AI-powered e-commerce platforms scale SMB operations by providing sophisticated pricing analysis and inventory management. Encryption and blockchain applications significantly mitigate concerns about data security and privacy by enhancing data protection and ensuring the integrity and confidentiality of information.

A 2024 survey of 530 small and medium-sized businesses (SMBs) reveals that AI adoption remains modest, with only 39% leveraging this technology. Content creation seems to be the main use case, with 58% of these businesses leveraging AI to support content marketing and 49% to write social media prompts.

Despite reported satisfaction with AI’s time and cost-saving benefits, the predominant use of ChatGPT or Google Gemini mentioned in the survey suggests that these SMBs have been barely scratching the surface of AI’s full potential. Indeed, AI offers far more advanced capabilities, namely pricing analysis and inventory management. Businesses willing to embrace these tools stand to gain an immense first-mover advantage.

However, privacy and security concerns raised by many SMBs regarding deeper AI integration merit attention. The counterargument suggests that the e-commerce platforms offering smart pricing and inventory management solutions would also provide encryption and blockchain applications to mitigate risks. 

Regressions and trees: AI under the hood

Every SMB knows that setting optimal product or service prices and effectively managing inventory are crucial for growth. Price too low to beat competitors, and profits suffer. Over-order raw materials, and capital gets tied up unnecessarily. But what some businesses fail to realize is that AI-powered e-commerce platforms can perform all these tasks in real time without the risks associated with human error.

At the center is machine learning, which iteratively refines algorithms and statistical models based on input data to determine optimal prices and forecast inventory demand. The types of machine learning models employed vary across industries, but two stand out in the context of pricing and inventory management.

Regression analysis has been the gold standard in determining prices. This method involves predicting the relationship between the combined effects of multiple explanatory variables and an outcome within a multidimensional space. It achieves this by plotting a “best-fit” hyperplane through the data points in a way that minimizes the differences between the actual and predicted values. In the context of pricing, the model may consider how factors like region, market conditions, seasonality, and demand collectively impact the historical sales data of a given product or service. The resulting best-fit hyperplane would denote the most precise price point for every single permutation or change in the predictors (which could number in the millions).

What machine learning contributes to this traditional tried-and-true econometric technique is scope and velocity. Whereas human analysts would manually deploy this tool within Excel, using relatively simple data sets from prior years, machine learning conducts regression analysis on significantly more comprehensive data sets. Moreover, it can continuously adapt its analysis in real-time by feeding it the latest data. This eliminates the need for a human to spend countless hours every quarter redoing the work.

In summary, machine-learning regression ensures that price points are constantly being updated in real time with a level of precision that far surpasses human capability.

As for inventory management, an effective methodology within machine learning’s arsenal would be decision trees.

Decision trees resolve inventory challenges using a flowchart-like logic. The analysis begins by asking a core question, such as whether there is a need to order more products to prevent understocking. Next, a myriad of factors that are suspected to have an effect on this decision are fed to the model, such as current stock, recent sales, seasonal trends, economic influences, storage space, etc. Each of these factors become a branch in the decision tree. As the tree branches out, it evaluates the significance of each factor in predicting the need for product orders against historical data. For example, if data indicates that low stock levels during certain seasons consistently lead to stockouts, the model may prioritize the “current stock” branch and recommend ordering more products when stock levels are low during those seasons.

Ultimately, the tree reaches a final decision node where it determines whether to order more products. This conclusion is based on the cumulative analysis of all factors and their historical impact in similar situations.

The beauty of decision trees is that they provide businesses an objective decision-making framework that systematically and simultaneously weigh a large number of variables — a task that humans would struggle to replicate given the large volumes of data that must be processed.

The machine learning techniques discussed earlier are just examples for illustration purposes; real-world applications are considerably more advanced. The key takeaway is that e-commerce platforms offering AI-powered insights can scale any SMB— regardless of its needs.

Balancing AI with data security

With great power comes great responsibility, as the saying goes. An e-commerce platform harnesses the wondrous capabilities of AI must also guarantee the protection of its users and customers’ data. This is especially relevant given that AI routinely accesses large amounts of data, increasing the risk of data breaches. Without proper security measures, sensitive information can be exposed through cyber-attacks.

When customers are browsing an online marketplace, data privacy and security are top of mind. According to a PwC survey, 71% of consumers will not purchase from a business they do not trust. Along the same lines, 81% would cease doing business with an online company following a data breach, and 97% have expressed concern that businesses might misuse their data.

Fortunately, e-commerce platforms provide various cybersecurity measures, addressing security compromises and reassuring both customers and the SMBs that host their products on these platforms.

Encryption is a highly effective method for securing data transmission and storage. By transforming plaintext data into scrambled ciphertext, the process renders the data indecipherable to anyone without the corresponding decryption key. Therefore, even if hackers somehow manage to intercept data exchanges or gain access to databases, they will be unable to make sense of the data. Sensitive information such as names, birthdays, phone numbers, and credit card information will appear as meaningless jumble. Research from Ponemon Institute shows that encryption technologies can save businesses an average of $1.4 million per cyber-attack.

Block chain technology contributes an extra level of security to e-commerce platforms. Transaction data is organized into blocks, which are in turn linked together in a chain. Once a block joins the chain, it becomes difficult to tamper with the data within. Furthermore, copies of this “blockchain” are distributed across multiple systems worldwide so that the latter can detect any attempts to illegitimately access the data. An IDC survey suggests that American bankers are the biggest users of block chain, further underscoring confidence in this technology.

The argument here is that SMBs can enjoy the benefits of AI while maintaining data privacy and security. The right e-commerce platforms offer tried-and-true measures to safeguard data and prevent breaches.

Having your cake and eating it too

The potential of AI in SMBs remains largely untapped. As such, those daring enough to exploit machine learning to empower their business logics may reap a significant dividend over competitors who insist on doing things the old-fashioned way. By automating essential functions like pricing analysis and inventory management, businesses can achieve unprecedented levels of efficiency and accuracy. The e-commerce platforms providing these services are equipped with robust cybersecurity features, providing valuable peace of mind for SMBs.

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The rapid advancement of AI has revolutionized industries worldwide, transforming the way businesses operate. While some organizations are still catching up, AI is undeniably a game-changer, reshaping industries and redefining enterprise operations.

Estimates from Goldman Sachs suggest that AI has the potential to increase global GDP by approximately 7% (almost $7 trillion) over the next decade by enhancing labor productivity. Even with conservative predictions, AI is poised to drive significant progress in the global economy.

Perception Problems Around AI

The impact of AI on the workforce is both profound and complex. While there are many proven instances where AI integration has led to performance improvements and enhanced well-being for employees, concerns about job displacement still loom large. Reports citing AI-related job cuts have only bolstered that fear, however it’s imperative to remember the dual nature of technological innovation.  

While certain jobs may become redundant, new opportunities, particularly in AI and tech-related fields, are emerging. Gartner’s optimistic prediction suggests that AI could generate over half a billion jobs by 2033, emphasizing the need for a workforce skilled in AI technologies and applications.

It’s also crucial to consider how current roles might evolve to integrate AI tools alongside human workers. For instance, doctors could leverage advanced data analysis software to improve diagnostic accuracy, while IT professionals might utilize generative AI to swiftly and precisely obtain the scripts they need. In these scenarios, human involvement remains indispensable, but tasks can be completed more quickly and accurately.

Shifting Our Mindsets

For IT departments, traditionally at the forefront of technological innovation, the rise of AI signals a paradigm shift. AI is revolutionizing the IT industry by automating and optimizing workflows, increasing team output, and boosting cross-organizational efficiency. Rather than replacing the IT technician, AI has the potential to serve as the ultimate assistant by automating the manual, tedious tasks and enabling the technician to spend their time on high-value projects they wouldn’t otherwise have the time to tend to. This transition, however, necessitates not just adaptation to new tools, but also a fundamental shift in mindset towards embracing intelligent systems.

Central to this shift is the concept of closed-loop AI systems—an aspect of responsible AI—which ensures that any inputs to the system (such as data, sensitive information, etc.) are never used for outputs outside of the organization. In other words, any information given to the AI stays within the system, ensuring no information is compromised outside the organization, and the data is not used to train the AI or algorithm.

The Importance of Training and Development

Training and development also play a critical role in this AI-driven evolution. Recent data showed that 66% of American IT professionals agreed it’s harder for them to take days off than their colleagues who are not in the IT department, which has serious implications for burnout, employee retention, and overall satisfaction. This makes AI integration more important than ever before. But first, proper training is essential.

As IT professionals are beginning to leverage AI’s power, emphasis must be placed on cultivating skills in data analysis, algorithm development, and system optimization. Especially as organizations embrace closed-loop AI systems, considerations around data security, ethics, and workforce upskilling become imperative.

AI companions are becoming increasingly essential to ensure efficient IT operations. Luckily, innovative solutions are emerging with capabilities like ticket summaries, response generation, and even AI solutions based on device diagnostics and ticket history to help streamline daily tasks and empower IT professionals to focus on higher-value issues.

Integrating Closed-Loop Systems to Supercharge Your AI Integration

The evolution of AI technology and closed-loop systems is set to revolutionize enterprise operations. As businesses navigate this future, embracing these advancements responsibly will be crucial for staying competitive and efficient. AI’s ability to enhance decision-making, streamline processes, and drive innovation opens new avenues for growth and success.

By integrating closed-loop systems and prioritizing responsible AI, enterprises can create more responsive and adaptive environments, ensuring continuous improvement and agility. The future of enterprise technology is here, and those who adapt and leverage these powerful tools responsibly will undoubtedly lead the way in their industries.

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Artificial intelligence (AI) has made quite a name for itself in the previous year. However, lately, its pitfalls have been dominating most of the conversation. By now, we are all familiar with the failed, yet viral, Willy Wonka Glasgow experience, and have witnessed the harm AI can cause to an event and its attendees. Unfortunately, when it comes to using AI technology, “Pure Imagination” needs some limitations. 

Despite the 2017 Fyre Festival, event planners weren’t able to avoid the temptation of AI to prevent creating the next viral event hoax. The event industry can greatly benefit from technology advancements and AI, as long as event marketers and planners can implement a system of checks and balances to avoid relying too heavily on it. While it’s unlikely that this is the last instance of a misrepresented event, we can focus on learning from these mishaps, ultimately improving event experiences for everyone involved. Event planners can lean into these mistakes and turn them into lessons on how to ensure event goers aren’t led astray, address concerns and questions about the integration and use of AI, and work to prevent another viral hoax and fraudulent event. 

The Emergence of AI in Event Planning

AI has permeated nearly every industry, offering a wide range of possibilities for efficiency and innovation. Unsurprisingly, event planners have eagerly embraced AI to optimize their projects. It’s impossible to believe that event marketers won’t be leaning into the technology, so it’s imperative that they’re doing so responsibly and truthfully. The promise of AI lies in its ability to analyze vast amounts of data, predict trends, and automate tedious tasks, therefore freeing up time for planners to focus on what they do best: creativity and strategy. 

While these tools have great potential to lighten workloads, it’s essential to recognize that AI cannot completely replace the human touch in marketing efforts. Authenticity, personalization, and emotional connection are key factors in marketing that simply cannot be replicated by AI. Event marketers can find the balance with this by giving AI limitations to ensure it’s grounded in reality. With proper guardrails in place, event marketers and planners can ensure that ideas generated by AI are realistic in practice and geared towards the correct audience. 

Where Marketers Are Missing the Mark

One of the critical mistakes marketers can make is relying on AI for content creation without integrating it into the broader objectives of event planning. Authenticity in events hinges on a blend of AI-driven content development and personalized experiences. While AI can certainly generate compelling marketing copy and visuals, it lacks the intuitive understanding of human emotions and cultural nuances that are essential for creating memorable experiences.

AI tools won’t grasp the entire picture of the event – it doesn’t understand the limitations or small logistics that need to be considered. If teams plan to use AI to develop marketing visuals for an event, as was the case in the Willy Wonka debacle, there needs to be human oversight in the process. Without it, the event planners risk eroding the trust they previously worked to build with their customers. 

The Key to Successful AI Training and Integration

For AI to truly enhance event planning, it must be trained to understand and cater to the audience’s preferences. By leveraging high-quality data inputs, AI-generated output becomes more reliable, leading to more relevant marketing materials and enriched event experiences.

Integrating proprietary human inputs alongside external data sources allows AI to better grasp the nuanced needs of diverse customer segments, ensuring that events resonate authentically. This hybrid approach combines the analytical power of AI with the creativity and empathy of human planners, resulting in events that are both innovative and emotionally resonant.

As we navigate the intersection of AI and event planning, it’s imperative to ground AI systems in reality to avoid the pitfalls of overpromising and under delivering. The Willy Wonka experience serves as a cautionary tale, highlighting the importance of employing AI within a structured framework that aligns with marketing objectives.

When wielded effectively, AI serves as a powerful tool to craft compelling content and deliver personalized experiences, meeting or even exceeding the expectations of target audiences set by advertisements. By learning from the missteps of technological overreach and embracing AI carefully, event planners can unlock their full potential to create memorable and authentic experiences for attendees.

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The intersection of artificial intelligence (AI) and digital advertising to create truly engaging experiences across global audiences and cultures is reaching an inflection point. Companies everywhere are leveraging powerful trends in AI, machine learning and apps for performance marketing.

Today’s AI and machine learning technologies are allowing apps to understand speech, images, and user behavior more naturally. As a result, apps with AI capabilities are smarter and more helpful, and companies are using these technologies to create tailored experiences for customers, regardless of language or background. AI is leveling the playing field by making advanced data tools accessible to anyone, not just data scientists.

Kochava has incorporated AI and machine learning across our diverse solutions portfolio for years, such as within our advanced attribution and fraud prevention products. We have also adopted advanced technologies, like large language models (LLMs) to develop new tools.

Many organizations are instituting internal restructuring with a focus on enhancing the developer experience. The aim is to leverage the full potential of AI for smart applications, providing universal access to advanced tech tools, while adapting to changes in app store policies. Engineering teams are spearheading the development of self-service platforms managed by product teams. The primary objective is to optimize developers’ workflows, speeding up the delivery of business value, and reducing stress. These changes improve the developer experience which can help companies retain top talent.

From an overall organizational structure perspective, in pursuit of a more efficient and effective approach, Kochava is focused on enhancing developer experiences, leveraging AI for intelligent applications, democratizing access to advanced technologies, and adapting to regulatory changes in app marketplaces.

Reimagining the Future

The software and applications industry is one that evolves particularly quickly. The app market now represents a multibillion-dollar sector exhibiting no signs of slowing. This rapid growth and constant change presents abundant opportunities for developers to build innovative new applications while pursuing their passions. For app developers, monitoring trends provides inspiration for maintaining engaging, innovative user experiences.

As AI integration increases, standards will develop to ensure AI can automatically interface between applications. It will utilize transactional and external data to provide insights. Applications will shift from set features to AI-driven predictions and recommendations tailored for each user. This advances data-driven decision making and transforms the experience for customers, users, teams, and developers.

Democratizing access to generative AI across organizations has the potential to automate many tasks, lower costs and create new opportunities. It changes the competitive landscape by making vast knowledge more accessible to anyone through natural language. Increased access to information is a big trend.

Changes in regulations are also allowing third-party app stores on many operating systems. This reflects a shift that provides developers new opportunities and challenges as larger publishers set up their own markets.

Trends Transforming How We Work

Intelligent Applications: With increasing AI integration, standards will develop to ensure AI can automatically interface with other applications. It utilizes transactional and external data to provide application insights. Shifting from procedural features to AI-driven predictions and recommendations personalized for users advances data-driven decision-making, transforming customer, user, product owner, architect, and developer experiences.

Generative AI Democratization: Democratizing access to generative AI across the organization. With broad task automation potential, it reduces costs while fostering growth opportunities. This transforms enterprise competitiveness and facilitates democratized information and skills access across roles and functions. Enabling natural language interface access to vast knowledge equitably is key.

Third-Party App Stores: Regulation changes may allow third-party app stores on operating systems, anticipated to increase larger publishers’ establishment in these markets. This reflects an evolving app store ecosystem with new opportunities and challenges for both platform operators and developers.

AI can help marketers think outside the box to find those gems that standard sources aren’t turning up. Exercise AI’s power to maximize your marketing campaigns and connect with customers worldwide!

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In the rapidly evolving landscape of artificial intelligence (AI), media companies and other businesses alike continue to find themselves entangled in a web of lawsuits and public criticism, shining a spotlight on the issue of ethical transparency. Journalism has long been plagued by issues around deception — consumers often wonder what’s sensationalism and what’s not. However, with the latest casualty in the ongoing Sports Illustrated debacle, whose reputation greatly suffered after being accused of employing non-existent authors for AI-generated articles, a new fear among consumers was unlocked. Can consumers trust even the most renowned organizations to leverage AI effectively? 

To further illustrate AI’s negative implications, early last year Gannett faced similar scrutiny when its AI experiment took an unexpected turn. Previously, the newspaper chain used AI  to write high school sports dispatches, however, the technology proved to be more harmful than helpful after it made several major mistakes in articles. The newspaper laid off part of its workforce, which was likely in hopes AI could replace human workers. 

Meaningful Change Starts at The Top 

It’s clear the future of AI will face a negative outlook without meaningful change. This change begins at the corporate level where organizations play a key role in shaping ethical practices around AI usage and trickles down to the employees who leverage it. As with most facets of business, change begins at the top of the organization.

In the case of AI, companies must not only prioritize the responsible integration of AI but also foster a culture that values ethical considerations (AI and any other endeavor), accountability, and transparency. By committing to these principles, leadership, and C-level executives set the tone for a transformative shift that acknowledges both the positive and negative impact of AI technologies.

To avoid any potential mishaps, workforce training should be set in place and revisited at a regular cadence to empower employees with the knowledge and skills necessary to combat the ethical complexities of AI.

However, change doesn’t stop at leadership; it also relates to the employees who use AI tools. Employees should be equipped with the knowledge and skills necessary to navigate ethical considerations. This includes understanding the limitations and biases as well as learning from the mistakes of others who’ve experienced negative implications using AI technologies, such as the organizations previously aforementioned. 

By cultivating a well-informed and ethically conscious workforce, organizations can remain compliant while also bettering the workplace environment, all while mitigating detrimental risks. The collaborative effort of corporations and their employees is an essential stepping stone to building a more positive outlook for the use of AI and other technological advancements to come.

How to Improve Transparency Around AI Usage

Tom Rosenstiel, a professor of journalism ethics at the University of Maryland, emphasized the importance of truth and transparency in media specifically. He argues that experimentation with AI is acceptable, but attempting to conceal it will inevitably raise ethical red flags for consumers. “If you want to be in the truth-telling business, which journalists claim they do, you shouldn’t tell lies,” Rosenstiel asserted. Lies, consumers have asserted, include failing to share how articles are being written, such as with the use of AI. 

The media landscape’s ongoing transparency struggle with AI is further highlighted by a lawsuit filed by The New York Times against Microsoft and OpenAI in December. The Times alleges intellectual property violations related to its journalistic content appearing in ChatGPT training data. This ongoing legal battle illuminates a slew of other AI-related copyright suits, with experts noting a more focused approach to the causes of action.

With the rise of AI-related lawsuits and public scrutiny over AI usage growing, this begs the question, how do businesses bridge the gap between consumer distrust and using AI in an ethical way that streamlines workflows?

Boosting the Understanding of the Collaborative Effort Between AI and Humans

Enhancing transparency around AI usage in media involves a comprehensive multifaceted approach. The first, arguably most important step to take is for media companies (and any other business) to not only acknowledge the integration of artificial intelligence but actively share the role it plays in content creation. This includes highlighting whether AI was used for researching, editing, writing, or a combination of the three. In turn, media organizations must implement clear disclosure in any easy-to-locate place on their web pages, openly informing the audience when and where AI tools were used for the production of articles. 

Educating the general masses about AI and its role in content creation is equally important. Businesses can take a more proactive approach to help consumers understand how AI technologies work by offering insight into the inner workings of AI (such as its algorithms), the ethical guidelines that govern their use, and how much human oversight is involved. For example, sharing if the work was edited by an actual person or if AI was used for research but written by a human. 

Public awareness campaigns, informative articles, and interactive platforms can all come into play to help bridge the knowledge gap, empowering consumers to make informed decisions about the content they choose to consume. By improving transparency and calling attention to exactly how AI will be used, businesses only stand to build greater trust with their intended audience and mitigate concerns. Consumers are proving authenticity aligns with their core values, and businesses must comply with consumer expectations to stay ahead.

Lastly, establishing industry-wide standards for AI usage in journalism and every other industry can contribute to driving transparency forward. This begins with collaboration among media organizations, tech developers, and ethical experts to generate clear guidelines that outline best practices for AI usage. By developing these standards, businesses are looped into how and where to showcase disclosure protocols and how to address potential biases in AI algorithms. Clear standards also ensure every player upholds its commitment to transparency, leading to improved trust for both creators and consumers as AI continues to play a larger role in journalism.

Establishing A New Era of AI Trust

In the face of escalating AI-related lawsuits and growing public concern, the only clear route for businesses to take is to work diligently to bridge the gap between consumer distrust and ethical AI usage. The evolving landscape of AI demands a closer examination of how others have failed and what businesses can learn from these setbacks for a brighter road ahead. The Sports Illustrated, Microsoft, and Gannett examples highlight the need for prominent companies to set a more positive example, striking a balance between innovation and maintaining public trust.

To navigate these challenges successfully, organizations will need to become transparent about how they’re using AI. This starts with acknowledging how exactly they’re leveraging AI, and sharing if it’s a collaborative effort between AI and humans in content creation. Implementing clear disclosures, whether in the form of an individual AI usage landing page or standardized labels for AI-contributed content, helps ensure consumers stay in the know, building more trust through openness. The ongoing legal battles also bring attention to the need for industry-wide standards that outline best practices in AI integration, ensuring greater uniformity and understanding.

In an era where consumer trust has the power to make or break a business, all publicity is not necessarily good publicity. This is evident by the continuous negative attention large corporations continue to receive, months after these incidents take place. But it’s not all doom and gloom for AI. A recent study found that 31.8% of respondents think generative AI and/or machine learning will help their business a lot this year. The ethical use of AI remains a challenge to accomplish across the board, however, lawsuits and public backlash, as detrimental as they may be, are undoubtedly paving the way for a more harmonious future.

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The rapid advancement of artificial intelligence (AI) is revolutionising our lives and work, making processes more efficient. Technologies like large-scale machine learning and natural language processing models, such as ChatGPT, are pushing the boundaries of what was once confined to the realm of science fiction. However, a significant challenge remains in bridging the gap between technical brilliance and real-world application.

While AI has made significant progress in virtual environments, the introduction of AI-powered general-purpose robots in the physical world still faces substantial obstacles. Why is this the case, and how can we address these barriers? We explore the topic in more detail below. 

Energy efficiency stands out as a primary obstacle. At its core, a robot is essentially a self-propelled computer. Anyone who has used a laptop knows that even the best devices struggle to operate for more than a few hours without recharging. With robots, energy demands are even higher due to internal processes and physical movement. Safety considerations prevent them from relying on tethered connections, necessitating extended battery life. 

Unfortunately, current robot mechanics and autonomous systems lack the energy efficiency required for sustained operation. They require frequent and extended charging periods to perform optimally. While the first generation of robots is utilised in industrial settings for manufacturing, they remain constantly tethered to a power source. Although there are general-purpose robots available, like Sanctuary’s Phoenix humanoid, they are still cumbersome and expensive. It will likely take five to ten more iterations before we achieve a model that is truly independent, freely moving, and capable of performing various tasks.

To bridge this gap, we must start with smaller and simpler applications that gradually lead to full AI integration in the physical world. Cobots, which are robots designed for simple tasks, can play a crucial role in this process. Examples include self-driving wheelchairs, robots cleaning building facades, or autonomous technology performing complex, focused tasks like a smoke-diving robot searching for people or a drone fixing power lines. The key is focusing on single-duty performance, not only to enhance energy efficiency but also to achieve the highest standard of work.

Bringing AI into the physical environment requires addressing the complexity of real-world navigation. Human spatial awareness and navigation involve intricate mental processing, making it challenging to explain to robots. One solution lies in sensors, particularly 3D sensors like depth cameras, which capture the geometry and texture of physical objects. By analysing this data, AI algorithms can develop a better understanding of objects in the physical world. This understanding is vital for tasks such as spatial relationships, object movement, and human interactions. AI-powered mapping and localisation systems that generate maps of the physical environment and track object movements become integral in creating genuinely autonomous robotic assistants.

Mechanical efficiency is another critical aspect. By improving the way robots move, potentially by utilising artificial muscles and joints to mimic human motion, we can reduce their energy requirements. However, achieving fully functional humanoid technology is still a considerable distance away.
In the tech industry, the pursuit of intelligent robots has been a long-standing goal. However, a more nuanced approach is now necessary. Instead of relying solely on overall solutions from industry giants, an evolutionary methodology is called for. Specialist startup companies with relevant expertise can produce individual components that address the multiple challenges faced by developers. Once these components are in place, collaboration can occur, leading to the creation of efficient, functional, and affordable general-purpose robots.

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When generative artificial intelligence (AI) was released to the public in late 2022, it paved the way for new and disruptive solutions to enter the corporate world. Boasting extraordinary potential for a range of business functions, financial institutions have already begun integrating the technology into their existing systems and processes. 

In this article, we look to cut through the hype and negative noise surrounding AI by demonstrating how Large Language Models (LLMs) like ChatGPT can be integrated with process automation platforms to provide compelling, enterprise grade solutions to some of the financial sector’s biggest challenges. Not least, in the arena of regulatory intelligence, where it is allowing users to capture and interact with multiple regulatory news bulletins, produce summaries for distributions to end clients, and more.  

Building on the middle ground through integration 

Many organizations have developed their own automation initiatives within AI Centers of Excellence. In addition, the arrival of specialist model providers and generalist models provided by hyperscalers, like AWS (Textract) and Azure (Form Recognizer), has created a middle ground rife with both uncertainty and opportunity.  

For finance and operations, this middle ground has been particularly fraught. As a highly regulated area with stringent demands for repeatability, consistency, transparency, and high levels of control, it is difficult to bring AI projects into production. Consequently, enterprise platforms that offer contained and integrated AI environments can help AI Centers of Excellence get to value more quickly. The platforms accomplish this by providing safe and quick access to AI models and coupling them with enterprise automation features that enhance the likes of transformation and workflows. 

Although the ways in which modern AI and ML can be applied to business processes is theoretically limitless, right now, one of the most immediate relates to regulatory intelligence. 

Applying AI to regulatory intelligence 

In the data automation space, LLMs can capably deal with scenarios where the layout and format of data sources change significantly and often. However, their use in data pipelines – where criticality of accuracy and repeatability is imperative – is less reliable. For now, this precludes publicly available AI models from being used freely across all business functions. 

However, regulatory intelligence is different. On a daily basis, leading tax consultancies, regulators, and local market experts release news bulletins that cover changes to global tax regulations. Received as emails or PDFs, tax professionals are presented with an abundance of information that must be manually interrogated to identify which changes will affect their core tax processes. These changes must then be summarized, distributed downstream to clients, and then implemented into client systems. The impact on time and resources is keenly felt across the industry. It is a friction point within tax processing that has, until now, been denied any kind of practical solution. By integrating platforms with AI models, the promise of such a solution is finally within reach.

AI can be used to capture new tax regulation at source and examine the contents on behalf of the tax professional. Rather than spending hours poring over documents, tax professionals can simply direct the AI to prioritize the outreach in order of importance, extract the relevant information, and automatically produce summaries.  

Automated alerting and reporting that captures regulatory news bulletins is delivered through email notifications, dashboards, or other forms of communication and highlights important updates, trends, and potential impacts on the business. By using event extraction techniques, AI can then detect and track specific regulatory events or changes mentioned in the news bulletins, such as policy updates, regulatory announcements, enforcement actions, or changes in compliance requirements. 

The tracking of these events means keeping up-to-date with the latest regulatory developments is quicker and easier. Moreover, regular performance evaluations of your AI system will allow you to make improvements based on user feedback, while new data helps to further refine the system’s accuracy and ensure it remains current with an evolving regulatory landscape.

AI as a tool, not a replacement 

No one can say with any degree of certainty how AI will come to transform our understanding of the nature of work in the coming years. However, right now, tools such as ChatGPT are simply not capable of performing vital tasks unless they’re embedded in a controlled end-to-end business process that supports scalability, for example, by monitoring more news sources or reacting to updates faster.

As such, where AI is integrated with platforms for processes such as regulatory intelligence, it is not to replace human workers, it is to provide them with a formidable tool. 

Hours spent each day scrutinizing outreach on new regulations can be reduced to minutes, leaving this newly freed time to be exploited for more value-adding activities. Governance is built-in too. In collaboration with IT teams, system prompts developed by tax professionals can prime AI to respond in certain ways, thereby preventing the risk of users venturing beyond the tool’s designated boundaries. 

This level of control equips AI with a human-like attitude to work, meaning it will answer questions within supplied contexts and request clarification rather than provide responses based on guesswork. As businesses and organizations continue to adopt AI technologies, it is crucial that they develop working practices to ensure the limits of AI are not exceeded. The system prompt provides a clear mechanism to set these boundaries, eliminating the risk of misuse and ensuring accountability.

In time, platforms will inevitably go on to integrate with multiple AI systems, all capable of completing different tasks within the whole regulatory space. Organizations will be able to decide which use cases different systems are best suited to and manage how users operate each one. Judgements that were once the preserve of IT teams will be democratized across all operational departments within the organization. 

In the meantime, when integrated into platforms, ChatGPT and other adjacent LLMs are set to provide transformational empowerment to those operating in the regulatory intelligence space. 

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In today’s rapidly advancing digital era, the life sciences industry is undergoing a profound transformation fueled by cutting-edge technologies. Among these technologies, artificial intelligence (AI) has emerged as a powerful tool that holds immense potential to revolutionize various aspects of healthcare. Clinical trials, the cornerstone of evidence-based medicine, are no exception to this transformative wave. The integration of AI into clinical trials has the capacity to enhance efficiency, accuracy, and patient outcomes significantly.

In this article, we will talk about how healthcare leaders can benefit from integrating AI into the traditional clinical trial process.

Table of Content

What are the pain points in the traditional clinical trial process?
Why NOW is the time for AI-enabled clinical trial research?
Which transformation approach works best for C-suiters to revolutionize clinical trials?
How can healthcare leaders benefit across various aspects of the clinical research process?
What is the future of clinical trial development?

With all the above benefits, it is vital to ask what are the pain points in the traditional clinical trial process.

Traditional clinical trial processes are marred by numerous challenges, such as prolonged timelines, exorbitant costs, limited patient participation, and complex data analysis. According to a report by Deloitte, only 11% of drugs entering Phase I clinical trials receive regulatory approval, indicating an urgent need for innovation in this critical domain. However, by leveraging AI technologies, C-suite executives can drive a paradigm shift in clinical trials, addressing these challenges and unlocking new opportunities.

AI-powered platforms can provide healthcare leaders with intelligent insights and recommendations, optimizing patient recruitment and retention strategies, while also improving patient safety through early detection of adverse events. By leveraging AI, clinical trial stakeholders and leaders can achieve faster enrollment, reduce dropout rates, and ultimately accelerate the delivery of innovative therapies to patients in need.

In order to drive meaningful change and address the pressing challenges within the industry, C-suite leaders must seize the opportunity to revolutionize clinical trials through AI. By doing so, they can usher in a new era of faster, more efficient, and patient-centric drug development that has the potential to transform healthcare outcomes on a global scale.

But it brings us to address the question, “Why NOW is the time for AI-enabled clinical trial research”?

To answer this, let us check the two fronts in which technology is advancing:

1. Standardized approaches to industrialization and scaling of machine learning (ML)—for example, MLOps (ML operations) and DataOps (data operations) alongside customized services and platforms.
2. Development of deep-learning approaches for designing new molecules and computer vision, which are increasingly accessible through public code repositories and academic literature.

These two fronts i.e. advancements in technology and regulatory openness to innovation have combined to make AI-enabled clinical trial research a practicable proposition. So with this reasonable and realistic timeframe, healthcare leaders have the opportunity to leverage the advancements and help solve the biggest pain points by integrating AI and transforming clinical trials.

Bringing us to another question, which transformation approach works best for C-suiters to revolutionize clinical trials?

1. Asset-Centric Restructuring
Restructuring R&D empowers asset teams that operate at a fast pace similar to small biotech companies. This approach involves decentralizing decision-making authority, fostering an agile culture, and adopting a tailored talent model.

2. Operating-Model Enhancement
Enhancing the R&D operating model to improve efficiency and promote innovation. This could include reassessing the role of contract research organizations (CROs) and bringing essential capabilities in-house, establishing platforms for rapid development, or reconfiguring the global innovation footprint through new alliances, partnerships, expansion in Asia, or other strategies.

3. Functional Optimization
Optimizing specific parts of the value chain within key R&D functions to gain a competitive edge. Target areas may involve innovative trial design, digital-enabled site selection, agile study start-up and conduct, submission excellence, and launch planning.

Even though these transformative approaches have yielded successful results; how can healthcare leaders benefit across various aspects of the clinical research process?

Study Design
Utilizing AI, ML, and natural language processing (NLP) tools, leaders can harness extensive healthcare data sets to evaluate and choose optimal primary and secondary endpoints in study design. This ensures the definition of the most relevant protocols for regulators, payers, and patients. By doing so, healthcare leaders can mitigate the detrimental impact of poor study design on the cost, efficiency, and success potential of clinical trials. This approach helps optimize study design by providing insights into ideal country and site strategies, enrollment models, patient recruitment, and start-up plans.

As a result, improved study design yields several advantages. It leads to more predictable results, shorter protocol development cycles, fewer protocol amendments, and greater overall efficiency throughout the study. Moreover, it enhances recruitment rates, reduces non-enrolling sites, and minimizes protocol amendments. These enhancements significantly increase the chances of success while enabling more realistic and accurate planning.

Site Identification and Patient Recruitment
Continuously identifying suitable trial sites that have access to a sufficient number of eligible patients presents an ongoing challenge in clinical research. As studies focus on more specific populations, achieving recruitment goals becomes increasingly difficult, resulting in higher costs, extended timelines, and an elevated risk of failure. According to the Tufts Center for the Study of Drug Development (CSDD), nearly half of all sites fail to meet enrollment targets.

One practical approach to address these risks is leveraging AI and ML technology to identify sites with the greatest recruitment potential and recommend appropriate recruitment strategies. This involves analyzing patient populations and proactively targeting areas that are predicted to yield the highest number of eligible patients. By doing so before even opening a single site, sponsors can reduce the number of sites needed, expedite the recruitment process, and minimize the risk of insufficient enrollment.

With such on-site and off-site benefits, healthcare leaders can deliver value quickly in the present times, bringing us to address what lies ahead for the future of clinical trial development.

With advancements in artificial intelligence (AI) and data analytics, clinical trials are expected to become more efficient, precise, and patient-centric. As highlighted in industry reports, AI has the potential to revolutionize various stages of drug development, from study design and patient recruitment to data analysis and regulatory submissions.

By leveraging AI algorithms, predictive models, and machine learning, healthcare leaders can identify suitable patient populations more effectively, design optimized trial protocols, and analyze vast amounts of data with unprecedented speed and accuracy. This not only expedites the drug development process but also enhances patient safety and improves clinical trial outcomes.

Furthermore, AI-powered platforms enable real-time monitoring of patient health, early detection of adverse events, and personalized treatment recommendations, leading to a more tailored and effective approach to healthcare. As we move forward, the future of clinical development holds immense promise, where AI and advanced technologies will continue to shape the way new therapies are discovered, developed, and delivered to patients worldwide.

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