By Edwin Jongsma, VP of AI and Integrations at Financial Finesse. Financial Finesse won the ‘Best Use of AI-driven Personalization‘ category, and was a finalist in the ‘Best Use of AI for Learning’ category at The 2024 A.I. Awards.

Roughly 90% of all fintech companies are utilizing some level of AI in their operations and business models.

While most of these use cases pertain to streamlining day-to-day tasks and boosting productivity amongst employees, companies are increasingly harnessing the power of AI for the benefit of their clients, customers, and users.

Although this is an important step for the future of the fintech industry, it is essential that companies deploy AI that is safe and responsible for users, rather than integrating this technology solely for the sake of being able to say they use AI. The best way to guarantee that this technology is created safely is for the technology to be built on a closed loop model and for the human to remain in the loop.

No Room for Error

Most generative AI models are built on outside data. They learn and function by analyzing large external sources like the internet, public databases, etc. Although this can be advantageous in some scenarios, such as providing real-time updates and information for users, it can also result in an AI tool that is prone to biases and hallucinations, which can become outright dangerous, especially regarding financial guidance. Outdated and untrustworthy information might also result in inaccurate results generated by AI.

The finance industry has little room for error when it comes to the accuracy of tools and systems that users rely on for financial guidance. Missteps can result in users making decisions that harm their financial well-being and erode the trust they have in the companies deploying these tools. To safeguard against these risks, fintech companies must make it a priority to protect the financial futures of millions of users who depend on their solutions for secure and reliable coaching.

Keeping Financial Experts in the Loop

An important step in creating safe and responsible AI is to ensure the tool is grounded in the company’s own documentation and datasets. In the fintech industry, every company has a wealth of information and data at its fingertips. With an AI tool meant to impart financial guidance or suggestions, companies have the opportunity to integrate their proprietary data into the technology, ensuring that the information users receive from the tool stems from actual resources that a human financial planner or coach from the company would provide.

It is critical to build AI as a Chain of Thought system that builds in evaluations by AI and keeps human financial experts in the loop – ensuring there is a way to authenticate responses and teach the AI. By building a quality assurance program into the technology, financial coaches or planners can validate responses from the AI, helping it learn and adapt over time. Typically, this process requires heavy training on the human side upfront, but as time goes on, the tool continues to learn the difference between right and wrong responses, requiring less human interference and, ultimately, resulting in an AI tool that companies can feel confident in deploying to their clients and users.

Let’s take a look at this model in practice. Financial Finesse’s Ask Aimee Anything – an interactive financial coaching AI search engine – was built utilizing the AI Transformer technology also used by GPT but designed to overcome common AI limitations by training the resource on a closed universe of thousands of original articles and other content pieces created and maintained by our on-staff team of CFP® professional financial coaches only – not the wider internet. Additionally, financial coaches are able to interact with the quality assurance program built into the tool, monitoring the responses output by the AI and correcting any mistakes that come up. This model prevents biased or inaccurate responses, ensuring the information users receive is up to date and trustworthy.

Striking the Balance

The next challenge we face as an industry lies in finding a balance between keeping the human in the loop and letting the technology work independently. Ultimately, as the technology evolves, the goal is to reduce the time humans have to be involved but corners should never be cut.

As fintech companies move forward with integrating AI, they must recognize that responsible implementation is not just a technical challenge but an ethical imperative. The future of the industry will depend on how well companies integrate these safeguards into their technology, ensuring that innovation does not come at the expense of the user. By prioritizing accuracy and safety, the fintech industry has the opportunity to lead in demonstrating how AI can improve lives while upholding the highest standards of integrity.

By Neil Patel, Senior Director of Product Marketing at BigID. BigID were finalists in the ‘Best Cloud Data Management Solution’ category at The 2024/25 Cloud Awards.

In today’s digital age, cloud data management is a critical tool for modern businesses, offering scalable storage and access to vital information.

But when sensitive information is stored in the cloud, the combination of certain data types can create toxic data clusters—combinations that, if exposed, significantly increase the risk of a data breach.

Consider this: every interaction we make generates data, from sensitive customer details and financial records to emails and internal communications. Individually, these data points are valuable, but when improperly grouped—such as linking employee access logs with personal identifiable information (PII) or storing unencrypted payment information alongside user credentials—they can become an attractive target for attackers and a compliance nightmare.

In today’s hyper-connected digital landscape, protecting sensitive data is no longer just a matter of safeguarding individual pieces of information. Toxic data combinations aren’t just theoretical risks but carry real-world consequences, ranging from the fine we see above to potential data breaches that, when breached, can lead to devastating financial, reputational, and regulatory consequences for businesses.

The concept of toxic data combinations marks a pivotal shift in how we approach data security. It’s not merely a buzzword; it’s an essential lens through which industries must view the intersecting risks posed by disparate sensitive data. As organizations handle increasingly large and complex data sets, understanding and mitigating the compounded risks from these toxic combinations is critical to both business resilience and long-term success.

The need to address these toxic data combinations is more urgent than ever before, especially as we head into 2025.

Risky Business – Toxic Data Combinations

As digital environments expand, the convergence of sensitive data into singular storage locations is no longer an isolated issue—it’s becoming an alarming trend. Toxic data combinations, where personal, financial, and other sensitive information are stored together, are increasingly fueling large-scale data breaches. These breaches are eroding organizational trust, damaging brand reputation, and compromising individual privacy at an unprecedented rate.

Much like in the classic movie Risky Business, where a seemingly innocuous decision spirals into a cascade of unintended and catastrophic consequences, storing different types of sensitive data together can turn even minor security oversights into massive breaches. The challenge? Identifying these toxic combinations isn’t as easy as spotting the obvious risk. Traditional classification methods often fail to detect how unrelated data points, when combined, multiply the overall risk.

To tackle the challenge of identifying toxic data combinations, organizations need to go beyond basic detection. They must find, classify, flag, tag, and label all types of sensitive data—whether it’s easily identifiable like a social security number, or more subtle, such as a customer ID or password. By tagging and labeling data wherever they live, businesses gain the visibility and control necessary to prevent toxic combinations from making them become tomorrow’s headline news.

How Toxic Data Combinations can be Exploited

In the world of cybersecurity, toxic data combinations are like a ticking time bomb waiting to be exploited by identity thieves, fraudsters, and other bad actors. By aggregating sensitive information—such as names, credit card details, and passwords—organizations unwittingly create a treasure trove for attackers. With just one breach, cybercriminals can build detailed profiles of individuals, enabling highly targeted scams, identity theft, or spear-phishing attacks. This doesn’t just erode customer trust—it can also lead to a catastrophic hit to an organization’s reputation.

Cloud data management amplifies the risks of toxic data combinations because of its vast, interconnected nature. A single misconfiguration or a lack of granular access controls can inadvertently expose these dangerous pairings to unauthorized users. Moreover, data silos that are eliminated for operational efficiency can unintentionally merge sensitive datasets, making it easier for bad actors to exploit vulnerabilities.

Handling sensitive data without a proper strategy can turn a minor security oversight into a full-blown crisis. One of the biggest threats often comes from within, whether it’s a malicious insider, careless employee, or third-party contractor. When toxic data combinations are exposed through insider threats, the fallout can include massive privacy violations and non-compliance with regulatory standards.

But the danger doesn’t end with insider threats. External attackers who gain access to these toxic data combinations can do far more damage than with isolated data points. For cybercriminals, stumbling upon a collection of interconnected sensitive data is like striking gold, where these bad actors can use that information to sell on the dark web or to infiltrate other organizations. This amplifies the impact of a breach, leading to severe financial losses, legal penalties, and regulatory scrutiny.

Without a data-centric approach, organizations are essentially playing risky business with their most sensitive assets. By focusing on data visibility, classification, and protection, businesses can proactively manage toxic combinations and prevent a manageable security incident from snowballing into a full-scale disaster that cripples operations and devastates the bottom line.

Methods for Detecting and Addressing Toxic Data Combinations

Mitigating the risks of toxic data combinations starts with being able to know and control your data. In today’s landscape of hybrid and multi-cloud environments, it’s essential to gain a comprehensive and clear view of your data. However, it’s not enough to rely on basic pattern recognition. Organizations need a deeper, context-driven understanding of how their data is interconnected. This is where advanced AI and machine learning-based classification techniques come into play, helping businesses go beyond surface-level detection to identify toxic combinations across both structured and unstructured data environments, whether on-premises or in the cloud.

Once your data is mapped, the next step is making the crucial connections. By leveraging advanced data correlation techniques, organizations can detect relationships between various sensitive data elements. This level of granularity is what separates a minor oversight from a serious security threat. Identifying these connections allows businesses to uncover toxic combinations that could otherwise remain hidden, preventing a small issue from escalating into a major crisis.

After you have mapped out your data, organization leaders need to be able to remediate it. Addressing toxic data combinations requires a proactive approach: prioritizing risks and implementing targeted strategies like encryption, deletion, or masking based on the severity of the threat. Data security demands ongoing monitoring and validation to ensure remediation efforts are effective.

In this digital era, cloud data management isn’t just about storage and accessibility; it’s about safeguarding against the unforeseen dangers that arise from how data is stored and combined. By addressing toxic data combinations proactively, businesses can better protect their sensitive information, maintain compliance, and ensure the trust of their customers.

By continually adapting and evolving your data security strategies, you can stay ahead of emerging threats and keep your most sensitive information safe. As the data landscape continues to evolve, managing toxic data combinations will no longer be just a best practice—it will become a fundamental requirement for protecting sensitive information and maintaining organizational integrity.

By the team at CADDi. CADDi were finalists in the ‘Most Innovative AI Technology‘ category at The 2024 A.I. Awards. They also won the ‘Best SaaS Product for Business Intelligence and Analytics‘ and ‘Best SaaS Product for Engineering Management, PLM or CAD‘ at The 2024 SaaS Awards.

The last few years have marked a turning point in manufacturing, as the industry has faced one challenge after another.

The COVID-19 pandemic, supply chain disruptions, and economic fluctuations have reshaped global manufacturing—and though progress has been made, true stability has not yet been reached. Fortunately,  these challenges also created opportunities for innovation, with a growing number of companies leaning on new technologies to chart a path through uncertainty. Among the top innovations is artificial intelligence (AI), which is unlocking untapped potential across manufacturing sectors.

AI-driven solutions in additive manufacturing, robotics, and intelligent software bring a new age of agility, efficiency, and adaptability to an industry founded on constant innovation. These breakthroughs allow manufacturers to meet demand more effectively, mitigate supply chain hurdles, and future-proof their operations. Here’s how these cutting-edge technologies are setting the stage for a new era in manufacturing.

1) Additive Manufacturing Powers Rapid Idea-to-Production Capabilities

Additive manufacturing, including 3D printing, has brought unprecedented flexibility to production lines. AI is making the entire process even more efficient by automating complex design steps and creating a seamless path from idea to physical product. With AI, manufacturers can transform an image or concept directly into a 3D model without needing extensive manual input. This streamlined process allows for a wider variety of products and significantly shortens the timeline from design to market-ready goods.

For instance, during COVID-19, when personal protective equipment (PPE) became scarce, several manufacturers pivoted to 3D printing to produce face shields and other essential supplies. AI-driven additive manufacturing allowed them to quickly iterate designs and scale up production without the traditional delays associated with retooling. The flexibility to make on-the-fly adjustments to product designs reduces production lead times and enables companies to offer a wider variety of customized products, tapping into market segments that demand tailored solutions.

AI’s role in 3D printing also opens up the opportunity for more customized product offerings, allowing manufacturers to create small, unique production runs without costly retooling. This capability is especially valuable in industries where customers demand highly personalized products, where companies must pivot swiftly to maintain competitiveness.

Even if additive manufacturing can’t produce a certain final product, it allows for rapid iteration in prototyping and experimentation. Rather than having to reprogram a sophisticated metalworking machine, things like size tolerances or assembling compatibility can be tested quickly by printing out parts. AI-enhanced additive manufacturing can expedite this process further by automatically iterating on design parameters.

2) Smarter, More Adapatable Robotics

AI is also transforming robotics, enhancing their flexibility, accuracy, and capacity for autonomous decision-making. Traditionally, robots were designed for repetitive tasks, each programmed with specific parameters. Now, AI-powered robotics can “see” and interpret their surroundings through machine vision, allowing them to adapt to different scenarios on the factory floor. This contextual awareness means they can handle a broader range of tasks—making each robotic unit more valuable and adaptable.

Consider a scenario on a high-volume production line where robots equipped with AI-driven vision systems can perform quality inspections. Instead of relying solely on humans to spot inconsistencies, these robots can detect subtle defects with high accuracy, enabling manufacturers to catch issues early and reduce waste. This technology also makes robots ideal for assembly tasks, where they can handle varying components without requiring reprogramming. For example, an AI-enabled robotic arm could assemble parts of different sizes and shapes, adjusting its grip and pressure based on real-time data from sensors.

For manufacturers, this adaptability means less downtime, fewer changeovers, and the ability to increase production capacity when demand spikes. AI-enabled robots also make it possible to introduce more complex products into the lineup without requiring a complete reorganization of the production floor. As manufacturing continues to face labor shortages, these advanced robotics solutions are proving to be valuable, enabling manufacturers to maintain or even increase productivity as headcounts decrease.

3) Insightful Software Turns Data into Actionable Intelligence

Manufacturers are no strangers to data, but with today’s scale of operations, managing and interpreting data can become overwhelming. This is where AI-driven software comes in, acting like a virtual expert that helps uncover insights hidden within complex datasets. Rather than requiring a specialist to comb through spreadsheets manually, AI can analyze data and provide actionable insights that support faster decision-making.

Instead of sifting through endless spreadsheets, manufacturers can now rely on software that understands what they’re looking for and delivers actionable intelligence. CADDi Drawer is designed to highlight operational insights, which is especially valuable for manufacturers dealing with multiple product lines or high-output facilities. This type of AI-powered software can answer nuanced questions about production efficiency, machine performance, or supply chain variables. Managers no longer have to spend hours finding answers; instead, they get instant, data-backed insights allowing them to adjust schedules, allocate resources, and identify potential issues before they escalate.

In many cases, AI-driven insights have become essential for new employees, enabling them to contribute effectively without years of experience. This technology is especially useful in today’s environment, where experienced staff may be harder to come by, allowing companies to onboard new hires more efficiently.

Moreover, AI in software can adapt to changing production demands, identifying patterns and suggesting adjustments that keep operations optimized. For instance, during supply chain shortages, AI can help find alternative materials or adjust production schedules to minimize impact on the end product. The speed and accuracy with which AI processes and applies this data helps manufacturers respond to disruptions faster, reducing the time and cost of manual adjustments.

AI’s Role in Shaping the Future of Manufacturing

AI isn’t just helping manufacturers get through disruptions; it’s positioning them to emerge stronger and more competitive. Additive manufacturing, advanced robotics, and AI-driven insights are all practical applications making a tangible difference on factory floors. By reducing lead times, enhancing flexibility, and giving businesses a clearer view of their operations, AI enables manufacturers to be proactive rather than reactive, putting them in control even in uncertain times.

However, the true value of AI in manufacturing lies not just in automating processes or boosting productivity; it’s about creating a more responsive, resilient industry that can weather even the most turbulent of times. Additive manufacturing driven by AI shortens product development cycles and diversifies product offerings. Robotics enhanced by AI provides adaptable, context-aware systems that augment workforce capabilities. And AI-powered software provides actionable insights, helping manufacturers harness the full potential of their data.

As manufacturers continue to navigate the post-pandemic landscape, AI offers the tools they need to stay ahead. The innovations reshaping manufacturing today are already proving that AI is not just a trend but a fundamental shift—one that will define the industry’s future for decades to come. For businesses ready to move beyond survival mode, AI is the gateway to a new era of opportunity and growth.

By Bob Cain, Business Unit Leader, BuildTools and Bolt, Residential Construction Division, at ECI Software Solutions. ECI’s Bolt won the ‘Best SaaS Product for Business Management‘ category, and was a finalist in the ‘Best SaaS for Improved Productivity’ and ‘Best SaaS Product for Small Business / SMEs‘ categories at The 2024 SaaS Awards.

Effective financial control is critical to the success of any construction project.

For custom home builders and remodelers, staying on top of costs, ensuring timely payments, and avoiding budget overruns can make or break a project. In an industry where thin profit margins and unpredictable expenses are commonplace, mastering financial management is no longer a luxury—it’s a necessity.

In this article, we’ll explore 12 key strategies for gaining better control of your construction finances. These strategies, when implemented properly, can help you mitigate risks, improve cost efficiency, and keep your projects on track. Whether you’re managing a large custom build or a smaller remodel, financial control will set you up for long-term success.

1) Set a realistic budget, and stick to it

A reasonable budget is the cornerstone of any well-managed project. Over-optimism during the budgeting phase can lead to significant financial stress later in the project, causing delays, cost overruns, and strained client relationships. Luckily, these are some simple steps to follow for better managing cash flow and avoiding financial pitfalls.

• Assess past project costs: Look at historical data to get a clearer sense of future expenses.
• Break down the project into phases: Account for materials, labor, overheads, and unexpected contingencies.
• Align expectations with reality: Ensure that the client and the project team agree on what the budget can cover.

2) Monitor costs regularly and adjust as needed

Costs can spiral out of control if they aren’t closely monitored. Regular tracking of both fixed and variable expenses ensures that any deviations from the budget are caught early. These best practices for monitoring costs can help manage the budget before costs grow uncontrollably.

• Setting up a schedule: Conduct weekly or bi-weekly reviews of spending against the budget to detect overspending or under-budgeting in real-time.
• Tracking individual cost centers: Break down costs into materials, labor, overheads, and any additional expenses.
• Using technology: Financial tracking software provides up-to-date reporting and automates cost monitoring.

3) Use project management software for financial tracking

In today’s construction industry, manual financial tracking is inefficient and prone to errors. Industry-specific project management software provides custom home builders and remodelers with real-time visibility into project finances, allowing for better budgeting and cost control. Here are the key features to consider:

• Real-time reporting: Get instant updates on costs as they are incurred, reducing the likelihood of budget surprises.
• Integration with procurement: Seamlessly link procurement data with financial reports to ensure no hidden costs go unnoticed.
• Cash flow management: Visualize cash inflows and outflows across all project stages.

4) Implement cost forecasting techniques

Cost forecasting is the process of estimating future expenses based on historical data, project scope, and market conditions. Accurate forecasting helps prevent budget overruns by predicting potential financial risks and preparing for them in advance. These four techniques for cost forecasting can improve financial predictability, enabling builders to make better financial decisions.

• Historical data analysis: Use past project data to estimate the costs of similar future tasks.
• Predictive modeling: Leverage technology to analyze market conditions, labor trends, and material costs.
• Regularly review forecasts: Adjust your cost forecasts as the project progresses to reflect actual costs.

5) Have a contingency plan for unexpected expenses

No matter how well-planned a project is, unforeseen expenses are almost inevitable. A contingency plan acts as a financial safety net, protecting your project from unexpected costs like material price spikes, subcontractor delays, or unforeseen structural challenges. Build an effective contingency plan with these three steps.

• Allocate 5–15% of the total project budget specifically for contingencies.
• Review and update contingency plans throughout the project.
• Include key stakeholders in contingency planning to ensure alignment in case of emergencies.

Contingency planning mitigates the financial impact of surprises and keeps projects on schedule.

6) Negotiate prices with suppliers and contractors

Building strong relationships with suppliers and contractors can lead to better pricing and favorable payment terms, which ultimately boosts your financial control. The following negotiation strategies can reduce material costs and improve cash flow, adding an extra layer of financial control.

• Bundle purchases: Group similar materials for bulk purchasing discounts.
• Leverage long-term relationships: Develop partnerships with key suppliers to negotiate favorable terms over time.
• Shop around: Always compare multiple quotes before committing to any supplier or subcontractor.

7) Keep detailed records of all expenses

Inaccurate or missing expense records can lead to financial chaos. Keeping detailed and organized records ensures that every cost is accounted for and prevents budget discrepancies, which are crucial for audits, reporting, and ensuring financial transparency. Try these tips:

• Automate with software: Use financial management tools to log all transactions and expenses automatically.
• Create categories for expenses: Group costs by materials, labor, overhead, and unexpected expenses for better organization.
• Regularly review: Ensure that records are consistently up to date to avoid errors.

8) Conduct regular financial audits

Audits are essential to ensure that a project’s finances remain aligned with the budget. A well-conducted audit can uncover inefficiencies, discrepancies, and areas where financial processes need improvement. Improve financial accountability and prevent fraud or financial mismanagement with the following:

• Schedule audits quarterly: Regular audits help identify and correct financial issues before they become significant.
• Use external auditors: For an unbiased review, consider hiring a third-party auditor.
• Cross-reference expenses with project reports: Make sure that all expenses align with the physical progress of the project.

9) Invest in training for project managers on financial control

Training project managers in financial control strategies equips them with the tools to manage a project’s finances more effectively. Well-trained managers can spot financial inefficiencies, prevent budget overruns, and improve cost control to better financial outcomes on construction projects. Consider these:

• Budgeting and forecasting: Teach managers how to set realistic budgets and forecast costs accurately.
• Financial reporting: Ensure project managers can interpret financial reports and make data-driven decisions.
• Cost control techniques: Equip managers with strategies to monitor and reduce unnecessary costs.

10) Consider outsourcing financial management tasks

For smaller firms or projects with limited resources, outsourcing financial management can offer substantial benefits. Outsourcing provides access to expert financial advice, improved efficiency, and frees up time for managers to focus on other project tasks. Benefits include:

• Expert guidance: Outsourcing allows access to professionals who specialize in construction financial management.
• Improved accuracy: External teams reduce the risk of errors in financial reporting and cash flow management.
• Cost efficiency: Outsourcing can be more cost-effective than hiring full-time financial staff for small to medium-sized projects.

11) Continuously improve processes for better financial control

Successful financial control is a continuous process of improvement. Construction firms should consistently evaluate their financial management processes to identify inefficiencies and implement changes. These best practices can help establish continuous improvement, ensuring your financial control remains sharp and adaptive.

• Regularly review financial workflows for inefficiencies and delays.
• Implement new technology to automate time-consuming financial tasks.
• Gather feedback from project managers and financial teams to identify areas for improvement.

12) Integrating procurement with project management

When procurement and project management operate in silos, financial control becomes difficult. By integrating procurement into your project management process, you gain real-time visibility into expenses and better alignment between teams. Procurement integration strengthens financial control by ensuring that spending aligns with the project’s overall financial goals. Key benefits include:

• Real-time expense tracking: Procurement software linked to project management allows for up-to-date financial reporting.
• Eliminates delays: Streamlined communication between procurement and project managers prevents budget overruns and delays.
• Better financial forecasting: Procurement data provides more accurate estimates of future expenses.

Building your budget’s best defense

Your budget is your first line of defense against financial mismanagement. However, for it to be effective, it needs to be built on strong foundations—risk mitigation, clear communication, and regular adjustments.

Account for potential financial risks, such as fluctuating material costs or labor shortages, during the planning phase. Make sure every project expense is properly categorized and accounted for. When unforeseen events occur, adjust the budget immediately to prevent long-term financial damage.

With the right defensive strategies in place, your budget can withstand most financial pressures.

Conclusion

Achieving financial control in construction projects is not only essential for profitability but also for maintaining operational efficiency and ensuring long-term business success. By following these 12 key strategies—ranging from setting realistic budgets and tracking costs to using project management software and implementing regular audits—custom home builders and remodelers can gain a firm grip on their finances, avoid costly overruns, and enhance overall project management.

The integration of technology further simplifies financial tracking and enables better decision-making through real-time data. Additionally, the focus on training, cost forecasting, and contingency planning equips construction professionals with the tools they need to stay prepared for any financial challenges that may arise.

By taking proactive steps, improving procurement processes, and continuously refining internal controls, builders can ensure not only the financial stability of their projects but also the future growth of their businesses. Financial control is the foundation upon which successful construction projects are built, and with the right strategies in place, you can maximize your profits while delivering exceptional projects.

By Vijay Nair, Head of Marketing, Alliances and Growth at Gramener – A Straive Company. Gramener was shortlisted for the ‘Best Use of AI in Transportation and Logistics‘ and ‘Best AI-driven Automation Solution‘ categories at The 2024 A.I. Awards. 

Generative AI, tracing its origins to the 1950s with pioneers like Alan Turing and John McCarthy, took a significant leap with the introduction of Generative Adversarial Networks (GANs) by Ian Goodfellow and his team in 2014.

The concept gained massive popularity with the emergence of ChatGPT in 2023, quickly becoming a hot topic. Powered by Large Language Models (LLMs), Natural Language Processing (NLP), and Machine Learning (ML), GenAI can analyze and create extensive text and images based on user prompts. This blog explores key Generative AI trends and their impact on various industries.

A Quick Recap of GenAI in 2023-24

The Generative AI market is forecasted to reach a market size of $66.62 billion by 2024 and is projected to grow at an annual rate of 20.80% from 2024 to 2030, reaching US$207.00 billion by 2030.

In 2023, Generative AI became a big deal. It’s like having smart robot helpers. For example, ChatGPT from OpenAI can help with writing and finding information, and understanding what you ask well.

GPT-4 is an updated version of ChatGPT, able to write better and understand more by reading even images. Then there’s DALL·E, also by OpenAI, which can turn your words into pictures.

From generating text content to creating graphics, videos and even business presentations – That’s how Generative AI has evolved in the last year.

Here are the top Generative AI trends for 2025 you should look out for.

AI for Creativity

The AI tool Dall-e introduced numerous unexpected capabilities, marking the first instance of an AI-generating artwork from minimal inputs. While its initial version struggled to produce high-quality art, its current iteration has significantly improved, closely aligning with user requests.

Beyond visual art, the capabilities of such AI tools extend to generating real-time animations, music, and audio for a wide range of applications. This area is poised for ongoing expansion, empowering musicians, songwriters, artists, sound effects specialists, and everyday users to fully leverage generative AI technologies for creative expression.

An example of GenAI for creativity is AI-assisted music composition. Several platforms like Aiva and OpenAI’s MuseNet already provide AI tools that assist musicians in composing original pieces of music.

GenAI for Hyper-Personalization

Hyper personalization is one of the significant Generative AI trends impacting on industries like pharma, manufacturing, retail & CPG, and logistics. The trend of leveraging Generative AI for personalization is rapidly transforming how businesses interact with their customers, offering highly customized experiences that cater to individual preferences and behaviors.

This personalization extends across various sectors, from e-commerce to entertainment, where AI algorithms analyze vast amounts of data to predict and adapt to user preferences.

Duolingo uses Generative AI to personalize the learning experience for each user, dynamically adjusting the difficulty, content, and feedback based on the learner’s progress, mistakes, and preferences.

Conversational AI

In 2025, Conversational AI will advance with hyper-personalization, contextual understanding, and multimodal interactions, integrating text, voice, images, and video. AI systems will be more emotionally intelligent, enabling empathetic and nuanced responses, especially in industries like healthcare and education.

Businesses will adopt AI agents for complex tasks, while AI-driven sales and marketing will enhance customer engagement. Ethical concerns around data privacy will drive secure, transparent models. With IoT integration, “zero interface” AI will seamlessly blend into daily routines, and voice cloning will create custom AI personas, offering more personalized and interactive experiences across various sectors.

Google Assistant is a voice-activated AI that uses natural language processing (NLP) and machine learning to engage in human-like conversations and assist users with various tasks, from setting reminders to controlling smart home devices.

Generative AI for Scientific Research

Generative AI (GenAI) is changing the way research papers, especially in the medical and pharmaceutical fields, are summarized, offering a more efficient approach to digesting complex information.

This technology leverages the power of large language models to efficiently condense lengthy documents into concise, comprehensible summaries, enabling researchers, practitioners, and industry professionals to quickly grasp key findings, methodologies, and implications without delving into the full text.

Particularly in the fast-paced environments of medical and pharmaceutical research, where staying abreast of the latest discoveries and innovations is crucial, GenAI-driven summarization tools streamline the literature review process, significantly reducing the time and effort involved in extracting vital data.

This not only enhances research productivity but also facilitates more informed decision-making and accelerates the development of new treatments and drugs, ultimately contributing to advancements in healthcare and patient outcomes.  Some of the leading summarization tools are Genei, DocHub, ChatGPT, SciSummary, etc.

AlphaFold is an AI system that uses deep learning to predict the 3D structure of proteins from their amino acid sequences. This has transformed fields like biology, medicine, and drug discovery by solving a critical scientific challenge: understanding how proteins fold into their complex structures.

Human in the Generative AI Loop

Human-in-the-Loop (HITL) is an interesting Generative AI trend in 2025, highlighting the symbiotic relationship between AI advancements and human oversight. As generative AI systems become increasingly complex, integrating human feedback into the AI training loop ensures these models remain aligned with ethical standards, cultural sensitivities, and real-world applicability.

This approach not only improves the accuracy and reliability of AI-generated outputs but also fosters a collaborative environment where human expertise guides AI evolution.

By leveraging HITL, organizations can harness the creativity and efficiency of generative AI while maintaining control over the output, ensuring that it meets the nuanced demands of diverse applications.

A real-world example of a Human in the Generative AI Loop is GitHub Copilot, an AI-powered code assistant developed by GitHub in collaboration with OpenAI.

GitHub Copilot uses generative AI to help developers write code more efficiently by suggesting code snippets, functions, or entire blocks based on the context of what the developer is working on.

Multimodal Generative AI

The ambition of generative AI is rapidly expanding beyond single-domain performance to embrace multimodal models that can process and interpret multiple types of data.

Though text-to-image and speech-to-text models like CLIP and Wave2Vec have laid the groundwork, the latest advancements aim at developing more versatile models capable of seamlessly transitioning between tasks like natural language processing (NLP) and computer vision, and even incorporating video processing capabilities, as seen with Google’s Lumiere.

This new wave of AI, including proprietary models like OpenAI’s GPT-4V and open-source options like LLaVa, is set to create more intuitive and flexible applications, enabling users to interact with AI in more complex ways, such as receiving visual aids with verbal instructions.

Moreover, by handling a broader range of data inputs, these multimodal models can improve their understanding and generate more accurate outputs, significantly enhancing AI’s utility across various fields.

A real-world example of Multimodal Generative AI is OpenAI’s GPT-4 (with vision capabilities), which can process both text and images to generate outputs across multiple formats.

Opensource Wave in Generative AI

Generative AI (GenAI) offers a wide range of possibilities, spanning the creation of complex art, music composition, pharmaceutical design, and the emulation of human speech. It has emerged as a center of both enthusiasm and critical examination.

The role of open-source projects is crucial in the progression of GenAI, as they democratize access, invite contributions from varied backgrounds, drive innovation, and help identify and address biases within the development process.

This collective approach fosters a more inclusive environment for innovation, encourages the sharing of knowledge and resources, and facilitates the rapid identification and correction of biases and errors.

Moreover, open-source initiatives in GenAI are crucial for ensuring transparency, fostering trust, and enabling ethical considerations to be at the forefront of AI development.

As a result, open source is not just a trend but a foundational element in the sustainable growth and ethical advancement of generative AI technologies. Some of the examples of GenAI in Open Sources are TensorFlow and TensorFlow Models, PyTorch and Hugging Face’s Transformers, GPT-Neo and GPT-J, Stable Diffusion, etc.

GenAI Adhering to Strong Regulatory Guidelines

Adhering GenAI to regulatory guidelines is a significant Generative AI trend to look out for.  The advancement of multimodal AI and its accessibility have raised concerns over privacy and biases.

Regulatory ambiguity could slow AI adoption due to the fear of future laws making current investments obsolete or illegal. The EU’s Artificial Intelligence Act seeks to regulate AI and ensure transparency, especially for high-risk systems. Meanwhile, in the U.S., where most AI innovation takes place, regulation remains uncertain, despite efforts to establish AI usage standards in government and commitments from developers to ethical practices.

As a trending application in the pharmaceutical sector, GenAI is streamlining regulatory compliance, creating the generation of compliant-ready materials for drug launches, promotions, and healthcare professional (HCP) outreach. It aids regulatory affairs by automating the creation of documents that adhere to strict industry standards, ensuring swift, error-free preparation for market entry and ongoing compliance, thereby significantly enhancing efficiency and reducing the risk of regulatory breaches.

Bring Your Own AI

Bring Your Own AI (BYOAI) refers to the practice of individuals or organizations integrating their custom or preferred artificial intelligence models into existing platforms, systems, or services. This approach allows for greater customization, efficiency, and alignment with specific needs or goals. There are not a lot of published real-world examples of BYOAI. However, healthcare providers are implementing AI algorithms they’ve developed or tailored to analyze patient data, predict disease outcomes, or customize treatment plans.

Banks like JPMorgan Chase have invested in developing their own AI systems called Index GPT to enhance risk management and customer service, reflecting a BYOAI approach even if not labeled as such.

GenAI-Augmented Apps and Services

In 2025, GenAI-augmented applications and services will stand at the forefront of Generative AI trends, marking a pivotal shift in how technology enhances human capabilities across various domains.

This trend involves integrating sophisticated AI algorithms into various software and platforms, enriching user experiences with personalized, intelligent functionalities.

From dynamic content creation tools that adapt to individual writing styles to smart healthcare apps providing customized treatment recommendations, AI-augmented solutions are redefining efficiency and personalization.

Some examples are Grammarly (Writing Assistance), Jasper (Content Generation), ChatGPT-Enabled Customer Support Bots, etc.

Ethical and Regulatory Focus

With the rapid evolution of GenAI, regulatory bodies are expected to introduce more stringent guidelines to address issues like bias, copyright infringement, and misinformation. Ethical frameworks for the responsible use of GenAI in commercial and public domains will take center stage, pushing for transparent, fair, and accountable AI systems.

AI-Generated Code for Automation

As more low-code/no-code platforms incorporate generative models, GenAI will be used to generate complex code, enabling faster development cycles and democratizing software engineering. Automation of software development, debugging, and even code refactoring will see a rise through GenAI-powered platforms.

A real-world example of AI-generated code for automation is OpenAI’s Codex, the AI model behind GitHub Copilot, being used to automate parts of software development.

Synthetic Data for Model Training

Synthetic data generated by AI will increasingly be used to train models where real-world data is scarce, expensive, or sensitive. This will improve AI training across industries like autonomous driving, robotics, and finance while maintaining privacy and reducing reliance on large labeled datasets.

A real-world example of synthetic data for model training is Waymo (Google’s self-driving car division) using synthetic data to train and improve its autonomous driving models.

Generative AI for Digital Twins

GenAI will integrate more with digital twin technology, creating highly accurate, dynamic simulations of physical systems or environments. This will enhance industrial applications, smart cities, and IoT systems, offering real-time insights, predictions, and decision-making capabilities for industries such as manufacturing and logistics.

A real-world example of Generative AI for Digital Twins is Siemens’ use of AI in manufacturing to optimize production processes and simulate factory environments.

Conclusion

As we enter 2025, the influence of GenAI is set to transform industries in unprecedented ways. With advancements in natural language processing, image generation, and predictive analytics, GenAI will continue to enhance creativity, productivity, and personalization across various sectors.

The integration of AI into everyday tools—from content creation and software development to customer service and design—will drive significant efficiencies and unlock new possibilities for businesses and individuals alike.

While ethical considerations, including data privacy and bias, remain critical, the evolution of GenAI promises to foster more intelligent, intuitive, and user-centric applications. As organizations adopt and refine these technologies, 2025 is poised to be a pivotal year in realizing the full potential of AI-augmented services and applications, reshaping how we work, interact, and create.

By the team at Billtrust. Billtrust were finalists in the ‘Best SaaS Product for Business Accounting and Finance‘ category at The 2024 SaaS Awards.

In an era where technology is reshaping every industry, finance has lagged behind, stubbornly clinging to traditional processes.

But the demands of today’s fast-paced global economy require us to move beyond spreadsheets and manual workflows. Finance departments, especially in B2B organizations, must adapt or risk being left in the dust. And the key to that adaptation is AI. 

Unfortunately, AI is still just a buzzword to many in finance. Some simply don’t know how transformative it is as a tool. Others understand its ability, but lack the confidence to implement it. In fact, when Billtrust surveyed 375 CFOs across industries, just under half (49%) claimed to be very knowledgeable about AI. 

Nevertheless, AI has the potential to fundamentally change how finance operates. From streamlining payment processes to providing real-time financial insights, AI can unlock the potential of finance teams and allow them to shift from number-crunchers to strategic leaders.

The Resistance to Change in Finance

Let’s face it—finance has always been risk-averse, and for good reason. Accuracy and compliance are paramount, and this caution has served the function well for decades. But in today’s digital age, sticking with old systems is no longer a safe strategy. It’s a liability.

Research shows that 30-40% of B2B payments are still paper-based. In a world where transactions occur in milliseconds, the inefficiency of paper is staggering. The slow pace of digital transformation in finance is preventing businesses from unlocking opportunities for growth, agility, and efficiency.

This needs to change. We need to drag finance into the digital age—and AI is the tool to do it.

Why AI is the Game-Changer Finance Needs

Artificial intelligence holds the power to revolutionize finance in ways we’re only beginning to understand. Today, AI is being deployed to automate repetitive tasks, analyze massive datasets, and generate predictive insights, helping finance teams make faster, smarter decisions.

But AI’s true potential lies in its ability to elevate finance beyond operational efficiencies. It enables businesses to leverage data for forward-thinking strategies. Imagine real-time, AI-driven forecasting that goes beyond historical data to anticipate market trends and customer behavior. This isn’t some far-off dream—it’s happening now, and finance leaders must take advantage of it.

Moreover, AI is optimizing processes like order-to-cash, ensuring that businesses get paid faster and more efficiently. With payment management issues sparking concern across finance, and 52% of executives saying their business has had to change major aspects of their OTC processes over the past 24 months as a result, the value of this really can’t be overstated. By analyzing transaction data, AI can identify patterns and recommend actions that finance teams can take to improve cash flow and reduce risk. This not only enhances operational efficiency but also strengthens the company’s financial health.

Addressing the Challenges of AI Adoption

Companies that adopt AI today will be better positioned to compete in tomorrow’s economy. Those that don’t will fall behind, unable to keep pace with competitors who use AI to enhance their financial processes.

Of course, implementing AI comes with its own set of challenges. Data privacy, security, and regulatory compliance are top concerns for finance teams, and rightfully so. Finance leaders need to ensure that the AI tools they adopt are secure, compliant, and tailored to their specific business needs.

Another challenge is the quality of data. AI is only as good as the data it processes. Poorly organized, inaccurate, or incomplete data will limit the effectiveness of AI-driven insights. 

But here’s the opportunity: by focusing on improving data quality, finance teams can set the stage for long-term success. The process of cleaning and structuring data not only supports AI adoption but also enhances decision-making capabilities, creating a stronger, more resilient financial foundation. With the right investments in data and technology, these initial hurdles can become the springboard for a more agile, innovative, and competitive finance function.

The Future is Now

CFOs can no longer afford to sit on the sidelines of the digital revolution. The finance function is at a turning point, and AI is the engine that will drive it forward. Those who act now will lead the charge in transforming finance into a strategic powerhouse. Those who don’t risk being left behind in a digital world that won’t wait.

We need to drag finance into the digital age. And we need AI to do it.

By David Ly, Founder and CEO at Iveda. Iveda were shortlisted for the ‘Best Use of AI in Manufacturing‘ category at The 2024 A.I. Awards. 

While traditional images of factories historically evoke thoughts of dirty, outdated machinery, modern manufacturing facilities are now heavily integrated with advanced technology and cutting-edge innovations.

The increasing demand for automation, plus the desire to offload mundane tasks to machines, are driving manufacturers to seek solutions that minimize human error and enhance productivity. Artificial intelligence (AI) and augmented reality (AR) software is being designed to simplify day-to-day manufacturing tasks, enabling workers to work smarter (not harder), ultimately reducing the need for human oversight in many processes.

Developing consistent and accurate workflows enables manufacturers to operate faster, but can be risky given the precautions needed to stay safe on the job. Implementing systems designed to enhance safety and efficiency at the same time provides manufacturers with more opportunities for comprehensive training, real-time data capture, and improved product quality. By enhancing standardized systems with AI and AR, manufacturers can accelerate decision-making, creating a workflow that is easier and more effective for everyone involved.

The Cost of Human Error

Human error can cost companies significant amounts of money each year. The International Journal of Engineering Research and Applications estimates that human errors account for nearly 80% of quality defects in manufacturing. Whether a meter number is misread or safety protocols are ignored, the power of AI and AR can help the manufacturing industry curb these costly mistakes.

For example, manufacturing personnel working with machinery must verify proper wiring to ensure the correct and safe operation of equipment. Without a robust verification mechanism, accidents like electrical fires can occur, posing risk to employees and damaging the equipment and facility. With AI on their side, maintenance personnel can verify the correct wiring configuration during visual inspection––leveraging the expertise of seasoned employees through the trained Al model––which then acts as a guide for personnel, correcting mistakes and preventing accidents. This continuous learning approach enhances protocols and helps identify potential safety risks, allowing human workers to concentrate on tasks that require more robust oversight and critical thinking.

During periods of heightened stress or understaffing, it can be easy for manufacturers to make mistakes or overlook potential problems. If a worker is responsible for reading 50 meters by hand, it’s more than possible for them to miscalculate readings –– after all, they’re only human. AI can help with number identification, turning analog information into digital data and automating readouts for maximum efficiency. Algorithms are designed for continuous learning, enabling the platform to tackle new challenges and deliver tailored solutions for a wide range of use cases.

In today’s fast paced environment, human’s need an AI powered co-worker that can complete tasks accurately while maintaining deadlines and high expectations. Those who choose to harness the power of AI and AR will stand out against those who do not and over time, those who choose not to jump on the AI train will be left behind at the station.

Harnessing the Power of AI and AR

According to Fortune Business Insights, the global augmented reality market is projected to exceed $1.1 trillion by 2030. As demand for technology-driven solutions continues to grow, the tech’s ability to adapt to unique job functions is essential, especially across the manufacturing industry. AI and AR solutions come in various forms, one notable example being Iveda’s vumastAR, an AI vision software designed to make day-to-day tasks easier than ever before. Iveda’s next-generation, customizable AI system is compatible with most smart devices––from smartphones and tablets to IP cameras, augmented reality (AR) glasses, and more––taking human skills and ability to the next level through the power of artificial intelligence.

As the manufacturing industry continues to step into the next industrial revolution, the use cases for AI and AR  are proving to be endless given the technology can be trained to recognize almost any task within a controlled environment. Picture this: manufacturing professionals can monitor multiple meter gauges with a product like vumastAR, accurately augmenting the process of identifying abnormalities and greatly reducing manual labor while eliminating human error. These capabilities empower teams to make well-informed decisions swiftly, boosting productivity and accuracy across all levels of the organization, which ultimately impacts revenue streams and customer satisfaction.

Additional Use Cases Include:

• Quality and Maintenance Exams: AI and AR has the power to assist with critical measuring of carcinogenic chemical compound levels, electrical wiring, and welding inspections.
• Factory and Line Work: Fast and accurate machine recognition enables itemized counting, inventory audits, and assembly kitting.
• Supply Chain: Detect defects and anomalies for improved accuracy, increasing the bottom line by actively reducing lost revenue incurred from manual mistakes.

Understanding AI and AR’s Limitations

Industrial AI and AR are enabling manufacturers to work in new and innovative ways. However, as with any new solution in the marketplace, there will always be limitations that take time to address and improve. One significant drawback of adopting AR and VR is the time required to train AI to recognize specific items. Like humans, AI needs time to develop and adapt to a particular industry or business in order to meet specific needs. With each use, the products learn from user interactions, ultimately becoming faster and more efficient over time. The secret to developing a robust and efficient AI and AR-powered workflows is to continuously work with the technology and to correct mistakes early and often for maximum return on investment.

The Future is Here, Embrace It

Companies worldwide are harnessing the power of AI and AR to enhance workflows and streamline everyday tasks. Manufacturers considering the integration of AI and AR technology into their workflows should leverage their existing infrastructure to minimize costs and reduce the time required to implement entirely new systems, maximizing the benefits of this emerging technology.

The true value of integrating AI and AR into existing infrastructure lies in the ability to customize these technologies to meet the specific needs of manufacturers—without the need for costly new equipment or infrastructure. The future of AI and AR will be embedded in the systems we already know, offering a sense of familiarity and ease as these technologies enhance everyday operations. In essence, today’s equipment will be upgraded with the innovations of tomorrow.

We stand at a pivotal moment in manufacturing, where rising customer demand for faster turnaround and higher quality necessitates the adoption of these technologies. The fourth industrial revolution is just beginning, and there will be a clear divide between manufacturers who embrace innovation and those who cling to outdated practices. The question is: which side of the coin are you on?