#AI for enterprise

[minedxai]

The Role of AI in Logistics and Supply Chain Management: Revolutionizing Business Intelligence!

Artificial Intelligence (AI) tried to be a Transformative security technology logistics and supply chain management, and a range. The Global Logical Industry, valuable to over $ 8, is more idealized with rationalization, operations, improvements, and reduced costs. The pre-intelligent auto-shaping analysis forms the future of chain management that lets you further introduce and promote Business intelligence (BI). One of their most essential applications in logistics is their ability to optimize operational efficiency. Traditionally, chain management was based on static data and manual processes that were bright and subjective.

With AI in logistics and supply chain, companies now have access to the objective analysis of predictive data and models that can optimize everything by the parliament's management. Their systems use large amounts of data to ensure the question, regulate the inventory levels, and provide for the string of strings. Algorithms of automatic learners can analyze past performance, weather conditions, market trends, and other variables to generate predictions. This ability allows the companies to make proactive decisions that reduce the risk or excessive inventory, which leads to improvement and money satisfaction.

Logistics plays an essential role in street optimization. Traditional distribution systems through the best source like Mined XAI often use basic algorithms to plan the most effective shipment routes. However, it may consider a great range of factors such as traffic pretenses, predictions of the street. These points can adjust dynamically and minimize time and fluctuating operational costs. Sleep Management is another area that has a significant impact. Automation through the logistics companies enables them to improve their storage trials. Robotics drive with him and turn those goods are stored, sent, and sent.

Artificial Intelligence in supply chain: The systems to turn on by constantly increasing inventory levels

The automated passage's vowel is still to move inside and out of stores with minimum human interference. Systems concentrated on driving these cars, allowing them to collaborate with human workers. This shows productivity and reduces the risk of human error and security. Moreover, it is revolutionizing stock management in the warehouse. Artificial Intelligence in supply chain, the systems are turned on by constantly increasing inventory levels, predicting the non-products, and making recommendations for reorganizing the claims of the cl. This reduces the supervision and reserves, which are common pain in supply chain management.

In the more regions to exchange an analysis "AI" various chain proposed provisions unprofitability and proficiency. Companies can simulate different predictions, distribution calendars, and choices for suppliers to understand the impact on their net profit. This analysis gives companies a competitive advantage, allowing them to respond more effectively to market changes and operational backgrounds. Predictive analysis is another area where you offer extraordinary value in chain management and a successful chain.

Business intelligence AI works together to provide a complete view of supply chain performance.

The union of artificial intelligence and business intelligence is one of the most influential advances in the supply chain field. BI troops help companies analyze and present data significantly. Business intelligence AI works together to provide a complete view of supply chain performance. Improved traditional application of automatic learning algorithms to analyze groups of massive data, identify trends, and anticipate future results. For example, it can ensure consumption requests, helping your supply chain shows make it based on supply and logistics.

Predicate maintenance is another valuable application where you can predict when cars or equipment in the supplies are likely to fail and reduce repair time. Integration of integration in the supply chain even improves the customer's experience. Allow the wicked of the supplied string to provide the exhilaration of the most accurate distribution, improvement, and service services. Analyzing customer data can predict purchasing behavior and preferences, allowing technologies rationalize communication and experience for the final customer.

Conclusion

It's already started to fund the logistics and management of the prostitution chain, producing a revolution in efficiency and automating analysis. Business intelligence allows companies to make the best data decisions, improve operational performance, and reduce costs. As a result to event and road planning to predict the information supply and enhance customer satisfaction refreshes the chain landscape. At the same time, technology continues to evolve, and the realization of chain development provides perks, efficiency, and trade. Thanks to its extraordinary potential, the supply chain's future is intelligent, automated, and focused on data.

[stark-edward]

Unlocking the Power of Enterprise AI for Scalable and Smart Business Growth

In an era where agility, precision, and data-driven decision-making determine competitive advantage, businesses are increasingly turning to Enterprise AI to transform how they operate, innovate, and scale. From manufacturing to healthcare, finance to automotive, artificial intelligence is no longer a futuristic concept—it is a critical driver of scalable, intelligent business growth.

Omnex Systems is at the forefront of this transformation, delivering advanced AI Enterprise software designed to integrate seamlessly with core business systems, optimize performance, and foster continuous improvement. This blog explores how Enterprise AI is reshaping modern enterprises, with a focus on quality management, auditing, compliance, and process automation.

What Is Enterprise AI?

Enterprise AI refers to the application of artificial intelligence technologies—such as machine learning, natural language processing, and predictive analytics—within large-scale organizations. The goal is to automate processes, derive insights from data, and improve business outcomes.

Unlike basic automation or data analytics tools, Enterprise AI is deeply integrated into an organization’s operations. It continuously learns from data patterns, adapts to changes, and provides decision-makers with real-time intelligence to drive growth and efficiency.

The Growing Demand for AI in Enterprises

As organizations generate massive volumes of data daily, managing and interpreting this information manually is no longer sustainable. AI offers the ability to process complex datasets, identify trends, and deliver actionable insights faster and more accurately than any human could.

From forecasting demand to detecting non-conformances, AI for Enterprise enables smarter operations across departments. Companies that integrate AI early gain a competitive edge, increased operational agility, and measurable cost savings.

Key Benefits of Enterprise AI for Business Growth

Scalable Automation Enterprise AI enables intelligent automation of repetitive and time-consuming tasks such as document classification, compliance checks, and data entry. This frees up employees to focus on higher-value work and accelerates organizational scalability.

Improved Decision-Making With predictive analytics and real-time insights, AI helps leaders make more informed decisions. Whether it’s supply chain optimization or quality risk prediction, AI improves accuracy and reduces decision-making latency.

Enhanced Customer Experience AI-powered chatbots, personalized recommendations, and intelligent service delivery enhance customer satisfaction and retention, driving long-term business value.

Risk Management and Compliance AI can monitor compliance in real-time, flag deviations, and suggest corrective actions. This is particularly critical in regulated industries where non-compliance can result in financial and reputational damage.

How Omnex Systems Delivers Enterprise AI Solutions

At Omnex Systems, we provide end-to-end AI Enterprise software that integrates seamlessly with your existing quality, compliance, and operational systems. Our solutions are designed to scale with your organization and deliver measurable results across key business areas.

Let’s look at some of our core offerings that demonstrate the power of AI for Enterprise growth:

AI-Enabled Quality Management Systems (AI-enabled QMS)

Quality is the cornerstone of sustainable business growth. Omnex’s AI-enabled QMS elevates traditional quality management by embedding AI-driven intelligence into every stage of the quality lifecycle—from planning and inspection to corrective action and continuous improvement.

Key Features:

Automated detection of quality trends and anomalies

Predictive failure analysis based on historical data

Smart recommendations for CAPA (Corrective and Preventive Actions)

Real-time alerts for process deviations

With an AI-enabled QMS, quality becomes proactive rather than reactive, significantly reducing defects, recalls, and customer complaints.

AI Based Auditing for Compliance and Performance

Audits are essential for ensuring compliance and driving performance improvements, but traditional audit methods can be resource-intensive and prone to oversight.

Omnex Systems offers advanced AI based Auditing tools that automate audit planning, data collection, and reporting. Using machine learning, our software identifies audit risks, detects non-conformities early, and recommends process improvements based on analytics.

AI based auditing not only improves compliance readiness but also enhances transparency, reduces costs, and accelerates audit cycles—making audits a strategic advantage rather than a regulatory burden.

AI for Enterprise Process Optimization

Business processes—from procurement to production—often involve manual steps, delays, and inefficiencies. With Omnex’s AI for Enterprise, companies can automate workflows, eliminate bottlenecks, and enable end-to-end process visibility.

By analyzing process data in real time, AI identifies inefficiencies, predicts potential issues, and optimizes resource allocation. This leads to:

Reduced operational costs

Improved cycle times

Enhanced cross-functional collaboration

Greater agility in responding to market changes

Whether you’re managing supply chains, production schedules, or engineering change requests, Enterprise AI ensures smarter, faster, and more adaptive execution.

AI Enterprise Software for Smart Integration

Integration is key to the success of any AI initiative. Omnex Systems’ AI Enterprise software connects with your existing ERP, PLM, MES, and CRM systems to provide a unified, intelligent business ecosystem.

Our platform supports:

Centralized data management

Real-time analytics dashboards

Role-based access and user permissions

Seamless data flow between departments

This enables leaders to make unified, informed decisions based on a single source of truth, rather than fragmented reports.

Real-World Applications of Enterprise AI

Here are some examples of how businesses are leveraging Omnex’s Enterprise AI solutions:

Manufacturing: Predict equipment failure using machine learning, reducing downtime by 30%.

Automotive: Use AI to analyze warranty claim data and identify recurring quality issues.

Medical Devices: Ensure FDA compliance through real-time audit readiness and AI-based document control.

Energy & Utilities: Optimize asset management and performance forecasting through predictive analytics.

In every case, AI for Enterprise not only improves operations but also enables strategic growth by aligning technology with business objectives.

The Future of Enterprise AI

As AI technology evolves, so too will its applications in the enterprise. Future developments in natural language processing, generative AI, and edge computing will unlock even greater possibilities for real-time insights and decentralized intelligence.

Organizations that invest in Enterprise AI today will be better positioned to lead tomorrow—adapting faster, operating smarter, and growing sustainably.

Omnex Systems is committed to staying ahead of the curve by continuously enhancing our AI capabilities and ensuring our clients stay at the forefront of innovation.

Final Thoughts

Unlocking the full potential of Enterprise AI requires more than just deploying tools—it demands a strategic approach, the right partnerships, and a willingness to embrace intelligent change.

At Omnex Systems, we deliver tailored AI solutions that drive real business outcomes, from improved quality and compliance to operational agility and strategic growth. Our AI-enabled platforms are built to empower enterprises with the intelligence and flexibility they need to succeed in a fast-changing world.

For more info please contact us +1 734-761-4940  (or)  info@omnexsystems.com

[sbscglobal]

In the fast-paced digital landscape of the 21st century, businesses are constantly seeking ways to innovate, optimize operations, and gain a competitive edge. Among the myriad technologies driving this transformation, Artificial Intelligence (AI) stands out as a cornerstone of modern business strategies. Specifically, Enterprise AI tailored for the needs of large organizations has emerged as a must-have tool for companies looking to thrive in today’s highly competitive markets. Let’s delve into why Enterprise AI has become indispensable and how it is revolutionizing business practices across industries.

Driving Operational Efficiency At its core, Enterprise AI empowers businesses to streamline operations and enhance efficiency across various functions. Whether it’s automating routine tasks, optimizing supply chain management, or predicting maintenance needs, AI algorithms can analyze vast amounts of data far more effectively than humans. This capability not only saves time and reduces costs but also allows employees to focus on higher-value tasks that require human creativity and decision-making. Enhancing Customer Experiences Customer expectations have evolved dramatically in the digital age, with personalized and seamless experiences becoming the norm. Enterprise AI enables businesses to deliver on these expectations by analyzing real-time customer behavior, preferences, and feedback. This data-driven approach allows for personalized recommendations, predictive customer service, and targeted marketing campaigns, thereby fostering stronger customer relationships and increasing loyalty.

  Facilitating Data-Driven Decision Making In today’s data-rich environment, making sense of vast datasets is a significant challenge for businesses. Enterprise AI excels in this domain by providing actionable insights and predictive analytics. By leveraging machine learning models, businesses can forecast trends, identify emerging opportunities, and mitigate risks proactively. This data-driven decision-making not only enhances strategic planning but also enables agile responses to market dynamics. Improving Employee Productivity Beyond optimizing customer-facing processes, Enterprise AI can revolutionize internal operations and boost employee productivity. AI-powered tools such as virtual assistants, chatbots, and workflow automation systems streamline administrative tasks, facilitate collaboration, and provide instant access to information. This not only frees up valuable time for employees but also empowers them with the tools needed to work more efficiently and creatively. Ensuring Scalability and Flexibility One of the key advantages of Enterprise AI is its scalability across different departments and functions within an organization. Whether it’s deploying AI for HR analytics, financial forecasting, or cybersecurity, the flexibility of AI solutions allows businesses to adapt and scale according to their evolving needs. This scalability ensures that businesses can maintain competitiveness and agility in a rapidly changing market landscape. Innovation and Competitive Advantage Innovation is the lifeblood of any successful business, and Enterprise AI serves as a catalyst for innovation by uncovering new insights, optimizing processes, and fostering a culture of continuous improvement. By harnessing AI technologies, businesses can pioneer new products and services, explore new markets, and differentiate themselves from competitors who have yet to fully embrace AI-driven strategies. The adoption of Enterprise AI is no longer just a competitive advantage but a necessity for modern businesses aiming to thrive in a data-driven economy. From enhancing operational efficiency and customer experiences to driving innovation and scalability, the benefits of AI are profound and far-reaching. As businesses continue to navigate complexities and opportunities in the digital age, those who harness the power of Enterprise AI will undoubtedly lead the charge towards a more efficient, insightful, and successful future. Embracing AI isn’t just about leveraging technology—it’s about transforming businesses to meet the demands of tomorrow, today. Contact SBSC to know more Email: info@sbsc.com Website: www.sbsc.com

[probablyasocialecologist]

This is it. Generative AI, as a commercial tech phenomenon, has reached its apex. The hype is evaporating. The tech is too unreliable, too often. The vibes are terrible. The air is escaping from the bubble. To me, the question is more about whether the air will rush out all at once, sending the tech sector careening downward like a balloon that someone blew up, failed to tie off properly, and let go—or more slowly, shrinking down to size in gradual sputters, while emitting embarrassing fart sounds, like a balloon being deliberately pinched around the opening by a smirking teenager. But come on. The jig is up. The technology that was at this time last year being somberly touted as so powerful that it posed an existential threat to humanity is now worrying investors because it is apparently incapable of generating passable marketing emails reliably enough. We’ve had at least a year of companies shelling out for business-grade generative AI, and the results—painted as shinily as possible from a banking and investment sector that would love nothing more than a new technology that can automate office work and creative labor—are one big “meh.” As a Bloomberg story put it last week, “Big Tech Fails to Convince Wall Street That AI Is Paying Off.” From the piece: Amazon.com Inc., Microsoft Corp. and Alphabet Inc. had one job heading into this earnings season: show that the billions of dollars they’ve each sunk into the infrastructure propelling the artificial intelligence boom is translating into real sales. In the eyes of Wall Street, they disappointed. Shares in Google owner Alphabet have fallen 7.4% since it reported last week. Microsoft’s stock price has declined in the three days since the company’s own results. Shares of Amazon — the latest to drop its earnings on Thursday — plunged by the most since October 2022 on Friday. Silicon Valley hailed 2024 as the year that companies would begin to deploy generative AI, the type of technology that can create text, images and videos from simple prompts. This mass adoption is meant to finally bring about meaningful profits from the likes of Google’s Gemini and Microsoft’s Copilot. The fact that those returns have yet to meaningfully materialize is stoking broader concerns about how worthwhile AI will really prove to be. Meanwhile, Nvidia, the AI chipmaker that soared to an absurd $3 trillion valuation, is losing that value with every passing day—26% over the last month or so, and some analysts believe that’s just the beginning. These declines are the result of less-than-stellar early results from corporations who’ve embraced enterprise-tier generative AI, the distinct lack of killer commercial products 18 months into the AI boom, and scathing financial analyses from Goldman Sachs, Sequoia Capital, and Elliot Management, each of whom concluded that there was “too much spend, too little benefit” from generative AI, in the words of Goldman, and that it was “overhyped” and a “bubble” per Elliot. As CNN put it in its report on growing fears of an AI bubble, Some investors had even anticipated that this would be the quarter that tech giants would start to signal that they were backing off their AI infrastructure investments since “AI is not delivering the returns that they were expecting,” D.A. Davidson analyst Gil Luria told CNN. The opposite happened — Google, Microsoft and Meta all signaled that they plan to spend even more as they lay the groundwork for what they hope is an AI future. This can, perhaps, explain some of the investor revolt. The tech giants have responded to mounting concerns by doubling, even tripling down, and planning on spending tens of billions of dollars on researching, developing, and deploying generative AI for the foreseeable future. All this as high profile clients are canceling their contracts. As surveys show that overwhelming majorities of workers say generative AI makes them less productive. As MIT economist and automation scholar Daron Acemoglu warns, “Don’t believe the AI hype.”

6 August 2024

[papercranesong]

Transparency in AI-use within Fandom Culture (or: how to be upfront when you risk getting shot down)

Since writing my original post, it’s been really cool hearing from fellow writers who use AI as a support tool to help them keep writing despite their mental health struggles, dyslexia, or in my case, depression.

There are valid and justified ethical concerns to do with the use of AI itself, such as the issue of consent, which I’ve tried to discuss elsewhere. But I wanted to write this post with fanfic writers and fandom in mind. There are some people like me who are already using it as a tool for writing, and so I wanted to look at how this can be done transparently and respectfully, and so that readers know and trust what they are reading.

Context

I’ve been using generative AI as a tool for over 18 months - initially as part of my work in the charity sector, and then later in writing fanfic. When my little one is older, I hope to go back into the field of Public Health, where I'll be using it as a tool to help analyse and synthesise qualitative and quantitative data (among a ton of other things), in order to help address health inequalities in the UK.

Perhaps naively, I didn’t fully understand the ethical concerns to begin with, particularly with regards to fanfic, and by the time I started realising there were issues it felt like there were no safe spaces in which to ask people about it.

Fear and loathing in Fandom Spaces

It seems like there’s this environment of fear and shame at the moment (the posts and reblogs that I see on my dash come across as absolutist, derogatory and even abusive towards anyone using AI for any reason), and I think this is why a lot of writers don’t want to be open about their use of AI, especially if they are in a small fandom and are worried what their mutuals or fellow writers and readers might think of them, or how they might get excluded from certain fandom spaces.

I’ve already seen some writing events that have a strict ‘no AI’ policy and whose language reflects the anti-AI sentiments above, so I can see why some people might join these events but not want to disclose their use of AI.  (Having said that, it’s not okay for people to enter an exchange undercover that has clear rules against AI, and to just stay silent and use it anyway. If an event or community has set boundaries, those need to be honoured and respected. We need to have integrity as AI users, and as a friend pointed out, respect has to go both ways).

Given that writers use generative AI for different reasons and in different ways, I think there needs to be a willingness to have an open and thoughtful conversation to reflect this spectrum of use. I’m just thinking off the top of my head – maybe a writing event could have these types of guidelines:

Whump-Mania Writing Event: AI Use Guidelines* 1. Be transparent. If you used AI (for ideas, research, grammar, etc.), mention it in your author notes. 2. Your words come first. AI can help but the story should be yours. No fully AI-written fic, please. 3. No purity tests. This is about honesty, not exclusion. Let’s keep the space kind and open.

(*For transparency: I asked chatgpt to come up with those guidelines, then I edited them. Also I made up the phrase Whump-Mania. As far as I know, there is no writing event called that, but it would be awesome if there was).

This is just a starter for ten, and would obviously need to be a lot more nuanced and thoughtful, especially in the context of gift exchanges, as people have varying degrees of comfort when it comes to accepting a gift where AI has been used in any aspect of writing it. (Personally, I’ve taken part in Secret Santa fic exchanges, and whilst I’d be fine with someone gifting me a work where they used AI to proof-read it, I would probably be a bit peeved if I found out they’d just taken my prompt, fed it into chatGPT and then gifted me that work).

So maybe some kind of tick box – “this is the level of AI-use I’m comfortable receiving” – ranging from ‘none’ to ‘fully-generated by AI’, with options in between. There would need a proper discussion, but I think it would be a worthwhile one so that these types of exchanges could remain inclusive.

(Just to point out again though, it’s up to the organiser at the end of the day - it’s their event and their hard work and time running it. If you’re unsure about their AI stance, it might be worth politely contacting them just to see what level of AI-use they might consider accepting, and sharing how you would use it - for example for spellchecking or research - and then politely accepting their decision without arguing or vagueposting about it, because they’re people too and it’s about remaining kind and respectful in this whole wider discussion, even if you feel hurt or misunderstood).

Tagging (or: my tag is not your tag)

So with regards to tagging – at the moment, I feel like tagging AI on AO3 isn’t a good option because there’s only one tag, “Created using generative AI”, which doesn’t distinguish between fully-AI generated works and one of my fics where I write every word and then use AI afterwards as a final spell-check before posting.

Also there’s a post going around on Tumblr at the moment that’s a screencap of the AO3 tag and listed works, and shaming people who have used the tag (although no individuals have been named). It’s got over 70,000 notes and it honestly feels a little scary.

Transparency can only work in an environment where people feel safe to speak (and tag), knowing they’re not going to get subjected to shame, hate and abuse. (Sorry for the jumpscare bold type. Just think that this is important to highlight).

Personal AI Disclaimer Use: (or, Me, Myself and AI)

So what I’m choosing to do is put an AI disclaimer use on my AO3 profile which gives me a voice to describe my own use of AI as well as advocating for more ethical AI. Then I’m putting a note in the author’s note of my fic saying “this fic was created in accordance with my personal AI disclaimer use, specifically - ” and then sharing how, e.g for research into mining duridium, a fictional ore in Star Trek.

This is the best I can come up with at the moment but I’d genuinely like to hear what other writers and readers think about it and if you have any suggestions – feel free to use the ask box (the anon function is on) or DM me. This is also why I’ve tagged this post with the fandom I’m currently writing in, for transparency and to get feedback.

It might be that because I use generative AI full stop, in any capacity, this means you’re not able to engage with my writing any more. I’m sorry for this but I do understand why you might feel like that. I appreciate your candour and wish you the Vulcan blessing of peace and long life and prospering in all you do.

Other people are understandably cautious about reading my fics going forward, and so that’s why I want to be transparent about the way I use AI, so that people can trust what they’re reading, and to make an informed decision about whether or not to engage with the story.

In conclusion

I think we need to be having this conversation out in the open. AI can be guilty of suppressing creativity, but as fans, we can also suppress creativity by creating environments that feel exclusionary or even unsafe, where people feel reluctant to speak up, share or create.

I know this topic of AI is a raw and emotive one, and I’m sorry if anything I’ve written has come across as minimising the issue or anyone’s feelings, that wasn’t my intention.

For more on this whole topic please check out my FAQ master post.

[themurdochmemesteries]

that fucking AI-generated fic currently at the top of ent's list on ao3 taunts me. if you can't be bothered to write it, why should anyone read it? and yet it has 8 kudos so far. idiots...

[mephilesthedork]

Oh fuck yeah

[thatswhatsushesaid]

just like with machine learning, we can and should demystify “the cloud” without demonizing it in the process. cloud computing and cloud storage architecture are extremely useful tools; it’s how they get deployed by big tech companies that can be a problem.

[living-space-design]

[redlettermediathings]

[the-alternate-realities]

[neilsblog]

Deception Technology: The Cybersecurity Paradigm We Didn’t Know We Needed

In an age of ever-evolving cyber threats, traditional security measures are no longer sufficient to protect critical digital assets. To stay ahead of sophisticated attackers, organizations are turning to more innovative and proactive solutions. One such approach gaining momentum globally is Deception Technology — a cybersecurity strategy that shifts the paradigm from reactive defenses to…

[jcmarchi]

Unlock the other 99% of your data - now ready for AI

New Post has been published on https://thedigitalinsider.com/unlock-the-other-99-of-your-data-now-ready-for-ai/

Unlock the other 99% of your data - now ready for AI

For decades, companies of all sizes have recognized that the data available to them holds significant value, for improving user and customer experiences and for developing strategic plans based on empirical evidence.

As AI becomes increasingly accessible and practical for real-world business applications, the potential value of available data has grown exponentially. Successfully adopting AI requires significant effort in data collection, curation, and preprocessing. Moreover, important aspects such as data governance, privacy, anonymization, regulatory compliance, and security must be addressed carefully from the outset.

In a conversation with Henrique Lemes, Americas Data Platform Leader at IBM, we explored the challenges enterprises face in implementing practical AI in a range of use cases. We began by examining the nature of data itself, its various types, and its role in enabling effective AI-powered applications.

Henrique highlighted that referring to all enterprise information simply as ‘data’ understates its complexity. The modern enterprise navigates a fragmented landscape of diverse data types and inconsistent quality, particularly between structured and unstructured sources.

In simple terms, structured data refers to information that is organized in a standardized and easily searchable format, one that enables efficient processing and analysis by software systems.

Unstructured data is information that does not follow a predefined format nor organizational model, making it more complex to process and analyze. Unlike structured data, it includes diverse formats like emails, social media posts, videos, images, documents, and audio files. While it lacks the clear organization of structured data, unstructured data holds valuable insights that, when effectively managed through advanced analytics and AI, can drive innovation and inform strategic business decisions.

Henrique stated, “Currently, less than 1% of enterprise data is utilized by generative AI, and over 90% of that data is unstructured, which directly affects trust and quality”.

The element of trust in terms of data is an important one. Decision-makers in an organization need firm belief (trust) that the information at their fingertips is complete, reliable, and properly obtained. But there is evidence that states less than half of data available to businesses is used for AI, with unstructured data often going ignored or sidelined due to the complexity of processing it and examining it for compliance – especially at scale.

To open the way to better decisions that are based on a fuller set of empirical data, the trickle of easily consumed information needs to be turned into a firehose. Automated ingestion is the answer in this respect, Henrique said, but the governance rules and data policies still must be applied – to unstructured and structured data alike.

Henrique set out the three processes that let enterprises leverage the inherent value of their data. “Firstly, ingestion at scale. It’s important to automate this process. Second, curation and data governance. And the third [is when] you make this available for generative AI. We achieve over 40% of ROI over any conventional RAG use-case.”

IBM provides a unified strategy, rooted in a deep understanding of the enterprise’s AI journey, combined with advanced software solutions and domain expertise. This enables organizations to efficiently and securely transform both structured and unstructured data into AI-ready assets, all within the boundaries of existing governance and compliance frameworks.

“We bring together the people, processes, and tools. It’s not inherently simple, but we simplify it by aligning all the essential resources,” he said.

As businesses scale and transform, the diversity and volume of their data increase. To keep up, AI data ingestion process must be both scalable and flexible.

“[Companies] encounter difficulties when scaling because their AI solutions were initially built for specific tasks. When they attempt to broaden their scope, they often aren’t ready, the data pipelines grow more complex, and managing unstructured data becomes essential. This drives an increased demand for effective data governance,” he said.

IBM’s approach is to thoroughly understand each client’s AI journey, creating a clear roadmap to achieve ROI through effective AI implementation. “We prioritize data accuracy, whether structured or unstructured, along with data ingestion, lineage, governance, compliance with industry-specific regulations, and the necessary observability. These capabilities enable our clients to scale across multiple use cases and fully capitalize on the value of their data,” Henrique said.

Like anything worthwhile in technology implementation, it takes time to put the right processes in place, gravitate to the right tools, and have the necessary vision of how any data solution might need to evolve.

IBM offers enterprises a range of options and tooling to enable AI workloads in even the most regulated industries, at any scale. With international banks, finance houses, and global multinationals among its client roster, there are few substitutes for Big Blue in this context.

To find out more about enabling data pipelines for AI that drive business and offer fast, significant ROI, head over to this page.

[wirbelwindria]

I asked a colleague in a specialized department for her opinion/latest legal verdict to her knowledge in regard to a somewhat complicated matter and received an AI generated answer...

I asked YOU not the stupid LLM; if I wanted AI nonsense, I could've done that myself and I think that's wildly disrespectful tbh

The stupid fucking thing was WRONG because, of course, it was - as I said, it is a complicated matter after all; otherwise, I wouldn't have asked in the first place

But, being wrong is not enough in this instance... said colleague told me, "I asked the specifically designed AI for an answer, and the answer is great!", and I just know that a lot of my other colleagues would've either asked the AI themselves (getting wrong answers and probably wouldn't have checked properly) or would've relied on the specialized department being right when they called the WRONG FUCKING ANSWER a great one without checking the result provided - which, let me reiterate, is so, so, so wrong

I honestly don't know how anyone in their right mind could think that people would go through the trouble of actually checking the stupid LLM once they allowed using it. Of course, if you use it and don't check the answer, you will be the one held responsible, but I don't think that it should be an option in the first place

(also, I checked, despite my loathing of using anything LLM related, and it gets plenty of pretty cut and dry legal questions wrong, too)

[papercranesong]

i used to love Your writing but now i can't believe you have turned to AI :/ at least tag "created with ai" on your archive profile so people can know

Hi Anonymous, thanks for sharing how you feel, I appreciate your honesty. It sounds like you’ve got quite strong feelings about the use of AI and about my writing, and that must feel quite conflicting and uncomfortable for you, and I’m sorry for that.

Just for context - I’ve been using AI as a tool in writing for about a year and a half now, in the various ways that I outlined in my original post here.

I started off by making a podfic of Tripping the Light Fantastic (an Enterprise fic), using AI voices (you can listen to it here on AO3, along with my blurb about why I’d chosen to use an AI software over my own voice). In this case, I think the tag ‘created with AI’ fits, and I’m happy to use it.

However, your suggestion that I should tag my fanfic with a “created with AI” tag is a little more complex. I think this tag would imply to most people that I’ve replaced my own words with Chatgpt’s words (which isn’t the case) or that I entered a prompt and got chatGPT to spew out a story (also not the case). Just to be clear, that’s not what I use AI for. It’s a tool, a writing aid, but the words and style are my own.

You do raise a good point though – what should be tagged as ‘created with AI’? (I’m genuinely curious). I’ll use my own fics and posts as examples.

1. Using AI as a proof-reader

With my original post, I edited it lots, checked it for typos, ran it through my notes app spell checker, and then as a final check I ran it through chatGPT. It turned out I’d written “throws of exhaustion” rather than “throes of exhaustion.” My notes app didn’t flag it, because “throws” is a real word, but chatGPT caught that I probably meant “throes.” This is because LLMs are able to read each word within the context of its meaning, rather than just reading it as an individual word.

So I changed it. That was my only use of chatGPT in that original post. So would you say that this needs a ‘created with AI’ tag?

2. Using AI for background research

So before I wrote Beautiful Wreck, I was watching Silent Enemy again, and thinking about Malcolm and his pineapple allergy. In the episode he’s listed as having quite a lot of allergies, at least one of which requires regular injections, implying it’s quite severe.

My background includes some immunology and pharmacology, but I’m a bit rusty, so I asked chatGPT to create an immunological profile based on the allergies listed. I didn’t use that information directly when I came to write Beautiful Wreck, but it did help shape my understanding of what it must have been like for Malcolm growing up, and informed the final chapter, when Hoshi confronts Malcolm in the mess hall.

So do I tag that story as ‘created with AI’?

3. Using AI as a writing counsellor

One of my Enterprise fics is called ‘everything’s all right now’ and it deals with some quite dark themes. I wrote it, hesitated, posted it anonymously on AO3, then deleted it a few hours later. For months I kept second-guessing myself. I wanted to publish it but I didn’t have the courage. I was also frustrated with myself because I thought that this fic was evidence of how bad a writer I felt I was – in that I’d edited it and edited it so many times, and I was annoyed that I couldn’t just ‘get it right’ the first time like other writers seemed to be able to do.

So I talked my concerns through with chatGPT (I’ve never done a writing class or studied creative writing, and there’s a lot that I don’t know about the writing process). It reassured me that editing was part of writing, not something you do after, and it gave me a nice gardening analogy to help me understand.

I plucked up the courage to post the fic, and it received a really nice response - it seemed to strike a chord with people, and I even had one author gift me their own story inspired by that one and based on my style, which was a big honour.

So do I tag that fic as ‘created with AI’?

There’s lots of other examples I could give, but you can hopefully see my point. I think the tag ‘created with AI’ is just too broad, mainly because

People already have their own opinions about AI that they will then imbue the tag with.

The tag doesn’t capture the complexities and nuances of how AI can be used as a tool rather than to simply generate words on demand.

If tags don't reflect how AI is used, they risk distorting perception more than they inform.

So, Anonymous, coming back to your suggestion, that’s why I don’t feel comfortable tagging my last 8 or so fics with that tag. (I have already linked to my original post in my AO3 profile, for transparency).

I do think it’s important to have this type of discussion though. The genie isn’t going back in the bottle any time soon, and I think it’s good to have respectful and nuanced discussions about the use of AI, especially when it comes to writing fanfic.

(Also, just to say, I’m not adverse to tagging when it’s appropriate, but I do think we as a community need to come up with clearer terms or categories that reflect how AI is used).

Thanks again for sharing your thoughts. Feel free to push back on any of these points 🖖