Rajan Nagina, Head of AI Practice at Newgen Software, explains why AI in banking is actively reshaping the finance industry. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

There was once a time when Artificial intelligence (AI) was regarded as a vague, futuristic concept. And yet, we have reached a point where it is completely embedded into multiple industries and is revolutionizing how they operate. The banking industry, which had initially been slow to join the bandwagon, has now started deploying new-age technologies like AI in several of its operations, which has opened a new chapter in its future. AI supports the industry in boosting efficiency, reducing risks, and delivering hyper-personalized experiences, from customer service to fraud detection. Banks that fail to adopt these technologies risk falling behind in an increasingly competitive landscape.

By allowing smarter decision-making, automation of repetitive tasks, enhanced security with coded guardrails, and unlocking new revenue streams, AI is inevitably revolutionizing how financial services operate. AI-powered “digital cognitive workers” are reshaping lending processes by reducing approval times from weeks to minutes. The next five years will see AI become the backbone of banking, drastically changing how financial institutions handle risk and interact with consumers.

This article explores how AI is paving the way for a faster, smarter, and more efficient future for banking and the ethical challenges that come with it.

The Rise of Smart Customer Experiences  

 As customer expectations rise, banks can no longer rely on the one-size-fits-all approach. Today, AI enables banks to analyze enormous volumes of customer data, from spending habits and income fluctuations to life events, to offer tailored financial advice.

1. Chatbots & Virtual Assistants  

AI-powered chatbots manage routine inquiries, minimizing wait times and enhancing customer experience. Banks like JPMorgan Chase and HSBC are now utilizing virtual assistants to address account queries, process transactions, and offer investment advice without human intervention.

2. Predictive Banking  

Machine learning can anticipate customer needs and make suggestions before a big purchase or issue an alert regarding potential overdrafts.  For instance, several banks have started analyzing transaction histories to predict when a customer may require a mortgage or credit line adjustment. Boston Consulting Group even reports that when finance companies incorporate AI-driven planning and forecasting, they can increase overall productivity by 20-30 percent.

3. Voice & Facial Recognition  

Biometric authentication expedites the speed of logins and also improves security. Some excellent examples of how AI makes banking seamless and secure are HSBC’s voice recognition system and Citibank’s facial ID verification.

Fraud Detection & Risk Management – AI as the Ultimate Guardian 

Financial fraud costs the world economy billions of dollars annually, but artificial intelligence is here to change that. AI can identify anomalies in real-time, while traditional rule-based systems find it challenging to keep up with shifting threats.

 1. Analytics of Behavior  

AI monitors transaction patterns and flags anomalous activity, such as abrupt, large-sized withdrawals or international transactions. For instance, Mastercard’s AI-powered system can instantly detect fraud by analyzing spending patterns across millions of transactions.

 2. Evaluation of Credit Risk  

To forecast loan defaults more precisely, machine learning models examine non-traditional data, such as social media and utility payments. Fintech companies like Upstart and ZestFinance implement AI to evaluate creditworthiness in ways other than traditional FICO scores, thereby enhancing financial inclusion.

 3. Anti-Money Laundering (AML)

AI can save up to 30 percent on compliance expenses by lowering false positives in AML alerts. For instance, Deutsche Bank uses AI to sort through millions of transactions and detect suspicious activity more accurately than manual reviews.

Operational Efficiency – Doing More with Less  

AI is steadily changing the face of the banking industry by minimizing human error, cutting expenses, and simplifying banking operations.

1. Automated Document Processing

AI reduces processing times from days to minutes by extracting important data from contracts, invoices, and loan applications. In certain cases, AI agents are also automating loan underwriting, which reduces the human workload by more than 70 percent.

2. Adherence to Regulations  

AI monitors changing regulations, ensuring that banks stay compliant without human supervision. For instance, AI can assist organizations in avoiding expensive penalties by scanning through legal documents and identifying inconsistencies.

 3. Employee Productivity

By automating routine tasks like data entry and customer verification, AI helps employees concentrate on more complex and high-value work. According to a McKinsey report, AI could save banks up to $1 trillion by 2030 through operational efficiencies.

Challenges & Ethical Considerations 

Despite its many advantages, banking leaders must consider AI’s drawbacks to utilize it to the best of their capacity.

1. Privacy Issues with Data  

Banks must balance using personalization to appeal to customers and safeguarding their data. They must ensure that AI models don’t misuse sensitive data to comply with stricter laws and regulations, such as the CCPA and GDPR.

 2. Bias in Algorithms  

AI may reinforce discrimination in lending if it is trained on biased data. For instance, an AI model that favors particular groups might unjustly refuse loans to eligible candidates.

 3. Excessive Reliance on Automation  

Human oversight continues to be essential in critical areas to ensure banks avoid any possible errors. The dangers of unrestrained automation are demonstrated by the 2020 ZestFinance case, in which it was discovered that an AI lending model discriminated against minority borrowers.

Regulators are taking action, and US guidelines and the EU’s AI Act influence how banks use these technologies responsibly and ethically.

 AI as the Foundation of Banking in the Future  

The banking industry is at a crucial turning point. AI is steadily becoming the foundation of financial services, rather than just being an add-on.

1. Hyper-personalized Banking  

AI helps banks provide context-aware, real-time financial advice, such as modifying savings plans in response to market fluctuations or life events.

2. Independent Financial Consultants  

Robo-advisors will develop into completely self-sufficient systems that require very little human intervention to manage portfolios.

 3. Integration of Blockchain and AI  

AI-powered fraud detection and smart contracts will speed up transactions and make them more secure. Banks that adopt AI will lead in innovation, efficiency, and customer satisfaction, while those that don’t run the risk of becoming obsolete.

Conclusion  

The banking industry is already experiencing an AI revolution. AI is redefining finance in the blink of an eye, from enhancing fraud detection to automated lending and regulatory compliance. At the same time, to realize AI’s full potential, banks must overcome moral and legal obstacles.

The AI revolution is underway, and only the financial institutions that strike when the iron is hot and successfully incorporate AI into their operations stand the best chance to win in this race against time.

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To help companies remain competitive amidst changing markets, the Solutions Review editors are exploring how an Empathetic AI (EAI) mindset can improve AI adoption, optimize automation initiatives, and future-proof their operations without displacing employees.

Artificial intelligence (AI) has been a fundamental part of enterprise technology for years; it’s helped power manufacturing plants, analyze complex data sets, track customer sentiments, and much more. What’s changed in the last couple of years is the widespread awareness of AI-powered technologies and how closely integrated they are into modern business processes. For example, when it comes to business automation, companies worldwide and across industries are looking to save money and time by providing workers with systems that lessen workloads and, ultimately, enable them to use their professional skills in more valuable ways.

However, it’s not uncommon for traditional automation approaches to prioritize efficiency metrics while ignoring human-centered outcomes, leading to failed implementations, employee resistance, and customer alienation. The issue can be exacerbated by the rapid adoption of AI technology, especially when an organization is not adopting it with an empathy-first mindset. Without that mindset, companies risk creating a systematic blind spot that prevents their “AI transformation” initiatives from achieving the necessary success.

Those failures aren’t technical, though; they’re empathy failures. That’s where the principle of “Empathetic AI,” or EAI, as we’re calling it, comes into play. Empathetic AI doesn’t mean making robots more human-like. Instead, it’s a strategic framework that designs automated systems with explicit consideration for their emotional, psychological, and social impacts on the human workforce working with them. This approach transforms automation from a replacement paradigm into a human augmentation strategy, creating sustainable competitive advantages through stronger stakeholder relationships and higher implementation success rates.

With that perspective in mind, the Solutions Review editors are exploring how an EAI-forward approach to business automation can transform company processes, improve employee productivity, boost morale, and maximize the value AI technologies can provide.

The Three Pillars of EAI Implementation

Implementing EAI into your company’s AI adoption efforts can seem abstract, but it doesn’t have to be. Think of it as another layer in your change management strategy, and initiate a program that creates comprehensive “empathy maps” that document emotional touchpoints, anxiety triggers, and relationship dependencies within existing processes. That info will be crucial for the actual EAI implementation effort, which can be categorized into the three pillars outlined below.

1) Assessing Stakeholder Impact

The first step in implementing empathetic AI is to evaluate how automation can and will affect various stakeholder groups, including employees, customers, and business partners. This means documenting not only what those people do, but also how they feel about doing it. Have users built any informal relationships around current workflows? Are there any sources of professional identity or customer connection that could be disrupted with the introduction of AI-powered automations? Answering those questions before rolling out an AI strategy can transform how easily workers adopt and adapt to the new processes and tools.

For example, imagine a healthcare organization implementing an AI patient scheduling system to reduce call volume and optimize the scheduling process for users and patients. While the ROI on such an initiative would seem obvious, an empathetic assessment might reveal that scheduling staff positively impacts the quality of care regular patients report receiving. With that information, the organization can redesign its operations to free staff from routine scheduling without disrupting the relationship-based care that patients have come to expect.

Employees want this kind of thinking, with a 2025 McKinsey report showing that nearly half of surveyed workers “want more formal training,” “would like access to AI tools in the form of betas or pilots,” and “indicate that incentives such as financial rewards and recognition can improve uptake.” Workers are already using AI—maybe more than executives even realize—and the best way to equip them for success is to provide the resources and scaffolding they need to augment, not replace, their existing workflows.

2) Adopting Gradual Integration Protocols

It takes time for a workforce to adjust to new tools, even if they are relatively easy to use (like generative AI). The next pillar of implementing an EAI strategy is to allow and encourage employees to adapt to the new systems gradually. Failing to do so can trigger defensive responses from employees, making eventual adoption more difficult. According to Vitaliy Tymoshenko, founder and CEO of SmartExpert.ai, “employees and managers often resist the implementation of AI because they perceive automation as complex or unreliable.”

Gradual integration requires a sophisticated, agile technical architecture capable of supporting multiple operational modes simultaneously. This includes confidence thresholds that automatically trigger human involvement, real-time adjustment capabilities based on user feedback, and cultural adaptation algorithms that modify system behavior based on organizational preferences. While this approach can extend the duration of an implementation, the benefits will be longer-lasting. Like Eddy Azad, CEO at Parsec Automation, explained in Forbes, “Small, consistent steps forward enable organizations to integrate AI into their operations seamlessly, mitigating risks, enhancing long-term resilience, and getting planned-for outcomes.”

3) Deploying a Feedback Loop Architecture

The next step in implementing EAI is establishing built-in mechanisms for continuous human input and system adjustment. Unlike traditional feedback collection, an empathetic feedback loop supports a co-creation relationship where affected stakeholders actively participate in the ongoing automation refinement process, instead of only the initial design or post-implementation evaluation.

One of the best ways to include stakeholders is by integrating sentiment analysis and emotional state recognition to help teams adjust system behavior in real-time. For example, companies can involve teams most affected by AI in ongoing “automation labs” where the end-users propose or test system modifications and participate in customer advisory plans to ensure the technology rollout is best situated for success. This collaborative approach treats automation as an evolving capability rather than a fixed implementation and plays a foundational role in promoting transparency throughout the development of an AI policy or system.

However, you still need to measure the results of this feedback. Instead of relying on traditional KPIs, decision-makers should incorporate additional metrics—or even identify new ones—that capture empathetic outcomes alongside operational efficiency. These metrics should include stakeholder comfort indices, adoption velocity measurements, and relationship preservation scores that track whether AI enhances or degrades human connections within business processes.

Making Empathy a Priority

The question isn’t whether your business should adopt AI—it’s whether you’ll implement it in a way that strengthens or weakens your human relationships. By adopting an empathetic AI policy, companies will create sustainable competitive advantages through higher implementation success rates, stronger customer relationships, and more engaged workforces.

Want more insights like this? Register for Insight Jam, Solutions Review’s enterprise tech community, which enables human conversation on AI. You can gain access for free here!

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Brian DeWyer, the CTO and Co-Founder of Reveille Software, outlines the most significant benefits of prioritizing real-time visibility in intelligent automation systems. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

They all have them—cars, planes, and boats. What do they all have? Dashboards! You don’t drive a car without knowing your speed and fuel amount; airplane pilots need to know their altitude, and boat captains want to see the depth of the water. The result can be catastrophic without dashboards that render real-time views of vehicle operation status. Excessive operating temperatures, threshold breaches, and critical maintenance lapses can all cause significant consequences. 

The need for real-time visibility into your content systems is similar; you need visibility into the operational status before applications break down. With the introduction of Intelligent Automation—composed of Intelligent Document Processing (IDP), Enterprise Content Management (ECM), and Robotic Process Automation (RPA)—and collaboration systems into business operations, real-time visibility into your enterprise’s systems has become crucial for smooth and efficient operation. In this article, we’ll look at real-time visibility for Intelligent Automation, how it works, and how it benefits your organization. 

Real-Time Visibility 

Real-time visibility allows you to see and comprehend events as they are happening within your systems and processes. This ability to gain instant insights will enable managers and IT teams to react when a change or challenge arises. With Intelligent Automation solutions, real-time visibility gives you complete knowledge of content access, user behavior, and system performance, which are measured and displayed in dashboard form. For example, Intelligent Automation will alarm before end-users are impacted if a server’s storage is nearing capacity. 

Without real-time visibility, numerous challenges can arise. Knowing what these challenges are is critical for keeping operational efficiency and ensuring prompt response to potentially crippling issues. Additional challenges are: 

• Delays in System Failure Detection: Slow or nonexistent detection of system failures can exacerbate problems and often lead to prolonged system downtime.
• Unauthorized Access: Without immediate alerts, unauthorized access can go unnoticed, posing a risk to data security and integrity.
• Bottlenecks in Workflow Processes: Lack of real-time data can prevent the timely identification and resolution of workflow bottlenecks.
• Compliance Risks: Delayed detection of non-compliance activities can lead to serious legal and financial consequences. 

Without real-time visibility, a business’s ability to manage, repair, and optimize its content is severely limited. Without it, operational inefficiencies, increased costs, and the risk of data exposure could all be “real-time” problems. Intelligent Automation systems manage large volumes of content across various departments and functions. Real-time visibility into these systems is vital, ensuring that any glitch in operations can be caught and immediately addressed before the damage is too costly or destructive to functioning. 

Benefits

Real-time visibility has two significant benefits: It minimizes downtime and reduces operating costs. It also bolsters security, improves records management, and helps ensure compliance by allowing swift responses to threats or anomalies. 

Intelligent Automation systems’ observability provides more than a broad overview of what’s happening; it also targets additional critical areas that need visibility to ensure metrics are being gathered, as well as: 

• System Performance Metrics: Your ability to monitor transaction performance and user response times can significantly improve response efforts. Real-time alerts on spikes in error rates can prompt immediate investigation, helping to maintain system health and reliability.
• Access and Security Controls: Tracking who accessed what and when gives insight into unusual access patterns that could indicate internal threats or breaches. Immediate notifications about changes in document permissions ensure that sensitive information is guarded against unauthorized access.
• Workflow Efficiency: Real-time monitoring helps identify and address bottlenecks quickly, reducing downtime and improving efficiency. Monitoring task completion also helps identify inefficiencies and reveal training needs.
• Compliance and Audit Preparedness: A real-time display of detailed audit trails helps businesses prepare for compliance audits and internal reviews without it being the laborious process that it’s known to be. Continuous monitoring also ensures that all content management activities comply with policies and regulatory requirements.
• Integration and Interoperability: Real-time visibility is essential for monitoring the performance of integrated systems and maintaining the ecosystem’s integrity. Tracking the frequency and response times of the application programming interface (API) provides insights into the system’s scalability and external dependencies. 

Intelligent Automation Observability 

The constant tracking of Intelligent Automation system performance and user activity with real-time observability and monitoring software allows for actionable insights that optimize processes. Here’s how real-time visibility improves enterprise content management: 

Customized Dashboard

The real-time dashboards that provide valuable visibility are customizable. A clear view of your performance metrics gives you a real-time understanding of Intelligent Automation systems. Customizable dashboards allow managers instant access to critical data, allowing them to monitor trends and gauge their system’s health. The immediate awareness and swift actions that address potential issues as they arise are indispensable business resources. These dashboards also improve productivity while ensuring optimal system performance and user satisfaction. 

Alert Systems

Real-time monitoring software instantly and automatically alerts to potential issues. Because the monitoring and tracking are based on defined or ML forecasted thresholds, issues are flagged before they harm the system. These alerts assist IT teams in responding swiftly to anomalies, minimizing downtime, and preventing minor problems from becoming major obstacles. Operational efficiency is improved while maintaining system integrity, security, and reliability. 

Performance Metrics

Monitoring system health and user activity can identify performance trends and anomalies. This visibility in real-time operations results in proactive management of the entire Intelligent Automation software environment, optimizing performance, addressing inefficiencies, reducing risk, and improving customer service. 

Conclusion  

As Intelligent Automation platforms, solutions, and systems evolve, the need for real-time visibility will become increasingly evident for maintaining operations. Observability with customizable dashboards will be integral to managing these newly sophisticated systems efficiently and securely. By integrating Intelligent Automation monitoring and management solutions, organizations will be ready to face any possible risks, eliminate potential harm, and enhance operational agility, which will benefit all users. 

A real-time dashboard that monitors all Intelligent Automation functions provides instant visibility into system behavior as it unfolds. Its proactive insights empower administrators with the data needed to ensure business integrity, just as a dashboard helps keep a car running, a plane flying, and a boat floating. The net result of full observability is greater efficiency, productivity, and profitability.

The post The Benefits of Real-Time Visibility In Intelligent Automation Systems appeared first on Best BPM Tools, Vendors, Software and BPMS.

Brian DeWyer, the CTO and Co-Founder of Reveille Software, summarizes how intelligent document processing (IDP) is changing business workflows across departments. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

The digital era has significantly evolved our ability to create and store content. Today, businesses are choking on terabits of information, which is the new gold. But just like traditional gold, it must be mined and carefully monitored to ensure adequate storage, instant access, and layered security. The need for intense monitoring has morphed into a physical process of Intelligent Data Processing (IDP).  

Emails, social media posts, Word documents, images, videos, audio files, and surveillance footage are several examples of content flooding storage devices. Although its value is immeasurable, most data remains unstructured. Although the volume of unstructured data is overwhelming, IDP helps organizations with data extraction, classification, and analysis.  

IDP turns structured and unstructured content chaos into accurate processes by using artificial intelligence (AI) and machine learning to automatically extract, categorize, and organize all that data without straining the limits of human capabilities.   

IDP Origins 

The roots of IDP go back to the early 1900s when Optical Character Recognition (OCR) was first developed. OCR began by translating text into sound for people with visual impairments. It then evolved to translate written characters into telegraph code, which evolved into digital credit card and barcode systems. The first volume capable of OCR systems appeared in the 1960s and 1970s, designed for specific applications like mail sorting based on zip codes or reading handwritten numbers. Then, OCR was used to digitize historical archives such as newspapers; when it entered the cloud, it became a tool accessible from desktops and mobile devices.  

Now, artificial intelligence, machine learning, natural language processing, and sophisticated computer vision have transformed OCR into IDP and enabled it to take over the tasks associated with document classification and data capture functions entirely. Modern IDP systems can transform unstructured data into usable formats with speed and accuracy while learning from each interaction to improve processing accuracy. In retail banking, for example, IDP streamlines the handling of customer-related documents like account opening forms and identity verifications. With IDP, banks can automatically extract data from these documents, regardless of format or quality; this speeds up decision-making and reduces errors associated with manual data entry. 

The IDP Process 

But how does IDP do it? Here’s how the process unfolds: 

1. IDP recognizes and categorizes documents, such as invoices, emails, or forms. It then facilitates using tailored rules and extraction methods for more precise and efficient data processing.  
2. Once documents are classified, the IDP extracts the needed information such as dates, names, and amounts. For example, it can extract the due date, vendor name, and total amount due from an invoice. 
3. After extraction, IDP validates data, enhancing accuracy. This process reduces manual checks, ensuring data reliability and seamless integration into existing workflows. 
4. IDP systems facilitate continuous learning through user interactions and feedback. User analytics is crucial for analyzing usage patterns and benchmarks. It helps organizations refine data processing strategies and align the system with changing business requirements. 
5. IDP systems track processing time, error rates, and throughput volumes. The insights gained help identify bottlenecks, improve workflows, and enable organizations to make data-driven decisions.  
6. Deploy focused observability and monitoring of IDP applications, processes, and systems for comprehensive IDP management. 

Behind IDP’s Technology 

There are several core technologies behind IDP. There is no ‘one size fits all’ as multiple technologies exist to support different unstructured data processing requirements. 

1. Optical Character Recognition (OCR) converts images of text from scanned documents and photos into machine-encoded text. 
2. Machine Learning (ML) enhances IDP systems by enabling them to learn from data inputs and corrections. ML algorithms can identify patterns and anomalies.  
3. Natural Language Processing (NLP) allows IDP systems to interpret the data and the semantics of information.  
4. Deep learning, a specialized ML technique, uses layers of algorithms called artificial neural networks to further enhance document processing capabilities. 
5. Generative AI can generate summaries and derive actionable insights from unstructured data, further enhancing IDP’s capabilities. 
6. Computer Vision helps recognize and interpret visual elements within documents, such as logos, stamps, and handwritten notes. 
7. Robotic Process Automation (RPA) automates repetitive tasks, such as data entry, sending notifications, and updating records, without human intervention.  

The IDP Benefits 

The following are the benefits associated with IDP: 

• IDP streamlines operations and enhances data management across sectors. Automating the extraction and processing of data streamlines workflows and cuts down on manual labor. IDP improves productivity and optimizes resource allocation across the organization.
• IDP enables rapid data processing and quick accessibility, providing faster access to data and helping organizations make swift decisions. Companies can maintain a competitive edge by staying responsive to market changes and customer needs.
• Reducing the reliance on manual document handling serves two benefits: it decreases labor costs and minimizes the occurrence of errors. Both help prevent financial losses associated with data mishandling and reduce the workload of correcting such mistakes.
• IDP can seamlessly integrate with existing systems, which allows for the automation of complex processes at an enterprise scale. This automation ensures data consistency and enhances quality across various operations, boosting reliability and compliance within the organization. 
• IDP’s efficient data processing allows quick responses to customer queries and ensures accurate request handling. This process improves customer interactions and satisfaction, which fosters loyalty and increases a business’s reputation.  
• IDP systems can adapt to increased workloads without requiring additional resources. Such scalability supports business growth, enabling companies to expand their operations while managing the larger data sets that come with scaling. 

IDP Penetration: From Banking to HR 

The insurance industry is revolutionizing claims processing by automating the extraction and analysis of data from claims forms and related documents. This has reduced processing times, boosted accuracy, expedited claim resolutions, and elevated customer satisfaction. IDP’s ability to detect patterns and anomalies has also helped insurers strengthen their fraud detection efforts. 

In banking and financial institutions, IDP accelerates customer onboarding processes. By automatically extracting data from ID proofs, application forms, and other documents, IDP speeds up the verification process, reduces errors, and improves compliance with regulatory mandates. This results in a more efficient onboarding experience that enhances customer satisfaction. 

IDP streamlines the management of shipping documents, invoices, and freight bills within logistics. It automates data capture from various forms and documents, enabling quicker invoice processing and reducing discrepancies. This efficiency improves supply chain visibility, enhances tracking accuracy, and reduces bottlenecks, resulting in more reliable and faster deliveries. 

HR departments can automate the processing of employee documents, such as legal documents, resumes, onboarding paperwork, and all the forms that come with benefit packages. This process speeds up hiring, improves records management, and ensures compliance with regulations. It also allows HR professionals more time to focus on tasks like talent management and employee engagement rather than toiling away at data entry work. 

Conclusion 

The future of IDP is auspicious and expected to become more sophisticated. Fortune Business Insights states, “The global Intelligent Document Processing market size was valued at USD 5.89 billion in 2023. The market is projected to grow from USD 7.89 billion in 2024 to USD 66.68 billion by 2032, exhibiting a CAGR of 30.6 percent during the forecast period.” 

Artificial intelligence will continue to evolve, impacting IDP’s ability to process complex documents and provide deeper insights into extraction, classification, ingestion, and validation. Because digital transformation is a process, not a goal, IDP will always be essential for automating and streamlining document processes. Five-year calculators, such as the Intelligent Data Processing Community’s calculator, can determine the return on investment (ROI) from using IDP.  

All industries, from banking to HR, need the right tools to process information accurately with as little human intervention as possible. Intelligent Document Processing (IDP) virtually eliminates the paralysis by analysis syndrome, which has hampered the operational efficiencies of document-driven processes with human errors for decades. IDP’s future as a valued business tool shines as brightly as a gold nugget in a clear-water stream. Organizations that embrace these nuggets will prosper faster than those that retain outdated data procedures and workflows. 

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Mike Jennett, Global Director and Head of CloudBlue Platform Strategy, explains why B2C companies are adopting self-service platforms and eliminating the middleman in their subscription models. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

Today’s tech-savvy consumers expect frictionless, on-demand shopping and appreciate self-service portals. These platforms empower users with the tools to help themselves throughout the buying experience, putting control in the hands of the customer. Research has found that people prefer dealing with tech over personal interactions.

According to a survey by PlayUSA, 84 percent of Americans like self-service, and 60 percent use kiosks or mobile apps to avoid talking to people. As the subscription model economy evolves, more B2C businesses will shift toward self-service account management. Why? Because it cuts down on costs, reduces friction, and gives customers the flexibility they want. When done right, self-service is a win-win for everyone.

AI-Powered Convenience

Artificial intelligence (AI) is key in enhancing self-service platforms by providing personalized recommendations and predictive customer support. AI-powered chatbots assist users in navigating options, answering common queries, and troubleshooting problems without human intervention. Companies like Netflix and Amazon are leading examples of how self-service can enhance customer satisfaction while reducing operational burdens.

For instance, Amazon leverages self-service for everything from account management to automated returns. Netflix uses AI to recommend content based on viewing habits, helping users discover shows and movies without manual searching. But while AI brings a ton of benefits, many businesses struggle to use it effectively. Simply adding AI tools isn’t enough–you have to be strategic. Here are three things to keep in mind:

• Maintaining Personalization: Striking the right balance between automation and human interaction is crucial. Businesses should ensure that AI-driven solutions complement human expertise rather than replace it, preserving authenticity and meaningful customer engagement.
• Ensuring Customization: A one-size-fits-all approach often falls short, as different industries and organizations have distinct requirements. Tailored solutions are needed.
• Improving AI Usability: Intuitive and user-friendly interfaces and accessibility are needed; if your self-service platform isn’t easy to use, people won’t use it.

AI works best when it enhances customer interactions instead of replacing them.

Why a Unified Platform is Essential

The shift from traditional rule-based processes to AI-driven, dynamic workflows makes automation significantly more effective. When given the right data and guidelines, AI agents can take smarter actions and provide better recommendations. But here’s the catch: AI can’t do its job properly if your systems aren’t connected. A unified platform helps systems work in sync, enhancing the self-service experience:

• Beyond Chatbots: AI agents do more than provide conversational interfaces; they actively complete tasks, automate resolutions, and drive efficiency.
• Personalized Bundling: Machine learning tailors recommendations based on user behavior, enhancing customer experience and increasing conversions.
• Data-Driven Insights: Advanced analytics empower businesses to refine sales strategies, optimize performance, and make informed decisions.

A unified platform helps businesses utilize AI tools in a practical yet impactful way.

Capitalizing on Unified Commerce

Self-service solutions broaden access to software procurement, giving small businesses more options. Independent Software Vendors (ISVs) are increasingly embracing direct-to-consumer approaches, using automation to move from traditional distribution models.

With a unified platform, ISVs have more control over their sales channels and can easily publish on hyperscaler marketplaces for broader distribution. Automating processes eliminates manual tasks such as paperwork, phone calls, and reliance on intermediaries, leading to greater efficiency and cost savings. Businesses can negotiate directly with ISVs to secure custom pricing and private offers, ensuring more flexible and competitive pricing options.

Automated provisioning further streamlines operations by enabling instant access to software, reducing delays, and administrative overhead. A unified platform simplifies sales management across multiple channels, allowing businesses to efficiently oversee and optimize their sales strategies. Centralized reporting and real-time analytics offer valuable insights to help companies track performance and identify trends.

Focus on Customer Satisfaction 

Self-service account management portals are a game-changer for B2C companies in the subscription economy. With these self-service tools, customers can tweak their subscription models to match their changing needs. Whether it’s upgrading, downgrading, or canceling a plan, they have the flexibility to adjust their services anytime.

Updating payment methods is a breeze, too, reducing potential hiccups due to outdated billing info. Users can also customize their preferences, like opting in or out of notifications and promotional emails, for a more personalized experience. And it’s not just customers who benefit. ISVs can use these same tools to manage customer interactions more efficiently. The more seamless the experience, the more engaged customers will be. Businesses that invest in self-service solutions are better prepared to meet the expectations of today’s consumers.

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The editors at Solutions Review have summarized some of the most significant ways AI has impacted software development, from hiring developers to sought-after skillsets, best practices, and more.

One of the least surprising things someone can say in 2025 is that artificial intelligence (AI) has impacted the software development space. What is less clear is the specific impact AI has had on that market and whether developers have cause for concern. As AI is integrated into software development processes at unprecedented levels, the form and function of a company’s dev team will inevitably continue changing and evolving.

To keep track of those changes, the Solutions Review editors have outlined some of the primary ways AI has changed software development, what professionals can do to remain agile during those changes, and what the future may hold for them and the technologies they use.

Note: These insights were informed through web research using advanced scraping techniques and generative AI tools. Solutions Review editors use a unique multi-prompt approach to extract targeted knowledge and optimize content for relevance and utility.

How Has AI Changed Software Development?

In just a few years, AI’s role in software development has dramatically restructured the kinds of roles, responsibilities, and required skill sets companies look for. This transformation has been freeing for many, as AI has streamlined their workloads and empowered them to focus on more specialized, high-value tasks and projects. However, it’s not uncommon for developers to feel uneasy about the rapid adoption of these technologies, as they have already proven capable of rendering some tasks and roles nearly obsolete. Here are some of the development processes that have been impacted the most by AI:

Code Generation and Assistance

Arguably, AI has had the most visible impact on DevOps in code generation. Thanks to large language models (LLMs) like GitHub Copilot, Amazon CodeWhisperer, and more general models like Claude, developers can now generate functional code from natural language descriptions in a fraction of the time. A 2024 report confirms this trend by showing that 80 percent of global developers use AI when writing code. This capability has created a force multiplier effect that enables developers to delegate routine coding tasks to AI assistants, freeing them to focus their skills on higher-level architecture and business logic.

This has reduced implementation time for common patterns but is not without trade-offs. Developers have started reporting a weakened understanding of underlying implementations, leading to what some call “implementation amnesia”—where they become dependent on AI suggestions rather than building mental models of the systems they create. That’s why traditional developers will remain essential, as their experience and thinking, when paired with AI, can improve productivity and efficiency.

Testing and Quality Assurance

Testing has also been revolutionized by AI-powered tools and bots, which can automatically generate test cases, detect edge cases, and even self-heal tests that break due to UI changes. Tools like Mabl, Testim, and Applitools use machine learning to maintain test suites with minimal human intervention.

Perhaps most significantly, AI-powered visual testing tools can detect subtle UI regressions across thousands of screen permutations in minutes rather than days, making previously impossible testing scenarios routine. The most sophisticated implementations combine reinforcement learning techniques to continuously explore application states, identifying critical bugs in production-like environments before deployment.

The benefits can be substantial, as these tools can increase test coverage and decrease maintenance costs. However, this can result in a trend where developers are overly reliant on automated testing that doesn’t use the proper verification frameworks, leading to false confidence in system stability. The most effective teams use AI to augment their human testing expertise rather than replace the contextual understanding and intuition that experienced QA professionals bring to complex systems.

Architecture and Design

Despite what you might think, AI is increasing the premium on good architecture and design rather than diminishing it. As implementation becomes easier, the relative importance of system design, interface definitions, and architectural boundaries has grown. Leading organizations now spend more time on design activities and less on implementation than a few years ago. This represents a healthy evolution, though there’s concern that AI tools aren’t yet sophisticated enough to validate architectural decisions, potentially leading to technically functional but poorly structured systems.

Inbal Shani, the chief product officer (CPO) and head of R&D at Twilio, says in an article by McKinsey, “AI can help analyze data sets and be an unbiased element in the conversation. After making strategic decisions, AI can help continuously monitor metrics and evaluate the progress.” As McKinsey’s article explains, this can accelerate development cycles by increasing the odds that resources are allocated to the most promising ideas, ultimately reducing costs and lowering the chance of failure.

The Emergence of AI-Centric Development Roles

The impact of AI on software development is significant, but the most dramatic effect on the industry is the influx of new, AI-specific roles that these technologies have necessitated. These specialized roles range from generative AI prompt engineers to workflow architects, AI code auditors, and technical debt analysts. LinkedIn’s Skills on the Rise report says as much, reporting that AI literacy is the fastest-growing skill that “professionals are prioritizing and companies are increasingly hiring for.”

One way these technologies can change DevOps teams is by converging several roles into one. For example, if AI can autonomously perform tasks that once required a human, the responsibilities of each team member are destined to change. Varun Parmar, general manager at Adobe and former CPO and COO of Miro, said to McKinsey, “The PMM and PM role will most likely converge under the same product organization.” He explains that, as AI automates more PMM-centric tasks, the “PMM function will need to go really deep into positioning and become fully integrated within the product team.”

Upskilling for the Future

Sulabh Soral, the Chief AI Officer at Deloitte, puts it this way: “Hybrid collaboration between human expertise and AI efficiency will pave the way for not only creating functional and reliable software but also groundbreaking, transformative solutions that push the boundaries of what is possible in the digital world.”

The AI impact on software development is an evolving, fluid thing. As clear as its repercussions have already proven, the continued growth and integration of new AI technologies will result in the impact being an ongoing, ever-changing reality for professionals to reckon with. However, as dramatic as the effects might be, the expertise DevOps professionals bring to the industry will never go out of style. They might need to pivot their skillsets to adapt to new or emerging needs, but the creativity of a human mind will remain essential.

Want more insights like this? Register for Insight Jam, Solutions Review’s enterprise tech community, which enables human conversation on AI. You can gain access for free here!

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Nathan Stearns, the Vice President of Workforce Engagement Strategy at NICE, explains the role that workforce management (WFM) tools play in an increasingly bot-infused world. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

Customer self-service capabilities have been reducing workload in contact centers for decades. IVRs have been automating self-service since the 1970s. Improvements in search engines have given customers direct access to knowledge base systems to conduct their own troubleshooting and problem-solving. These and other tools have been running alongside the contact center workforce for years, creating a much-needed reprieve in headcount requirements.

More recently, chatbots and generative AI solutions have gained prominence, with growth accelerated by the COVID-19 pandemic. The pandemic years sped up the adoption of digital channels and increased the use of bots in contact centers. In fact,  according to a Gartner Press Release: “By 2027, 40 percent of all customer service issues will be fully resolved by unofficial third-party tools powered by GenAI, according to Gartner, Inc.” Eight in 10 consumers are more willing to do business with companies that offer self-service options, a survey by Simpler Media Group found, and 74 percent of chat interactions result in a first-contact resolution—higher than agent-assisted transactions.

Agents in contact centers are seeing the positives of AI in customer service, too. Chatbots are estimated to handle around 30 percent of the tasks currently completed by contact center staff. This frees up their day to focus on more complex interactions and boosts efficiency. According to Salesforce, 64 percent of agents with chatbots can spend most of their time solving complex problems, compared to 50 percent of agents without them.

The increase in chatbot usage—and the benefits contact centers realize as a result—have raised some key questions related to the role of workforce management (WFM) in a bot-infused world. Here’s what you need to keep in mind.

WFM Forecasting

Robust WFM systems specifically focus on workforce management—just the human element. That means most WFM tools capture the interaction history and workload of agents, excluding the interactions bots, IVR, or other self-service channels handled (if you’re one of the 58 percent of centers with their chatbot integrated into their scheduling/WFM tools). Forecasting data scrubs the self-service interactions to suggest an ideal headcount and schedule based solely on human needs.

If self-service success rates increase, then the historic workload of agents automatically decreases, and vice versa. No user intervention is required to make this happen, meaning you can rely on your WFM tools to give you appropriate scheduling forecasts no matter how effective your self-service channels currently are.

WFM Scheduling 

Because of how forecasting algorithms work, bot effectiveness has no direct bearing on how you schedule employees. Bot activity indirectly impacts employee schedules through the natural ebb and flow of customers using self-service. Those gradual changes will be apparent in the agent-based schedule history collected for forecasting and scheduling purposes.

Just as you wouldn’t look to your WFM system to plan employee schedules around IVR success rates, there’s no need for a WFM system to plan employee schedules around bot success rates. Instead, keep your attention on what matters at the point of data collection: the management of the workforce.

WFM Real-Time Adjustments

The ability of WFM forecast algorithms to self-adjust came about with the proliferation of IVRs in the 1980s. The algorithms are now designed to automatically scrub the queue history and remove outliers that unexpected changes in bot/IVR/self-service success rates may cause. They pick up on shifts in what the bots/IVR/self-service are (or are not) doing.

Ideally, WFM systems should generate a forecast per interval, not per day. This significantly improves the contact center’s ability to respond quickly to unique changes in bot/IVR/self-service success rates that may be time-of-day sensitive. Automated intraday re-forecast algorithms automatically adjust the employee forecast in near real time based on the self-service success of that particular time period.

Bots—and their ability to understand complex customer service scenarios—are improving at an unprecedented rate, and the market is nowhere near saturated. The good news is that WFM solutions on the market today are already set up to accommodate this increase in usage. There’s no need to schedule bots in WFM, which would result in the contact center having to license additional seats for the bots in WFM—an added, unnecessary expense. Rest assured, your WFM has it handled, no matter the impact your self-service channels have.

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Lindsay Sterrett, the Vice President of Product Marketing for Content Services at OpenText, explains how artificial intelligence (AI) is changing enterprise information systems and content management. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

Enterprise content management has undergone significant transformation in recent years. What began as simple document storage has evolved into sophisticated systems that can analyze, categorize, and extract value from information across organizations. With the integration of artificial intelligence, we’re witnessing perhaps the most important evolution in how businesses manage their information assets.

The Current State of AI Adoption in Content Management 

Research from Foundry, surveying IT decision-makers at major U.S. enterprises, reveals that 70 percent of organizations are already implementing or significantly investing in AI technology for content management. This isn’t surprising, considering the volume and complexity of information that today’s enterprises must manage effectively.

The rapid adoption reflects a growing recognition that traditional content management approaches are insufficient for modern business needs. As information volumes grow exponentially, organizations require more intelligent systems to extract value from their content assets.

Breaking Down the Benefits of AI Content Management

The integration of AI into content management systems (AI content management) offers several distinct advantages:

1. Enhanced Productivity: 42 percent of surveyed organizations cite increased productivity as a primary benefit. AI-powered systems can automate tasks such as document classification, metadata tagging, and information retrieval, freeing knowledge workers to focus on higher-value activities.
2. Data-Driven Decision Making: 40 percent of respondents highlighted improved decision-making capabilities. By analyzing patterns across large document repositories, AI can surface insights that might otherwise remain hidden, enabling leaders to make more informed strategic choices.
3. Content Performance Optimization: Another 40 percent of organizations value AI’s ability to optimize content performance. This includes identifying the most frequently accessed content, its use, and where information gaps exist.
4. Security and Compliance: 37 percent of enterprises appreciate AI’s contribution to content security and compliance. Advanced algorithms can identify sensitive information, flag potential compliance issues, and help enforce governance policies at scale.
5. Cross-Repository Insights: 30 percent of organizations benefit from AI’s ability to provide insights across multiple repositories. This is particularly valuable for enterprises with fragmented information landscapes from mergers, acquisitions, or organic growth.

AI Content Assistants represent one of the most promising applications in this space. These tools provide knowledge workers a simple way to interact with enterprise information, quickly finding, understanding, and utilizing content from multiple repositories and formats. By leveraging Retrieval Augmented Generation (RAG), AI content assistants deliver secure, accurate responses with linked citations to relevant documents, ensuring users gain contextualized insights exactly when needed.

The Challenge of Digital Friction

One of the most compelling reasons for AI adoption is addressing what experts call “digital friction”—the unnecessary effort employees expend to locate, access, and use information needed to perform their jobs. This friction represents a significant productivity drain across organizations.

AI-powered content management directly addresses this challenge by:

• Providing natural language search capabilities
• Anticipating user information needs based on role and context
• Automatically summarizing lengthy documents
• Connecting related information across disparate systems

Future Directions for AI Content Management

Looking ahead, organizations have clear expectations for how AI will transform content management over the next three years:

• 59 percent anticipate business process automation as the most relevant use case
• 39 percent expect more intuitive, personalized user experiences
• 39 percent look forward to more effective content discovery capabilities

Implementation Considerations

For organizations considering AI-powered content management solutions, three factors emerge as critically important:

1. Integration capabilities: The ability to work seamlessly with existing enterprise systems
2. Flexible model support: Adaptability to specific business needs and use cases
3. Multimodal AI capabilities: Support for diverse content types beyond just text

Conclusion

Integrating AI into content management represents a fundamental shift in how organizations view and leverage their information assets. Rather than seeing content as a static resource to be stored and retrieved, AI enables enterprises to treat information as a dynamic, continuously valuable asset that actively contributes to business success.

As AI technologies mature, we can expect even more sophisticated applications that further reduce digital friction, enhance knowledge sharing, and unlock new forms of business value from enterprise content. Organizations that successfully implement these technologies will likely gain significant competitive advantages through improved productivity, better decision-making, and more effective use of their information resources.

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Jason Beem, the CEO of Panopto, explains how seamless integrations are the key to maximizing workplace productivity. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

In the last decade, a digital transformation has drastically changed the modern workplace with a myriad of tools and platforms designed to meet the needs of businesses across all industries. The rise of cloud computing, artificial intelligence (AI), and automation has given businesses endless solutions to address all different aspects of operations. While these tools have increased efficiency, streamlined processes, and moved data-driven decisions to the forefront, they’ve also introduced their own unique challenges, creating fragmented workflows, app overload, and barriers to productivity.

Employees are now expected to juggle multiple platforms, each with its own interface, learning curve, and access restrictions. This growing complexity doesn’t just slow down work; it actively diminishes focus, collaboration, and overall effectiveness. Without seamless integration, even the most powerful technologies fail to reach their full potential, leaving employees frustrated and organizations struggling to maximize their investments.

The Pitfalls of Fragmented Workflows

While specialized tools serve critical business functions, they often operate in silos, forcing employees to switch between platforms to complete tasks. On average, organizations use 112 software applications, leading to app fatigue, cognitive overload, and inefficiencies that slow down workflows. The time spent switching between applications breaks employee focus and ultimately reduces the quality of their work. The more tools required, the greater the learning curve—further compounding the problem.

This challenge also extends beyond traditional software. Many organizations rely on recorded knowledge, such as training materials and onboarding resources, to help employees stay informed. When these resources exist in disparate systems with limited searchability, they often go underutilized or even completely neglected. Employees waste valuable time searching for critical information, and the lack of accessibility undermines productivity and learning outcomes.

Streamlining Workflows with Seamless Integrations

The idea of “in-the-flow-of-work” engagement is not a new concept, yet it is very rarely used. Development and training should be integrated into everyday tasks, allowing employees to access relevant information and resources when and where they need them rather than disrupting their workflow.

The concept is simple: instead of forcing employees to adapt to new systems, technology should adapt to them. Seamlessly embedded solutions minimize disruptions, allowing employees to complete tasks within a unified environment—without switching between platforms.

The full benefits of in-the-flow-of-work solutions include:

• Eliminating productivity roadblocks: 71 percent of organizations report that employees spend more time than necessary searching for information to complete any given task. Integrated tools reduce app-switching, allowing employees to access training resources, meeting recordings, and operational data within the platforms they already use. The result? Fewer disruptions and greater efficiency.

• Driving adoption of new technologies: Employee resistance is one of the biggest barriers to digital transformation. Integrating new tools into existing systems alleviates the learning curve, making transitions smoother and boosting adoption rates. Employees are far more likely to engage with resources when they are embedded into platforms they already use every day. By eliminating the friction, businesses can drive higher engagement and ensure technology is fully leveraged across teams.

• Enhancing accessibility with AI: AI-powered integrations take accessibility to the next level by providing real-time recommendations and intelligent search capabilities, reducing the time spent searching across multiple systems. AI-driven search tools can automatically index and transcribe video content, allowing employees to instantly locate key moments within recorded meetings without manually reviewing hours of footage. Additionally, AI-powered automation can surface key insights from past interactions, offering employees instant access to relevant knowledge when making decisions.

• Boosting collaboration and knowledge retention: When critical information is easily accessible, employees can collaborate more effectively. Instead of knowledge being siloed within teams or lost due to turnover, integrations ensure that expertise is captured and shared across the organization. Video-based learning, AI-driven documentation, and centralized content repositories all play a role in maintaining institutional knowledge and reducing redundancies.

Harnessing the Full Potential of the Modern Workplace 

The rapid expansion of digital tools has brought undeniable benefits and improvements in business operations—but it has also led to fragmented workflows and data silos. New solutions are around every corner, and to capitalize on the full potential of digitizing the workforce, businesses need tools that foster collaboration, minimize fragmentation, and ultimately improve productivity.

The future of workplace productivity is not about adding more tools; it’s about making existing knowledge more accessible. Whether it’s AI-powered search surfacing key insights from a recorded discussion or seamlessly embedding video-based learning directly into existing platforms, businesses must ensure that their digital solutions work for employees—not against them.

The bottom line? In-the-flow-of-work or seamlessly embedded integrations are essential for optimizing productivity in the modern workplace. By reducing the complexity of managing multiple specialized tools, these solutions empower employees to maintain focus, collaborate more effectively, and drive innovation. When technology is thoughtfully integrated, it doesn’t just support workplace productivity; it transforms it.

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Anand Narasimhan, the Chief Technology Officer at S-Docs, explains why static documents are bad for your company’s data and how automation can help. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

Data is table stakes for an organization, and how it’s managed is equally, if not more, important. This process should be dynamic, timely, and consistent. If you wouldn’t use those words to describe how your organization manages, shares, and stores your data, it’s a big sign that you need to revisit the process. This is where static documents can hurt, and automation can help.

Static documents are critical to business operations and are a familiar, easy-to-use format for managing data. Instead of learning a new data management system, people can more easily create a document, fill it out with the correct data, and store it. Just because static documents are easy and familiar doesn’t mean they are reliable or dependable.

Static Documents Lead to Fragmented Data, Which Leads to Risk.

There’s a right time and place for static documents. When used incorrectly, they can unknowingly expose your organization to avoidable risks.

Compliance and security breaches

Let’s use a business contract as an example. Contracts contain sensitive Personally Identifiable Information (PII), such as email addresses, social security numbers, names, and more, all of which must be protected.

For the sake of our example, say someone generates a contract, attaches it to an email, and sends it to a client. There are a few significant risks in doing this:

1. Email breaches: If you lose access to your email or someone gains access, they can see this sensitive data.

2. No encryption: Housing data in documents sent via email without encryption, redaction, or security measures can open the door for security breaches and compliance issues.

3. Local machine downloads: If team members download documents with sensitive data onto their local machine, the machine could be stolen or infected with a virus.

4. Human error: It is also possible to accidentally send a contract to the wrong person or give someone’s data to the wrong person.

5. Compliance issues: Static documents don’t allow data to be updated and handled by team members consistently and securely. This creates problems in complying with industry regulations and getting the latest data from original data sources.

Compliance issues and security breaches can be costly in terms of fines and fees — not to mention the reputational damage that can take years to overcome.

Frustration and friction

Word, Google Docs, spreadsheets, and PDFs are static in nature (which has benefits and drawbacks). As soon as you create these documents, they (along with the data you’re collecting with them) are outside of your system. This leads to data silos, adding more fuel to the fire.

Here’s an example of how this can be an issue for your team: When you’re actively negotiating a contract, you need the contract to stay up to speed with your system — and if this is done manually, it requires a lot of time and effort. If, for instance, a contract’s start and end date change, you will need to go back and update every document to ensure that it reflects the change, or make a new one and risk your team using the wrong version.

Document versions also can be an unnecessary hurdle. While it’s ideal that everyone will work off the same document, this isn’t always the case. People often create their own version of a document to avoid confusion, which can cause a big issue when it’s time to make a consolidated, accurate contract version and send it to the signer.

Moreover, no one wants to sift through spreadsheets, paperwork, contracts, and other documents to realize that the one they found had the wrong, outdated information and have to start from square one.

Automation’s Role in Risk Mitigation

Static documents and data are somewhat at odds with each other, whereas data and automation systems are more secure and synergistic—they work for you and are harder to use against you. Automation makes it easy to stay compliant and reduce risk.

• Improved data accuracy: Going back to the contract example, contracts are usually treated as static documents. However, they have many dynamic data points that allow them to be used as living, breathing documents. This has many benefits, including streamlined operations, better customer service, and consistent data management processes across teams.

• Version history and tracking: A good document automation tool will give you a centralized place for multiple people to work on a document and allow for version tracking, making it much easier to get a contract to the finish line.

• Higher security: Some document automation tools utilize cloud storage and can encrypt, redact, and mask sensitive data, which can help you rest assured in terms of security and compliance. This data can be shown to those authorized to access it, and it will be hidden from others.

• Tying your documents to your CRM: Updating data directly in your CRM and having that data dynamically pulled into an updated document saves significant time, makes it easy, and ensures everyone has the latest information. Some document automation solutions also have write-back capabilities, meaning that data entered into a field is automatically mapped back to your CRM.

Document automation platforms work wonders in terms of saving time and ensuring accuracy, organization, and security. They ensure your data is dynamic, timely, and consistent. Static documents are where data goes to die.

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