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Automation, Personalization, and Data Management Artificial Intelligence (AI) is driving a profound transformation across industries worldwide, and the funds industry is undergoing a significant evolution as a result. With its ability to automate tasks, provide personalized or client-specific services, and enhance data management to almost unimaginable levels, AI is reshaping the way funds operate. This is bringing with it improved efficiency, lower costs, and the need for less staff - not to mention increased client satisfaction and improved decision-making capabilities. Streamlining Tasks And Transforming The Workforce In the funds industry, AI is revolutionizing operations by automating a wide range of tasks that were once time-consuming and prone to errors. Tasks such as data entry, reconciliation , and reporting are being streamlined through AI-powered systems, freeing up human administrators to focus on more complex and strategic responsibilities. Even activities that were once considered too complex, such as dispute resolution or investigation, or those that involved decision-making , are now firmly in the sights of AI. This automation not only reduces costs but also minimizes errors and enhances operational efficiency. With AI's scalability potential, funds can handle larger volumes and adapt to changing demands with ease. The Era of Clientization AI is ushering in a new era of personalized client service , often referred to as clientization, in the funds industry. The automation and scale described above will give fund administrators and outsource specialists the ability to take on smaller or more demanding clients with little or no concern about the cost. Through AI-powered chatbots, virtual assistants, and data analytics, funds can provide tailored services and experiences to individual clients. AI enables 24/7 support, instant query resolution, and customized reporting. By leveraging AI technologies , funds can gain a deeper understanding of client preferences, risk tolerances, and financial goals, allowing for personalized investment advice and product recommendations. The result is an enhanced client experience, increased engagement, and stronger client relationships. Taking Data Management To The Next Level Data management lies at the heart of the funds industry, and AI is revolutionizing this aspect in multiple ways. Through advanced data analytics, AI empowers funds to make informed investment decisions by providing real-time insights, identifying patterns , and assessing risks. Algorithmic trading, driven by AI, leverages vast amounts of data and market indicators to execute trades with speed, accuracy, and adaptability to changing market conditions. AI also enhances reporting capabilities, generating comprehensive reports on fund performance, asset allocation, and risk exposure. Additionally, AI plays a vital role in fraud detection by monitoring transactions, identifying anomalies, and mitigating risks to protect fund integrity. The Future of AI And The Funds Industry The funds industry is undergoing a profound transformation, fueled in no small part by AI. Automation streamlines tasks, reduces costs and allows for the redeployment of human resources to more strategic activities. Personalized client service powered by AI deepens client relationships, fosters engagement, and drives client satisfaction. AI-driven data management empowers funds with real-time insights, improved decision-making, and enhanced fraud detection capabilities. As the funds industry embraces AI, it is crucial to address challenges such as data privacy, algorithmic bias, and regulatory compliance to ensure the ethical and responsible use of AI technologies. By harnessing the potential of AI, funds can unlock new opportunities, gain a competitive edge, and navigate the ever-evolving landscape of the financial services industry. Artificial Intelligence And Fund Industry Professionals The rapid adoption of Artificial Intelligence (AI) in the funds industry brings forth a range of implications for professionals working in the field. It is important for industry professionals to understand and adapt to these changes and it is only natural to begin to worry about job security. Routine and repetitive tasks, even those requiring a moderate level of skill are likely to disappear. Everyone from research analysts to risk professionals, and accountants to actuaries, will see their work change drastically. Not everyone will lose their job, of course, but job losses are beginning to feel inevitable. As Rob Thomas, Senior Vice President at IBM famously said , “AI is not going to replace managers, but managers who use AI will replace the managers who do not." So what can you do about it? Developing expertise in AI technologies, data analysis, and machine learning is the obvious first step. That does not mean you need to learn to code or build LLMs however. Upskilling can involve learning how to effectively utilize AI tools and algorithms, interpret data insights, and make decisions based on AI-driven analytics. Professionals who adapt and acquire new skills will be well-positioned to leverage AI technology, navigate the changing industry landscape, and seize emerging career opportunities. Position yourself in areas such as strategic decision-making, relationship management, and client engagement to ensure longevity. Artificial Intelligence will be to the funds industry what the sewing machine was to seamstresses in the 1850s. Funds industry professionals who truly understand what drives their business can prosper over the coming decade, but those who blindly resist will most likely fall by the wayside. It is inevitable that work will be automated, so rather than resist it, embrace it. Let AI take the mundane tasks out of your day-to-day and focus on truly satisfying your clients' needs.

Have you ever heard the term 'Father of AI' or 'Godfather of AI' and wondered who they are talking about? The journey of AI is not a story of a single champion; rather, it is a relay race of intellects, each handing off the baton of innovation. The last century has seen a tapestry of unique thinkers whose collective genius has shaped today's AI marvels - from ChatGPT to the complex algorithms suggesting your next online purchase. In this article, we spotlight eight remarkable innovators; each hailed at some point as the 'father' or 'godfather' of AI. We'll explore their groundbreaking contributions, profound impact, and some interesting facets of their lives. As you traverse through this AI hall of fame, you may decide who, in your opinion, deserves the crown of 'father of AI. The Father of AI - 8 Artificial Intelligence Pioneers 1 - Alan Turing (1912-1954) Background Born in London, England, Alan Turing displayed a strong aptitude for science and mathematics from an early age. He attended King's College, University of Cambridge, and Princeton University. Turing's work during World War II at Bletchley Park, where he developed the Bombe machine to decrypt the German Enigma code, is celebrated as a significant contribution to winning the war. Contribution to AI Alan Turing's work laid the foundation for the field of artificial intelligence in many ways: Turing Machine : Turing proposed the concept of a "universal machine" capable of computing any computable sequence. Today, we know this theoretical device as the Turing Machine, and it has formed the backbone of modern computing. Universal Machine : His concept of a universal machine— a device capable of mimicking the logic of any machine given the correct inputs and instructions—has profoundly influenced the design and functionality of today's computers. Turing Test : Turing also conceived a test to measure a machine's ability to exhibit intelligent behavior equivalent to or indistinguishable from a human's. This Turing Test is still a hot topic in the philosophy of artificial intelligence, sparking ongoing debates about the nature and potential of AI. "We can only see a short distance ahead, but we can see plenty there that needs to be done." - Alan Turing Impact The theories and ideas put forth by Turing have been instrumental in shaping not just the field of artificial intelligence but computer science as a whole. His invention of the Turing Machine and the Universal Machine revolutionized our comprehension of computational processes. They were essential building blocks in creating the digital computers we use today. Furthermore, the Turing Test, conceived by him, still plays a pivotal role in driving critical conversations around the possibilities of AI and machine intelligence. His invaluable work has received due recognition in the form of the "ACM A.M. Turing Award" - the highest honor in computer science, named to commemorate his legacy. Interesting Fact In addition to his groundbreaking work in computation, Turing had diverse interests and talents. Notably, he was a world-class marathon runner and nearly qualified for the British Olympic team in 1948. 2 - Allen Newell (1927-1992) Background Born in San Francisco, California, Allen Newell was a pioneering computer scientist known for his extensive work in artificial intelligence and cognitive psychology. He earned his doctorate in Industrial Administration at Carnegie Institute of Technology (now Carnegie Mellon University), where he later held various academic positions and conducted his seminal research. Contribution to AI Allen Newell's influential work laid crucial groundwork for the evolution of artificial intelligence: Logic Theorist : Together with Herbert A. Simon, Newell developed the Logic Theorist, often recognized as the first artificial intelligence program which could mimic the problem-solving skills of a human being. General Problem Solver (GPS) : Newell and Simon also co-developed the General Problem Solver, a computer program designed to imitate human thought processes. The GPS was groundbreaking because of its ability to address any problem that could be described in the form of a well-structured task. Unified Theories of Cognition : In his later years, Newell proposed that human cognitive behavior could be understood as a system governed by a few basic principles. This significant work attempted to bring all cognitive theories under a unified framework. "Each new machine that is built is an experiment. Actually constructing the machine poses a question to nature; and we listen for the answer by observing the machine in operation and analyzing it by all analytical and measurement means available." - Allen Newell Impact Newell's pioneering work has profoundly impacted the study and development of artificial intelligence and cognitive psychology. His development of the Logic Theorist and General Problem Solver marked significant strides in AI's early days, exploring the potential of machines to mimic human thought processes. Newell's unified theories of cognition provided a novel lens through which to study human understanding, influencing subsequent research in cognitive science. He has been recognized with the prestigious ACM A.M. Turing Award for his contributions to AI and psychology. Interesting Fact Allen Newell, known for his work on complex systems, was an enthusiastic sailor and would often draw parallels between the complexity of sailing and his work. 3 - Marvin Minsky (1927-2016) Background Born in New York City, Marvin Minsky was a pioneer in the field of artificial intelligence. He studied at Harvard University and later received his Ph.D. in mathematics from Princeton University. Minsky co-founded the Massachusetts Institute of Technology's Media Lab, where he made his most notable contributions to AI and cognitive psychology. Contribution to AI Minsky's seminal work has been instrumental in shaping the field of artificial intelligence: Artificial Neural Networks : Minsky was one of the earliest researchers of artificial neural networks. His book, "Perceptrons," co-authored with Seymour Papert, presented a comprehensive analysis of artificial neural networks. His exploration and scrutiny of their limitations were pivotal in developing multi-layer networks, now a fundamental component of modern machine learning. Frames : Minsky introduced the concept of "frames" as data structures for representing stereotypical situations in AI. This concept underpins many natural language processing, robotics, and computer vision systems, facilitating more efficient information processing. Society of Mind : In his book, "Society of Mind", Minsky proposed a model for how human intelligence might be produced by the interaction of non-intelligent parts. This theory continues to inspire AI research, contributing to developing models that attempt to mimic human cognition for problem-solving. "You don't understand anything until you learn it more than one way." - Marvin Minsky Impact Minsky's theoretical contributions significantly impacted the understanding and development of artificial intelligence. His early work on artificial neural networks laid the groundwork for much of today's machine-learning field. The concept of frames has influenced natural language processing, robotics, and computer vision. Minsky's book, "Society of Mind," continues to inspire researchers interested in understanding intelligence's complex nature. Minsky was honored with the ACM A.M. Turing Award, recognizing his pioneering work in AI. Interesting Fact Aside from his scientific pursuits, Minsky was a talented pianist and invented several musical instruments, including a pioneering synthesizer called the "Triadex Muse". 4 - John McCarthy (1927-2011) Background John McCarthy, an American computer scientist, was born in Boston, Massachusetts. He studied mathematics at the California Institute of Technology and Princeton University, where he earned his Ph.D. McCarthy made significant contributions to the field of artificial intelligence during his time at Stanford University, where he founded the Stanford AI Lab. Contribution to AI McCarthy's endeavors have been instrumental in AI's evolution: LISP Programming Language : McCarthy developed LISP, the second-oldest high-level programming language that's still in use today. LISP is widely used in AI for its unique features, such as tree data structures, dynamic typing, and the ability to treat code as data. Coined the term 'Artificial Intelligence' : McCarthy is credited for first using the term 'artificial intelligence' in 1956, and he was one of the organizers of the Dartmouth Conference, which was crucial in establishing AI as a field of research. Time-Sharing Systems : He played a crucial role in developing time-sharing systems, which allow multiple users to use a computer simultaneously. "The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it." - John McCarthy Impact McCarthy didn't just contribute to the field of AI - he helped define it. The creation of the LISP programming language became a cornerstone for AI research. At the same time, his coining of the term 'artificial intelligence' and subsequent organization of the Dartmouth Conference gave birth to AI as a distinct field of study. Moreover, McCarthy's work on time-sharing systems charted a new course for the progression of computer technology. His innovations were aptly recognized when he received the prestigious ACM A.M. Turing Award. Interesting Fact McCarthy had a strong interest in space travel and exploration. Upon retiring from Stanford, he wrote and occasionally lectured on the feasibility of human interstellar travel. 5 - Geoffrey Hinton (1947 - Present) Background Geoffrey Hinton, an English Canadian cognitive psychologist and computer scientist, was born in Wimbledon, London. He received his education from King's College, Cambridge, and the University of Edinburgh, where he earned his Ph.D. Hinton has made significant strides in the field of artificial intelligence, specifically in machine learning and neural networks, during his tenure at the University of Toronto and Google. Contribution to AI Geoffrey Hinton's pivotal work in artificial intelligence centers on: Backpropagation : Alongside his colleagues, Hinton developed a fast, practical method to train multi-layer neural networks, widely known as backpropagation. Today, backpropagation is the standard algorithm for training a wide array of machine learning models, making tasks such as image and speech recognition more efficient and effective. Deep Learning : Hinton has been instrumental in developing and advancing deep learning, a subset of machine learning involving neural networks with several layers. His pioneering work has driven many recent advancements in AI, enabling machines to process and learn from large quantities of data in ways that mimic human cognitive processes. Boltzmann Machines : Hinton co-invented Boltzmann machines, a type of stochastic recurrent neural network. These are often used in optimization problems and have influenced the design of more sophisticated machine learning models, contributing to areas such as recommender systems and image recognition. "Early AI was mainly based on logic. You're trying to make computers that reason like people. The second route is from biology: You're trying to make computers that can perceive and act and adapt like animals." - Geoffrey Hinton Impact Hinton's work has revolutionized the field of artificial intelligence, establishing neural networks as a cornerstone of contemporary AI research. His practical development of backpropagation transformed how we train neural networks, unlocking new possibilities in fields ranging from natural language processing to autonomous vehicles. Hinton's foundational contributions to deep learning have powered many advancements, from speech recognition systems to medical diagnostics tools, truly reshaping our interaction with technology. His trailblazing efforts were internationally acknowledged when he was awarded the prestigious ACM A.M. Turing Award, an honor he shared with his esteemed colleagues Yoshua Bengio and Yann LeCun. Interesting Fact Hinton hasn't sat down since 2005 due to chronic back problems. Despite this, he's continued his influential work in AI, often while standing or lying down. This adjustment extends even to his meals, which he typically takes while kneeling on a cushion. 6 - Yann LeCun (1960 - Present ) Background Born in Paris, France, Yann LeCun's fascination with AI emerged during his electrical engineering and computer science studies. His trailblazing career has spanned both academia and industry, including impactful tenures at the prestigious Bell Labs and the University of Toronto. Known for his innovative thinking and technical leadership, LeCun continues to make waves as the Chief AI Scientist at Facebook, shaping the social media giant's approach to AI and machine learning. Contribution to AI Yann LeCun has significantly contributed to the field of artificial intelligence in several ways: Convolutional Neural Networks (CNNs) : LeCun developed the Convolutional Neural Network (CNN), a class of deep, feed-forward artificial neural networks most commonly applied to analyzing visual imagery. Today, CNNs are fundamental in the realm of image and video processing. Backpropagation : Along with Geoffrey Hinton and others, LeCun popularized and developed the backpropagation algorithm. Now a cornerstone of neural network training, backpropagation is pivotal to many contemporary AI applications, from voice recognition and recommendation systems to autonomous vehicles. LeNet-5 : LeCun introduced LeNet-5, a pioneering 7-level convolutional network for recognizing handwritten and machine-printed characters. This technology is now ubiquitous, underpinning modern optical character recognition and image classification software. "Our intelligence is what makes us human, and AI is an extension of that quality. Artificial intelligence is extending what we can do with our abilities. In this way, it’s letting us become more human" - Yann LeCun Impact LeCun's work on Convolutional Neural Networks and backpropagation has proven foundational to many modern AI systems, particularly in the field of image recognition. His LeNet-5 model spurred a revolution in digital document processing and set a new precedent for machine learning architecture. His significant contributions to the field were recognized when he shared the ACM A.M. Turing Award with Geoffrey Hinton and Yoshua Bengio. Interesting Fact LeCun likes to build and fly model airplanes. He has a blog and a picture gallery where he talks about his miniature flying contraptions, techno toys, and music(there is even a few MP3 samples of his music on the blog). 7 - Yoshua Bengio (1964 - Present ) Background Born in Paris, France, Yoshua Bengio grew up in a household that nurtured his intellectual curiosity. His academic journey led him to Montreal, Canada, where he pursued a Ph.D. in Computer Science at McGill University. Bengio is a renowned academic and researcher, with his career primarily based at the University of Montreal, where he heads the Montreal Institute for Learning Algorithms. Contribution to AI Yoshua Bengio's seminal contributions to artificial intelligence include the following: Deep Learning : With his colleagues Geoffrey Hinton and Yann LeCun, Bengio has developed and advanced deep learning, transforming how machines interpret and learn from complex datasets. Today, this subfield of machine learning underpins numerous applications, from speech recognition to image analysis. Probabilistic Models : Bengio's work on probabilistic models, which quantify the uncertainty of predictions, has significantly enhanced machine learning methodologies, leading to more effective and nuanced AI systems. Today, these probabilistic models play a critical role in diverse fields such as finance, where they are used for risk assessment and price prediction, healthcare for diagnostic and prognostic purposes, and even in autonomous driving, where they help make reliable decisions based on sensor data. "The value of AI in the workplace goes beyond automation. It is about augmenting human intelligence, enabling workers to make better decisions, and fostering a culture of innovation and creative problem-solving." - Yoshua Bengio Impact Bengio's pioneering work has triggered a significant paradigm shift in AI and machine learning, with deep learning techniques transforming areas such as natural language processing, computer vision, and speech recognition. Bengio's research on probabilistic models and neural network architectures has introduced innovative approaches to machine learning, inspiring numerous researchers and revolutionizing AI-powered technologies used today. His profound influence earned him, along with Geoffrey Hinton and Yann LeCun, the prestigious ACM A.M. Turing Award, cementing his position as a trailblazer in AI. Interesting Fact Bengio's brother, Samy Bengio, is the Senior Director of AI and Machine Learning Research at Apple and, before Apple, spent many years as a scientist at Google, where he was one of the early leaders of the Google Brain research team. 8 - Andrew Ng (1976 - Present) Background Born in the United Kingdom, Ng is one of the most influential figures in modern artificial intelligence. Ng studied Computer Science at Carnegie Mellon University for his bachelor's degree. He later obtained his master's degree from the Massachusetts Institute of Technology (MIT). He completed his Ph.D. in Computer Science at the University of California, Berkeley. Ng has held prominent positions at Stanford University, Google, and Baidu. Contribution to AI Andrew Ng's work continues to shape the landscape of artificial intelligence: Deep Learning : Ng has significantly contributed to deep learning, a subset of machine learning involving artificial neural networks with multiple layers. His leadership of the Google Brain project, which developed large-scale artificial neural networks, resulted in a notable milestone: a network that taught itself to recognize cats in videos. Stanford Artificial Intelligence Laboratory (SAIL) : As the director of SAIL, Ng broadened the lab's research focus to include deep learning and its applications, thus advancing the broader use of AI technologies. Massive Open Online Courses (MOOCs) : Ng co-founded Coursera, a popular online learning platform. He also developed one of the first massive open online courses (MOOCs) on machine learning, introducing AI and machine learning to millions of students worldwide. "We're making this analogy that AI is the new electricity. Electricity transformed industries: agriculture, transportation, communication, manufacturing. I think AI is poised to do the same." - Andrew Ng Impact Ng's pioneering work in AI and machine learning has significantly influenced these fields. His work on Google Brain showcased the vast potential of deep learning. At the same time, his commitment to open-access education has democratized knowledge and catalyzed the growth of AI talents worldwide. Under Ng's direction, Stanford's AI Lab has become a hub for innovative deep learning and AI research. His role at Baidu, China's largest search engine, has also enabled significant advances in practical AI applications. His contributions have brought AI technologies closer to everyday use, affecting industries as varied as healthcare, transportation, and entertainment. Interesting Fact Ng deeply appreciates the arts and the creativity of artistic pieces and performances. His mother ran arts festivals when he was growing up and frequently brought Ng and his brother to musical concerts, opera, and the theater, which fueled his lifelong interest. The Final Word It's clear that each of these eight figures, in their unique way, has left an indelible mark on the landscape of artificial intelligence. Their revolutionary ideas and inventions have fundamentally reshaped our world, enabling the AI-driven technologies that amaze us today. So, who is the real 'father' or 'godfather' of AI? As we've seen, it's not about crowning a single person but about acknowledging a relay of remarkable minds. This succession of intellects, these pioneers pushing boundaries and daring to dream, has birthed the AI age. As we wrap up, remember this is just the beginning. The field of AI continues to grow, evolve, and surprise us. So, stay curious, and stay engaged.

Is there still an untouched corner in the vast universe of AI business opportunities ? The rapidly trending term "AI Automation Agency" (AAA) suggests so. AI Automation Agencies have been trending recently on YouTube. They are sold as a new business model, an excellent way for beginners who do not come from a software development or programming background to break into the AI industry in a cost-effective manner. There is a lot of hype being generated in YouTube videos and the comments section of the videos about AAAs. But is the hype warranted? Is this really a brand-new business model? Is an AAA a blue ocean business operating in a novel marketplace, untainted by competition? Read on as we dissect this trend and examine if it holds the promise of a fresh, competition-free marketplace. What is an AI Automation Agency An AI Automation Agency is a business that helps small and medium-sized companies incorporate AI into their processes and workflows to increase productivity, profit and performance . You help employees and business owners get more done with fewer errors. You also help them make better-informed business decisions. An AAA will automate repetitive tasks, optimize processes to make them more efficient by increasing speed and accuracy, and provide insights using company data that was previously impossible to utilize. By removing the need for employees to do the tedious, mundane tasks that we all hate, people are freed up to do more interesting and valuable work. What do AAAs offer and actually do? The scope of AAA services is broad, given the diverse business areas that AI can impact. The critical point is that AAAs will ultimately reduce manual tasks & touchpoints, eradicate process bottlenecks, increase task accuracy and generally remove any pain points in a company's operations. Let's look at the three main areas of focus for AAAs: 1) Automate Tasks and Processes Tasks requiring humans to manually input, extract, transfer, or reconcile data can all be automated. Entering expense claims into an expense system, retrieving file attachments from emails and copying data from one system to another can be done by AI quicker and more accurately than a person could. 2) Interactive Assistance Chatbots and automated self-help tools that people interact with using natural language fall into this category. Many customer support agents answer repetitive, formulaic and easy-to-answer questions from customers that chatbots or self-support AI tools could answer instead. 3) Intelligent Insights & Advice Data can be collected and fed into a machine learning algorithm to produce business insights that were impossible to create without AI. Examples include product recommendations or detecting anomalies such as data entry errors. Algorithms can sift through data at a scale impossible for humans to match. These are the broad areas in which an AAA can provide services . Ultimately, the services and solutions a specific AAA will offer will only be limited by the owner's ability to identify business pain points and their ability to resolve these pain points. Are AI Automation Agencies new? The term "AI Automation Agency" (AAA) was only popularized on YouTube in mid-2023, so usage of the term itself is new. The business model behind it though, helping other businesses incorporate AI, is not totally new. Large companies have been partnering with those producing AI models, such as OpenAI & IBM, or consulting in AI, such as McKinsey or the big four accounting firms, for several years. However, the idea of individuals setting up their own companies, or agencies, to target smaller businesses is a much newer trend . The release of ChatGPT in November 2022 brought the attention of a broader audience to the existence of AI tools. Since then, we have seen a noticeable increase in businesses using AI tools and software. This increase in the use of AI tools is the genesis of what have now become AAAs. Is an AAA similar to other "agency" models like social media marketing agencies (SMMAs) No. The connection between AAAs and other agency business models, like marketing agencies and SMMAs , comes from the world of online business/entrepreneurship. Many YouTubers who posted videos about business/entrepreneurship and related areas - dropshipping, e-commerce, NFTs - began pivoting to posting videos about AAAs when it started trending. Their only experience was with the word "agency". They were relatively new to "AI" and business process "automation". Most talk about AI automation for online marketing, lead generation, and social media advertising, as this is where their expertise is. As such, they are running SMMAs that use AI tools rather than an AAA focusing on automating company processes and workflows that extend far beyond the online marketing arena. Is there a market for AAAs and their services? Yes. While AAA may be a buzzword for many YouTubers who have limited AI or enterprise knowledge, the business case for legitimate agencies is extremely strong. As mentioned, large firms are partnering with AI companies and experts to roll out AI solutions in their firms. As a result, many medium and small business owners now understand the potential of AI, but they need more expertise to implement it. This need is where AAAs will step in and offer their services. Unlike previous technology trends, such as NFTs and the Metaverse, there is widespread acceptance that AI is a game changer. This acceptance can be quantified by the amount of cash being pumped by institutional investors into AI companies, the demand for employees with AI skills and the volume of media articles on the topic. Not since the advent of the iPhone in 2007 has a technology innovation garnered such attention . Should I start an AAA? Should you take the plunge and start your own AAA? There's no simple answer, but our post , "Should I Start an AAA?" delves into the complexities and potential of this new business model. Be sure to read that to find out if starting an AAA is for you!

So far this year 122,900 layoffs have been announced that are linked to Artificial intelligence with that number expected to mushroom into the millions over the coming decade. Sometimes it feels like every news article about Artificial Intelligence is warning us of the impending job-loss apocalypse and the dangers of AI. Although there are no doubt some risks associated with the rapid rise of these new technologies, it can pay to take stock of what is actually happening in the economy. Here we track layoff announcements related to AI and you will clearly notice some common themes: automation and cost-cutting are the primary drivers of job losses due to AI in 2023. McKinsey - 2,000 jobs Announced February 2023 McKinsey & Company is one of the top, if not the top name, in management consultancy, but they are not immune from rising cost pressures. Their announcement comes as part of Project Magnolia, a program to preserve the compensation of the firm's partners, as reported by Reuters. Although AI was not directly referenced, the language included the usual related terms. The losses will center on support staff, an ability to scale operations, and "redesigning the way our non-client-serving teams operate". Meta - 10,000 jobs (Facebook) Announced March 2023 As part of a wider post-pandemic trend among the tech darlings, Meta engaged in significant layoffs in 2023 and started things off with an announcement in March that 10,000 roles would go. Again, there was no explicit mention of AI in the announcement but the company has talked about flattening its management structure and increasing automation. Accenture - 19,000 jobs Announced March 2023 The mammoth Irish consultancy firm plans to trim its staffing numbers by 2.5% or roughly 19,000 over the next 18 months. More than half the layoffs will be in back-office functions with industry experts expecting, once again, that automation will be a primary driver. In their report to The SEC, Accenture stated its goal was to "transform our non-billable corporate functions to reduce costs.” Reading between the lines, this transformation could include a lot of AI software. Amazon - 27,000 jobs Announced March 2023 Another of the tech giants went on a cost-cutting drive in 2023 when Amazon announced a plan to cut 27,000 jobs, predominantly in advertising and related fields. Similar to Meta they did not actually attribute any of the job cuts to AI - but reading between the lines of automation and cost-cutting it seems a safe bet that AI played a part. Vodafone - 11,000 jobs Announced May 2023 The European telecoms giant will replace 10% of its workforce through automation as part of aggressive cost-cutting measures. Although they did not explicitly reference Artificial Intelligence it's widely believed to be a key driver. Vodafone has deployed an AI customer care agent, TOBi, in an effort to become “as lean as possible” freeing up hundreds of millions of euros for marketing and customer experience. IBM - 7,800 jobs Announced May 2023 The technology giant announced in May 2023 that up to 30% of its workforce could be replaced by AI with immediate plans to pause all hiring. CEO Arvind Krishna said the company plans to replace 7,800 back-office jobs with AI over the next few years, starting with HR. BT - 10,000 jobs Announced May 2023 The British telecoms behemoth announced that 55,000 jobs would be cut by the end of the decade due to cost-cutting and automation. The announcement heavily focused on AI, with CEO Philip Jansen going as far as saying, “We will be a beneficiary of AI unequivocally because we are a volume business.” That being said, only 10,000 of the 55K job losses were directly attributed to AI. Once again the focus of AI job losses would be on customer support and back-office functions. Unsurprisingly, Jansen is a big fan of BT's customer service chatbots. “Does everyone know it’s a chatbot? Of course they don’t.” Miscellaneous - 3,900 jobs Announced May 2023 The highly regarded Challenger Report recorded almost 4,000 job losses in May of 2023. Although the report does not name specific companies it does mention record-breaking layoffs in the media industry with news organizations particularly impacted. This is the first time Artificial Intelligence has been noted as a cause of job losses in this report. Bild - 200 jobs Announced June 2023 Written media is widely considered to be one of the industries most vulnerable to artificial intelligence, and generative AI in particular. With the explosion of tools like ChatGPT the German publisher Bild announced 200 redundancies along with a promise to make more cuts on the back of “the opportunities of artificial intelligence”. Staff of the newspaper were told, “unfortunately be parting ways with colleagues who have tasks that in the digital world are performed by AI and/or automated processes”. US Advertisers - 32,000 jobs (via Forrester) Announced June 2023 Up to 7.5% of jobs in American advertising agencies will disappear by 2030 according to leading research firm Forrester . This equates to 32,000 redundancies over the next seven years. Once again, automation is a big driver here with "problem-solving" jobs expected to remain untouched or even thrive as AI improves our ability to gather and analyze data. The Global Economy - 300 million jobs Predicted by Goldman Sachs March 2023 Leading investment bank GS expects that artificial intelligence and generative AI in particular could cost The US and Europe up to 300 million jobs over the coming years. They are not specific on timelines but the headline number of 300,000,000 jobs is still eye-catching. This is a staggering number and admittedly their published report is light on details and heavy on assumptions but even if they are remotely right this is a worry. Hollywood - an unknown number of jobs Part of the ongoing writers' strike. The Writers Guild of America (WGA) which represents 11,500 Hollywood writers went on strike on May 2, 2023, and at the time of writing is still ongoing. One of the key concerns is the potential use of artificial intelligence to replace writers. This feels like the first step in a protracted battle for jobs in creative writing roles and we will of course monitor the situation closely. Wimbledon Tennis - an unknown number of jobs No-job AI services from The All England Club & IBM The live nature of sports broadcasting has traditionally insulated it from media cutbacks but this year The All England Club is testing a new AI-powered commentary service using IBM's watsonx software. This will give text commentary and captions in the “unique language of tennis” during the Wimbledon tennis tournament. There is no direct link between this particular product and job losses but the direction within the industry is abundantly clear. The Impact of AI on Jobs Losses in 2023 The impact of AI on the jobs market in 2023 has been almost entirely centered on automation and cost-cutting. This should be no surprise. The cost of living crisis is driving wage inflation, which along with rising interest rates is putting companies under significant pressure to slash expenses. The aggressive adoption of AI tools that reduce headcount will be the early, and dare I say obvious, use case of AI in business. However, some more nimble organizations will look at how AI can offer new services, and not simply automate old ones. We continuously track press releases and announcements so follow us on Twitter or subscribe to our newsletter to stay up to date on everything AI.

Should I Start an AI Automation Agency? Does the idea of starting an AI Automation Agency (AAA) excite you? If the answer is yes, then an AAA could be your next big entrepreneurial leap. But before you make the jump, let's explore what starting an AAA really involves and how it differs from the traditional 'agency' business model blueprint. When we hear 'agency', images of digital or social media marketing often surface, along with other online businesses like dropshipping and Amazon FBA. However, when you start an AI Automation Agency, you're stepping into a more complex space, where complexity amplifies as your agency matures. AAA vs Dropshipping: Navigating the Learning Curve To show this complexity difference, let's compare a dropshipping business and an AAA, both six months into operation. After six months of successful dropshipping, your operations are down pat: reliable suppliers, profitable products, and established marketing channels . Sure, there's room for innovation and expansion, but the core mechanics of your business remain relatively static. Conversely, in an AAA, you're still scaling a steep learning curve six months in. You've deployed a chatbot, rolled it out across several customers, and referrals are flowing in. It's a promising start, but now your clients are inquiring, " What else can you automate with AI? " Addressing this request, you have two choices: Continue only offering your existing chatbot. Venture into unexplored terrain, conceiving and implementing new AI solutions. Beyond the Novice Attraction: Evolving from Chatbots One of the reasons aspiring entrepreneurs are drawn to start an AI Automation Agency is the initial simplicity of creating a basic chatbot . As of July 2023, few people can deploy robust, ready-for-market chatbots for SMEs. However, the industry's dynamics are changing rapidly. We're already witnessing an influx of new entrants offering chatbot services, evident from the number of YouTube tutorials on "how to build your own AI chatbot". So, how can you safeguard your AAA amidst this increasing competition? The answer lies in evolution. Identify fresh pain points, bottlenecks, or business challenges and develop AI-powered solutions to address them. You must surpass the initial chatbot phase and tap into the vast potential of AI to address business pain points and challenges. Unlocking AAA Potential: Capitalizing on Complexity Many who start an AI Automation Agency will achieve initial success with their chatbots, which is a major positive. But as AI services become more commonplace, demand for these standard chatbots will decline. The forward-thinking AAA owners, however, will adapt and innovate, turning complexity into their competitive advantage. The potential profitability of an AAA is directly linked to the complexity of the solutions you offer. If you only provide a basic chatbot — the same model every other AAA owner has learned to create on YouTube — your profits will plateau. But if you engage with business owners, decode their challenges, and devise unique AI solutions, you're setting the stage for success in a high-demand, low-competition environment. This environment will get us close to the holy grail of business, a blue ocean. Embracing Uncertainty: Beyond Structured Learning If you need a course or a detailed guide to start an AAA, the answer to "Should I Start an AI Automation Agency?" will be no. Successful AAAs in 12 months will not only be offering chatbots they learned to build on YouTube to clients. They may have started with that, but they will have quickly expanded into more complex and, as a result, more profitable areas where there is no guide or videos to follow. AI tools and technology, especially their application to SMEs, are new. As such, there is little material to refer to in terms of courses, books and guidance. Those who rely on courses or copy-and-paste solutions will offer the same limited number of solutions as others who use the same material. Their AAA will fail. The successful AAAs will transform business tasks , processes and operations using novel AI approaches and will get handsomely rewarded. In Conclusion When you start an AI Automation Agency, you're signing up for a journey very different from running an SMMA dropshipping or Amazon FBA business, where a course gets you started and surviving the first six months is the major hurdle. AAAs present a continuous learning journey — there's no definitive course to ensure long-term success, and that's what makes it a compelling opportunity ! So, if you're driven, ready to delve into the intricacies of AI, eager to innovate and not deterred by complexity, the potential for you and your AAA is potentially limitless!

I wrote in two previous posts about what AI Automation Agencies (AAA) are and if you should start one. As AAA content has grown exponentially on YouTube and social media in the last few weeks, many are asking a valid question off the back of this - "Are AI Automation Agencies a Scam" or potentially a fad? I will answer this question by discussing three main points: 1) The business of what a genuine AAA should do is not a scam. 2) The spike in AAA content on YouTube and other social media is fad-like. 3) The accuracy of this AAA content and the AI knowledge of its creators needs questioning What a Genuine AAA Does The attention AI and its potential for automating business tasks & processes has garnered since the launch of ChatGPT in November 2022 has been immense. But does the data back up this attention being warranted? Are the potential benefits and efficiencies businesses can gain from adapting AI legitimate? The infographic below is from the investment research firm MSCI. It shows the percentage share of tasks/processes in different US industries that AI could automate . A key point to note is the use of the word "tasks". When it states that 44% of the legal industry is exposed to AI, it does not mean 44% of lawyers, legal clerks etc., will be gone. It means 44% of the tasks they do today could be automated using AI: Imagine you are an AAA who has a client in the legal industry with ten staff. Your agency succeeded in automating 44% of the tasks using AI that MSCI said could be automated in legal firms. Does this mean the firm will lay off 4 of its ten staff ? They certainly have that option. But they could also use the additional 44% of the day you have created for them positively. They could spend more time doing research to win their cases, more time providing expert advice to their clients, and more time soliciting additional business for the firm. This time saving is why the services AAAs will offer are in demand and will continue to be in demand. Any work task that has a "process document" or "procedure document" for a human to follow mindlessly is ripe for AI to automate. Tasks that make people feel like an automaton at work will be done by a legitimate automaton, freeing humans up to focus on the more "human" task, providing real value to their business and employees. But the question arises: how has this revolution in task automation been translated and communicated in popular media? The Fad-like Spike in AAA Content on YouTube As we transition our discussion to explore the recent surge in AAA content on YouTube and other social media platforms, it's crucial to understand the discrepancy between the real business value of what an AAA can add and the rise in the use of buzzwords and trendy phrases, like AAA itself, that can overshadow the legitimate value a business in this field will provide. The use of AI in business is not new. McKinsey published a report this year that shows 50% of larger firms already use AI in at least one of their business units or functions. So the idea that an AAA is some new type of "business model" is false: What is new is the use of the term " AI Automation Agency ". AAA is a catchy name and well suited to the YouTube and social media audience used to bro marketers pushing "agencies" in areas like digital marketing and social media marketing - SMMAs. The firms currently performing automation services for businesses don't style themselves AAAs, but the services they offer are the same ones the "new" AAA business model pushes. So the only thing new about AAAs is the packaging and social media marketing behind them. While the packaging and presentation of AAAs as a 'new' model might contribute to the current social media frenzy, another factor we must address is content creators' credibility, or lack thereof, in this space. Let's delve deeper into this concern. Accuracy of AAA Content and the Knowledge of its Creators The actual "scam" in AAAs is people publishing content about creating and running AAAs who know little about AI or the application of AI to automate business processes. An easy tell is to look at the history of someone posting AAA content and see if any of the below are true: Have they previously posted content about NFTs, cryptos, SMMA, dropshipping, Amazon FBA etc.? When did they start posting about AI? Was it only after the launch of ChatGPT in November 2022? Do they mix AAA with other "agency models" like SMMA, affiliate marketing, digital marketing etc? 80%+ of AAA content on YouTube and social media is made to generate views on social media platforms. It is clickbait. Those creating the content are simply jumping on AAA to get clicks. These content creators have no experience in applying AI to automate business processes. Much of the content mentions AAA along with agency models like SMMA, affiliate marketing, digital marketing etc., which are popular topics on YouTube. These videos discuss the merits of starting an AAA agency versus an SMMA agency. As mentioned in a previous post, the complexity of what an AAA does amplifies as the business matures. This complexity contrasts with an SMMA-style business, where the core mechanics of your business remain relatively static after an initial learning period of about six months post your first sale. As a result of the above, these two "models" will be attractive to two completely different types of people. The idea of making a comparison between the two is, therefore, pointless. So someone comparing them on social media either a) lack the knowledge to understand these differences or b) understands the differences, but their actual goal is social media exposure, not providing accurate and helpful content. Neither is someone you want to take advice from on starting an AAA. In Conclusion The services and the core idea behind running an AAA is not a scam . The data suggests it will be lucrative over the following years as more firms, particularly small and medium-sized firms, adopt AI to automate and augment their processes and business. While 80%+ of AAA content on social media is produced for clicks rather than as sound business advice, there will be a market for high-quality content that addresses and discusses the reality of what an AAA should actually do and what it takes to run such a business. We expect the volume of AAA content to continue to spike in 2023. However, it will taper off as the realities of starting an increasingly complex business become apparent to those looking to make easy money by jumping on the latest fad early.

There is a growing amount of videos, articles and posts online discussing AI Automation Agency (AAA) niches and asking, "What is the best niche for AAAs?". However, there is a fundamental difference between niches for AAAs and niches in the traditional business sense that you MUST understand. A lot of the AAA material posted online fails to discuss or explain this difference, overlooking the intricate mechanics of the AI tools, models, and data that an AAA utilizes. In this article, I will explain a "niche" for an AAA and show how it differs from a traditional niche. Let's start by looking at what a niche means in the traditional business sense. Traditional Business Niches A traditional 'niche' generally refers to a business catering to a specific subset of consumers who share certain characteristics and are likely to buy a particular product or service. A niche comprises small, highly specific groups within a broader target market you may be trying to reach. A hotel, for example, might specialize in luxury experiences for high-income couples (no children allowed). Or a personal trainer may cater to women above the age of 40. Both businesses focus their offerings on a narrowly defined market or niche. AI Automation Agency Niches It might seem natural to think a niche for an AAA would be similarly specific - where you should focus on AI products & services for hotels or AI products & services for personal trainers. However, this is wrong, and it is one area where AAAs differ significantly from traditional businesses. For an AAA, the niche isn't defined by the industry in which the technology is deployed. Instead, an AAA's niche is tied to the specific AI product or service it offers and the particular problem this offering solves. Consider the example of creating AI chatbots for answering customer service queries. These chatbots can be 'taught' to understand and answer questions about any subject, whether it's hotel facilities, personal training plans, or anything else that can be captured in text. In doing so, they solve the problem of humans being required to answer these often repetitive and time-consuming questions. In this example, an AAA's niche isn't the hotel or personal training industry; it's solving the problem of handling customer inquiries across any business sector. So, while a 'niche' in a traditional business focuses on a segment of consumers who share similar characteristics, in an AAA, it focuses on a specific product/service that solves a particular problem or pain point a business has. Let's look at an example below of an AAA that created a luxury hotel chatbot. I will demonstrate how the niche, in this case, is chatbots rather than luxury hotels. Example Case - A Chatbot Built for a Luxury Hotel Imagine you run an AAA and have just created and rolled out a chatbot for a luxury hotel . The chatbot has been trained to understand and respond to various hotel-related queries, from room availability and rates to restaurant dining options and check-in procedures. Clients can access the chatbot via the hotel's website or by message on the hotel's Whatsapp, Facebook or Telegram. Importantly, to the chatbot , it perceives the hotel information it was trained on merely as 'data'. The chatbot doesn't understand or care about the content of the data, whether it's about room availability, restaurant dining options, personal training, or any other subject. It is programmed, using machine learning, Natural Language Processing (NLP) and complex AI algorithms, to analyze and respond to the data it is trained on, regardless of what it represents. Deep Industry Knowledge Isn't Crucial for AAAs Interestingly, the AAA owner or the AAA employees who created the chatbot are not concerned with the specifics of the hotel's data either. They only care about how the AI chatbot processes and interacts with that data. For the AAA, this data merely serves as the raw material to train the chatbot. Whereas a business owner in a traditional niche needs to have in-depth knowledge of their specific sector, a luxury hotel in this example, an AAA's expertise lies in the AI technology itself. The subject matter of the data their AI technology deals with is secondary. The AAA team that built the chatbot might not be well-versed in the intricacies of the luxury hotel industry, but that's irrelevant to their work. They are experts at creating AI chatbots that can answer customer queries automatically without any humans being needed. This AI expertise is why the hotel bought their product, not because of their knowledge of the luxury hotel market. A Personal Recommendation Suppose the chatbot has been successful and well-received by the hotel owner and staff. It operates round the clock, quickly responding to guests' questions. It has improved customer service while reducing the number of repetitive questions staff must answer. Using the chatbot has resulted in higher customer satisfaction and a happier, more engaged team. As a result, the hotel owner has recommended the AAA to a friend who runs a personal training company for women over 40. The owner of the personal training company has a problem, as many different clients repeatedly ask the same few questions to his trainers. He thinks a chatbot trained with the data needed to answer these repetitive questions could solve his problem and save his trainers a considerable amount of time. The AAA's Real Niche: Problem-Solving Capabilities A traditional firm would niche down and either understand the market for luxury hotels or the market for personal trainers with female clients over 40. An AAA can serve both clients without having a deep understanding of either. For the AAA who created the chatbot for the hotel, the same chatbot can be repurposed with ease for the personal training company. The hotel data the chatbot was trained on is replaced with data relevant to personal training. Once the chatbot has analyzed this new personal training data, it can answer questions about exercise routines, nutrition advice, or appointment schedules as effectively as it did for hotel-related queries. The versatility of AI chatbot technology, applicable across various industries, underscores an AAA's actual 'niche': the unique systems, processes or workflows it employs and the broad spectrum of issues its products or services can address across different sectors. What's the takeaway for AAA owners Shift your focus to the issues and pain points your AI can solve for clients instead of getting wrapped up in their specific industries or demographics. Suppose you create a product or service around systems, processes and workflows that solve a company's problems. In that case, it can then be sold to other companies experiencing the same problem, regardless of their industry.

As AI Automation Agencies (AAAs) are a brand new concept, there is little content of value available on starting and running an AAA . In particular, there needs to be more content on how to identify potential clients, approach potential clients, and what products and services the clients of AI Automation Agencies demand. As we discussed in a previous article, while the use of the term AAA is new, the actual business model behind AAA is not. With the explosion of interest in AI since the launch of ChatGPT in November 2022, several surveys and studies have been conducted among businesses on AI and its use by companies. In this article, we are going to deep dive into two recent surveys that ask small business owners about their use of AI and what they could use AI for in their business. So for those looking to start and grow an AAA, data on what products and services clients want is already available! It does not have the name "AAA" in it, but that does not impact the insights we can glean from it. Please continue reading below for our breakdown and analysis of these two AAA-related surveys' main points of interest. GoDaddy Survey of U.S. Small Businesses on Generative AI - April 2023 In April 2023, GoDaddy surveyed more than 1,000 small business owners in the U.S. to gauge their awareness and perceptions of Generative AI. 98% of the businesses had fewer than 50 employees. Here are the highlights AAA owners need to know: Only 11% of the 1,000+ businesses surveyed have tried using Generative AI for their business: Of that 11%, 75% thought the Generative AI tool they used performed either "Very Well" or "Excellent". Only 4% thought it performed "Poorly". Of the 1,000+ small businesses, 57% are interested in using generative AI tools for their business. Aiifi's Key Takeaways from the GoDaddy Survey 57% of businesses want to use Generative AI tools, but only 11% have used them. This indicates a significant untapped market of businesses interested in Generative AI tools. AAAs who get in front of small businesses will have a very receptive audience for their AI product and service offerings. 3 out of every four who tried AI were very impressed Of the 11% who did try Generative AI tools, 75% thought they performed either "Very Well" or "Excellent". Only 4% thought it performed poorly. This highlights that the correct application of AI products and services by an AAA will likely result in very high client satisfaction. The demand from small businesses for AAA is there. Those who tried it are impressed, and those who have yet to are keen to start. They want to use AI as they know it can help their business, but they need to figure out how to apply it. For AAAs, the message is simple - go to a company interested in AI and apply AI effectively for them. They will likely be very impressed if you apply it correctly. Intuit QuickBooks Small Business Survey - May 2023 In May 2023, Intuit QuickBooks commissioned a survey of 1,000 U.S. small business owners with 0-200 employees asking about their plans for technology investments to drive growth: 32% of businesses want to use AI tools for analyzing customer trends and behaviours Demand for the automation of financial operations is vast. Even the smallest firms are planning to invest heavily in technology in 2023. Aiifi's Key Takeaways from the Intuit QuickBooks Survey Businesses want to analyze their customer trends and behaviours data 32% of the surveyed owners want to use AI tools for this reason. An AAA that expands beyond beginner solutions like chatbots and content generation into using machine learning to help clients glean new insights from the data they have about their customers will see huge demand for their services. The market for AAA products and services increases in tandem with the complexity of your offering. Massive market for automating operations, particularly in finance Nearly 7 out of 10 businesses want to automate their expense management and invoicing operations. The demand for automation is there. AAAs need to come up with solutions to satisfy this demand. Businesses are willing to pay for the technology 35% of owners said they lack time to complete important tasks. They do not have time to research and learn how to use AI tools. Even small businesses have the cash to invest in business technology. An AAA offering practical solutions that solve business problems will find a ready market of buyers for their services. In Conclusion These two surveys highlight three main things: More than 50% of small firms want to use Generative AI tools & services Well over 50% of firms would like to automate their finance operations Business owners have money allocated to spend on AI & technology The key point is that AAAs who embrace complexity to solve their client's business problems will be well rewarded. AAAs hoping to make some easy money with a copy-and-paste chatbot or selling second-hand ChatGPT prompts will only survive for a short time. Innovate, high-quality AI solutions are what the market is demanding.

Beginners are always asking for examples of AI Automation Agency Services that they can provide clients, so we decided to do precisely that. This article will walk you through a detailed example of an AAA service a beginner can provide to companies: Automated News Monitoring. This practical solution addresses a common pain point. I use an almost identical process to monitor news, so it is effective. Please note that this is a straightforward solution to a basic pain point many businesses have. Offering this service will not earn an AAA much if any, cash. However, it was a valuable solution for a client that can be applied to other prospects and clients, so we think it is worth showing as a "beginner" solution. Points to Note This example is a very basic AAA service that is easy to implement. No coding or complex knowledge is required for this solution. We aim to demonstrate how AI Automation Agencies approach and solve problems. If you can solve basic issues, companies will trust you to solve more complex problems. Companies may not even be aware that such a simple solution is available . Step 1 - Business Problem/Pain Point Below is a short background to this case study: Aiifi's client was a financial services company that had 20+ vendors/suppliers The vendor management team managed the relationships with the firm's suppliers The vendor management team had an ongoing task to monitor online news Checking for vendor risk-related news stories, such as compliance breaches, lawsuits, data security issues etc. This task was repetitive and boring for the person carrying it out They could spend their time doing more valuable work for the firm Because it is a manual check, there is also a risk the person may miss an important news story Step 2 - Aiifi's Analysis Aiifi was asked to review the news monitoring process and see if we could use AI to automate it. Our first task was to analyze the steps involved in the existing manual approach. By doing that, we could fully understand the process. Once we fully understood the process, we could identify where it may be possible to utilize an AI tool or workflow software to automate the process. Below are the steps a person on the vendor management team went through for this online news monitoring task: Old Manual News Monitoring Process: Open Google. Type in a vendor company name. Filter search results for recent news. Open news articles individually and read through them to assess their relevance. If an article is relevant, save the link for consolidation at the end. Repeat the process for each of the ten vendors. Consolidate the links and analyze the information gathered from the different news articles. Create an email, attach the relevant links and send the mail to the team. Repeat this entire process weekly. This was an easy problem for Aiifi to resolve. An off-the-shelf AI tool is available that can effectively replace the entire old manual approach on its own. The financial services company was not aware of this tool. As the team lacked time to investigate solutions, they welcomed Aiifi's implementation. Step 3 - Aiifi's Solution Feedly - AI-powered news aggregator Feedly is an AI-powered news aggregator application that automatically compiles news feeds from various online sources. Sources include news websites, trade publications, blogs, research journals, podcasts, newsletters, and YouTube videos. Users choose sources and organize them into feeds. Feeds are a way to organize the articles by topic, project, or industry. Feedly incorporates AI & machine learning into its platform through a feature called "Feedly AI". Users train Feedly AI by giving it insights about the subjects, trends, and sources that are relevant to them. The training helps Feedly AI understand patterns, refining its content curation over time to only show articles tailored to a user's preference in their feeds. Feedly allows articles to be saved from different feeds to a "Board" with one click. You can have a personal "Board" or add content to a "Team Board" to start creating libraries of must-read content for your team to view. Importantly for Aiifi and this problem, you can create team newsletters directly in Feedly. Feedly will automatically convert newly saved articles in a specified "Board" into a formatted newsletter. It will automatically send the newsletter on a set schedule to an email list saved by the user. Although we didn't utilize it in this case, Feedly's integration with Zapier enables connection with 700+ apps like Slack, WordPress, and Notion. Aiifi Implementation Aiifi did the below work for the vendor management team to implement the new process: Set up a Feedly account for the team members who required access for news monitoring. Created separate feeds for each vendor, using the vendor's name as the primary keyword. For each feed, Aiifi prioritized or deprioritized specific themes, topics, and sources relevant to that vendor. Aiifi reviewed the initial news articles Feedly curated on each feed. Aiifi provided feedback on each feed to Feedly AI by hitting the "Less Like This" button for articles that were not relevant. This signals to Feedly AI that it needs to adjust its model to omit similar articles from appearing in the future. This is called "training the model". Aiifi created a "Board" for each of the vendors. Aiifi created a "Team Newsletter" to be sent automatically at 07.00 Monday to Friday. It will include all articles newly saved to the "Boards" since the last email was sent. After the implementation, Aiifi trained the vendor management team to use Feedly. As Feedly is very user-friendly, training was done in a matter of hours. Below is the new monitoring process after Aiifi's implementation. Compare this with the "Old Manual News Monitoring Process" detailed earlier in the article to see the improvement. New News Monitoring Process Using Feedly Login to Feedly. Navigate to the vendor feeds. Review news articles: go through the articles curated by Feedly AI. Save relevant articles: Hit the "Save to Board" button to send important articles to that vendor's "Board". N:B: Remember the articles saved to the boards will be automatically emailed at 07.00 Monday to Friday. The employee no longer needs to save links and manually send an email. Results for Aiifi's Client The Aiifi-led transition to Feedly AI reduced the manual labor involved in monitoring vendor-related news by 95%. The new process was more accurate too. It ensured no relevant news stories were missed, as Feedly pulls from additional sources the employees had not looked at in their old, manual process. Employees were delighted as they no longer had to carry out the repetitive task of searching and filtering for news on Google, saving news story links and sending them in an email. The owner was happy as his employees were freed to spend more time on valuable work. Benefit to Aiifi While this is a straightforward solution, it solves a real pain point for a client . This process can now be used for any of our prospects or clients in the future, regardless of their industry. While we will not make much money from this (we will do it for free), the goodwill it generates and the additional business we will win more than offsets the cost of the time we spend doing it for "free". If you liked this article, you will be glad to hear that we plan to publish more similar articles. In the meantime, we recommend you read the following AI Automation Agency articles from our blog to grow your knowledge of this exciting area.

Have you noticed an increasing number of bland, formulaic answers that lack real substance on Quora lately? You are not alone. We are witnessing the rising trend of answers on Quora written by Artificial Intelligence (AI) writing tools. These answers certainly look polished and articulate. But if we take a closer look, are these AI answers enhancing the Quora experience? Or are they actually diminishing it? What are AI writing tools? AI writing tools, such as ChatGPT and Google Bard, are applications that generate humanlike text in response to a prompt or question from a user. The complex AI models behind these tools have been trained on billions of pages of text from the internet. This training allows AI writing tools to learn how to mimic human writing styles, giving them the ability to understand the context of questions and provide detailed, humanlike answers. Are people using AI writing tools on Quora? Yes! We are seeing people copying Quora questions and pasting them into AI tools like ChatGPT. The AI tool analyzes the Quora question and writes a detailed, articulate response with correct grammar and vocabulary. To the untrained eye, these AI-generated answers may seem to be written by a human. Why is it a problem if people use AI tools to answer Quora questions? In two words - user experience. The ability of AI to mimic human writing is impressive. However, these tools answers are often formulaic, opinion-less and bland. These AI-generated responses lack the personal perspective, nuanced thought and passion inherent in human writing. People use Quora as they enjoy reading other humans' unique insights and opinions. Bland answers written by AI are not what they are looking for on Quora. The increasing prevalence of AI-generated responses ultimately dilutes the platform's unique value in connecting people. This results in a disappointing user experience. If people use AI to assist them in writing answers, but the answer still includes their insights and perspectives, that is fine. The problem is the people who copy and paste AI answers directly into Quora without adding their opinions or thoughts. Let's examine an example of an AI answer to a Quora question To better illustrate the issues with AI-generated answers on Quora, let's look at the question below: The below screenshot shows three separate answers to this question from 3 different people. Note that I have placed the answers side by side for comparison purposes and added some colour (poorly): First, you will notice that the three answers are suspiciously similar in their layout. All three use bullet points of similar size. Next, you will notice that the content in the bullet points is suspiciously similar. In fact, I have coloured the points that are identical across all three answers. Some words are slightly different, but otherwise, the content is an exact match. The reason is that all 3 of these answers are written by AI. Specifically, they have been written by tools using OpenAIs GPT-3.5 model. The proof? Here's the top part of the answer I get if I copy and paste our Quora question into ChatGPT: Looks familiar? The exact same bullet point format with identical content to our 3 Quora answers. From this, we can deduce that all three people answering this question on Quora have copied and pasted the answer ChatGPT gave them. Poor quality of AI answers AI writing tools could benefit Quora if people used them to create helpful answers. But when I read the answer to this question, any of the three versions, it is not useful. It does not tell the person any specific places to find AI automation agencies. Suggesting the person "search online, ask your network or attend industry events" is not helpful. In fact, the person asked on Quora to get recommendations from real people with knowledge or insights to share. These AI answers tell them to seek out recommendations, which is literally what they are doing on Quora. Unhelpful responses like this result in a poor user experience. The three answers are duplicates of each other Another problem is 90% of the content in the three answers is identical. Having people read the same answer three times is highly annoying. The answer should be there once, and if it is genuinely helpful, others will use the Quora "Upvote" button. Implications of AI written answers on Quora The main problem with AI-written answers on Quora is their bland and formulaic feel, resulting in a lower-quality product. Looking at why Quora say they exist in the image above, the human element is removed with AI. So in Quora's own words, their reason for existing is removed with AI answers. Instead, people connect with a machine learning algorithm with no perspective of its own and no ability, or desire, to understand the perspective of a human. Quora themselves have an AI writing tool called "Poe", which people can ask if they want an AI answer. Having Poe is a good idea as it allows people to get an immediate reply from AI, which may be enough in many cases. However, if people ask a question on Quora itself, we can assume they are not looking for an AI answer; they are looking for the insights of other humans, as otherwise, they could just ask Poe. How will this play out? As mentioned above, writers can use AI tools like ChatGPT in a positive way to them craft better answers. The issue is the AI answers copied and pasted directly from ChatGPT that lack the personal experiences, insights and anecdotes the Quora community was built on. If left unchecked, the quality of answers on Quora will be diluted, leaving it a far less interesting place. However, Artificial Intelligence is a fast-moving space. I am hopeful that with the right decisions and protections in place, Quora will continue to be the rich and insightful knowledge-sharing platform it always has been.