners.

Five Reasons to Embrace a Networked Supply Chain, by Jason Tham of Nulogy

As part of Solutions Review’s Premium Content Series, Jason Tham, the co-founder and CEO of Nulogy, outlines five reasons companies should embrace a networked supply chain. In the article, Tham says that networked supply chains can provide increased data visibility, more flexibility in responding to market trends, better resource utilization, stronger resilience to disruptions, and enhanced innovation.

Read on for more.

The Time is Now: Smart Manufacturers Make Moves to Invest in Cloud Technologies, by Stu Johnson of Rootstock Software

As part of Solutions Review’s Premium Content Series, Stu Johnson, the Vice President of Product Marketing at Rootstock Software, explains why smart manufacturers should invest in cloud technologies. In the article, Johnson says that cloud technologies can help manufacturers create comprehensive signal chains, improve access to valuable data, meet customer demand, and more.

Read on for more.

How Process Mining in Healthcare Can Improve the Employee and Patient Experience, by Grace Nam of Laserfiche

As part of Solutions Review’s Premium Content Series, Grace Nam, the Strategic Solutions Manager at Laserfiche, explains how process mining in healthcare can help organizations improve employee and patient experiences by extracting valuable insights from data, streamlining processes, optimizing data handling, and improving overall outcomes.

Read on for more.

How MSPs Can Responsibly Leverage AI Technology, by Ray Meiring of QorusDocs

As part of Solutions Review’s Premium Content Series, Ray Meiring, the CEO and co-founder of QorusDocs, explains how Managed Service Providers (MSPs) can responsibly implement and use AI technologies in their processes. In the article, Meiring talks about what AI can offer to MSPs and identifies some of the challenges involved with AI adoption.

Read on for more.

For consideration in future WorkTech news round-ups, send your announcements to wjepma@solutionsreview.com.

The post Top WorkTech News From the Week of June 23rd: Updates from OutSystems, IFS, Unanet, and More appeared first on Best BPM Tools, Vendors, Software and BPMS.

As part of Solutions Review’s Premium Content Series—a collection of contributed articles written by industry experts in enterprise software categories—Grace Nam, the Strategic Solutions Manager at Laserfiche, explains how process mining in healthcare can help organizations improve employee and patient experiences.

Even before the COVID pandemic, healthcare workers were feeling overwhelmed and overworked. In studies conducted before 2020, 38-54 percent of physicians, 36 percent of residents and medical students, and up to 41 percent of nurses reported burnout symptoms. Though some obvious causes—like challenging work/life balance, high-pressure workload, and low staffing levels—are at the root of emotional and physical fatigue, another burnout catalyst often goes undiscussed: information overload.  

Healthcare systems generate enormous volumes of data, from lab results to treatment plans to payment-related paperwork, daily. Performing data-related administrative tasks—especially within aging IT systems—prevents healthcare providers from actually providing healthcare. More than 6 in 10 physicians say they “have little to no time to address their patients’ social determinants of health,” a fact that many feel contributes to burnout.  

One surprising key to giving practitioners more time with patients is through a technique called process mining. Evaluating systems and processes is essential for healthcare organizations looking to transform their operations digitally—but many don’t know where to start.  

Process Mining Unearths Helpful Insights in Clinical Settings 

Every workflow process has an information trail, but that trail is often left unattended. The Institute of Electrical and Electronics Engineers (IEEE) task force on process mining describes how “process mining techniques…extract knowledge from event logs commonly available in today’s information systems.” Mining software analyzes event data and provides a visual working model of that process, so organizations can “discover, monitor, and improve processes in a variety of application domains.” 

In a healthcare setting, the three types of process mining—process discovery, conformance checking, and enhancement—can relieve practitioners of burdensome administrative tasks. During an ER visit, a health information system records an event log for each admission. This contains information such as a case ID, activity, timestamp, whether the interaction has been completed, and who the attending professional is. However, the event log relies on manual input and is subject to human error.  

Process discovery uses algorithms to build a model based on the event log. Often, the model will focus on the order of activities, how those activities relate to one another, and the resources needed for each activity. In this case, the model will help teams visualize the flow of ER admission, triage, testing, treatment, and release or readmission, highlighting activity patterns humans may not have seen.  

Conformance checking compares the information presented in a model with the information in the actual event log. This version of process mining evaluates whether the model accurately describes the activities in question and uncovers deviations between observed and modeled behavior. The conformance-checking phase might show providers, for example, that antibiotic administration to a sepsis patient violated medical guidelines for timely administration in some instances.  

The third type of process mining is enhancement. This iteration of process mining uses actual event data to improve or extend an existing model. Enhancement can apply triage-form data to a model of sepsis-related ER admissions to show where process bottlenecks or data re-entry incidents exist. 

Click and Notification Overload is the Impetus Behind Process Mining Adoption 

As the sepsis example demonstrates, there are myriad drivers of process optimization in healthcare settings, from variable data quality to protocol deviation. Ninety percent of the physicians who wanted more time to address patients’ social determinants of health say “removing low-value work, including EHR clicks and minimizing inbox notifications” would be the most helpful action to improving health and wellbeing.  

Nearly three-quarters of physicians in a different survey are “very or extremely interested in technology that cuts down the time of administrative work,” which includes visit summaries, charting, and data entry. Process mining in healthcare can sift through data patterns from these activity logs to indicate where automation may effectively cut down redundant work. 

Process Mining Solutions Streamline Arduous Processes and Data Handling 

A story from one healthcare provider—the Translational Pulmonary & Immunology Research Center (TPIRC)—exemplifies how healthcare organizations can implement solutions that interest physicians. For example, TPIRC manually managed data for patient lab results and developed treatment plans for food-based allergies. By identifying solutions with process mining, TPIRC realized it could automate lab-results communications and treatment plan creation and even predict the level of allergens that will cause a reaction.  

Now, when patients show up, TPIRC clinicians only need to enter a patient’s identifying number. The electronic records system then displays all the required information for a given visit while enabling the TPIRC team to add information to its growing data set and improve treatment effectiveness.

By automating patient communications, TPIRC reduced the time practitioners spent poring over charts and spreadsheets. At the same time, it increased the number of patients it could see by 400 percent while maintaining the same number of staff and high levels of employee satisfaction. 

Process Mining Can Improve Healthcare Outcomes 

In healthcare, employee burnout—and the often-lagging pace of innovation—is not merely an inconvenience: it’s detrimental to care delivery and saving lives. Every industry has plenty of operational inefficiencies, but optimizing processes to eliminate those inefficiencies is critical to improving outcomes for practitioners and patients. While it is difficult to know where to begin, given the sheer volume of data and systems providers have, process mining in healthcare is the key to identifying those discrepancies and gaps which are diminishing care. Process mining enables organizations to go behind the scenes to gather valuable insights providers can use to inform decision-making, upgrade processes, and spend more time with patients.

The post How Process Mining in Healthcare Can Improve the Employee and Patient Experience appeared first on Best BPM Tools, Vendors, Software and BPMS.