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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.

Twitter and Reddit are now charging for their APIs and this looks to be the beginning of a battle to control data - and the impact on AI cannot be underestimated. There is a storm brewing online over the control of data and it will have massive repercussions for AI. While the output of ChatGPT and image generators attract most of the attention, the input data is being almost entirely overlooked. I have tremendous respect for the scientists and coders who have brought AI to life - but the key to Artificial Intelligence and Machine Learning in practice is data. The reason for this is technology tends to proliferate. With so much of the open-source community (admirably) working on AI, the actual software and models are not going to remain the key differentiators in this artificial intelligence arms race. Once the software exists only those with the requisite data will be able to use it. Elon Musk knows this, as do the Reddit moderators . OK, admittedly neither of these groups might be thinking specifically about data for AI models, but both have recently taken drastic steps to protect data they believe is theirs. Technology 101 The foundation of any technology lecture is Junk In, Junk Out. And to paraphrase that somewhat, the value of a piece of software, from a basic database to the most complex AI, depends enormously on the quality of the data you put in. For many years data on the internet, certainly publicly visible data, was deemed to be free to access. You could see every Tweet (in theory) via the Twitter app, so why not give unlimited bulk access to developers via APIs? Until recently this was not an issue. However, as AI has expanded and the value of data has been more acutely noticed. API stands for Application Programming Interface . That might sound technical but in this context consider an API like having a backdoor or bulk access to a platform. For example, you could use a Twitter API to retrieve all posts referencing “AI” in one go, rather than browning the app itself for hours. Twitter Data and APIs You may have seen the headlines about Twitter, now known as X, limiting how much people can use the app. You can read more details here but the gist is that Elon Musk has come to the conclusion that Twitter’s data, in its aggregate form, is a uniquely useful data set. Yes, your Tweet about what you ate for breakfast might be of interest to your followers. However, with bots that scrape Twitter data from everybody, it could be far more useful to see the trends of what thousands of users are eating for breakfast. Or, what politicians they like. This approach has been used to predict elections and trade financial markets and is vital to many highly profitable sentiment analysis tools. Twitter has always allowed other applications to access its data in bulk via APIs at no or de minimis cost. Now, they have jacked up the price. Reddit Moderators and APIs Similarly, the moderators on Reddit have been protesting over recent API price hikes. Reddit has historically allowed third-party apps to access subreddits via APIs at no cost. However, with growing talk of an IPO, Reddit executives are keen to boost revenue. So they have added a significant charge for the use of these APIs, much to the chagrin of the moderators who essentially manage the data. This has forced many apps to shut down including the hugely popular iOS app Apollo. It is worth noting that Reddit moderators are not employees of the company. They are generally motivated by a passion for the subreddits they moderate and many dedicate a huge amount of time for no direct reward. Why Do They Care About Data and the APIs? It is probably just as simple as wanting to own what they create. I doubt very much Reddit moderators would see themselves on the same side as Twitter but in this context there are similarities. Twitter’s quality data only exists because of Twitter’s infrastructure and user base. Twitter carries the cost, so they do not want other for-profit apps or organizations to profit. Reddit’s quality data only exists because of the moderators. Yes, there are some technical costs to hosting Reddit, but the role of moderators is staggering. There are more than one million communities on Reddit and 140,000 active subreddits . Each of these has between one and 25 moderators. It is estimated that these moderators save the company millions each year, but in truth, the benefit is far more than just the dollars saved. Facebook, Youtube, and other social media companies have moderators to maintain legal and policy standards (i.e. no violence). The Reddit moderators ensure subreddits remain on topic and truly cultivate a high level of content. What Next for Twitter and Reddit APIs? Neither Elon Musk nor the Reddit mods are keen for others to unduly profit from, or limit the use of, the data they help curate . (There may well be more complexities but this is at least partly the case.) Many AI-powered sentiment analysis tools have had to switch off their Twitter feeds because the data is simply too expensive. Reddit threads that were historically public are now going private to block the paid-for API data harvesting. Some subreddits have been flagged as NSFW (i.e. an adult content warning) in protest. Reddit is now threatening to remove moderators who do not comply. How far this will go is impossible to say, but I believe we are only in the early stages of the AI data battles. Own The Data, Own The Software These two examples are just the warning shots in what I expect to turn into a full-on war to control data online. As AI progresses and becomes more accessible, the demand for unique data will skyrocket. The advantage of an AI sentiment analysis tool will no longer be in who can access the software, but who controls the data feed. The same goes for facial recognition, election polling, marketing, and countless other fields that have gotten used to leveraging bulk data online. Soon, those who own the data will essentially own the software.

Much has been written in 2023 about the collapse in Twitter's ad revenue, a fact admitted by new owner Elon Musk himself. Yet, the deeper and more interesting story lies not in dwindling advertising revenue or even in subscription revenue through the much-maligned Twitter Blue. The real reason Musk bought Twitter, a detail often overlooked, is it's a veritable 'oil field' of data. In this article, we will look at why Musk, a recognized figure in the AI space, is less interested in Twitter as a social media and advertising platform and is far more interested in its value as a data source. From clamping down on bots and data scrapers to monetizing API access and incentivizing high-quality content creation by paying users, we'll analyze Musk's actions since his takeover. We'll see that the real reason Musk bought Twitter is not to control the discourse on social media but to control the valuable output of this discourse - data - the new oil of the AI age. Twitter is not a social media company What people are missing is that Elon Musk did not pay $44 billion to buy a social media platform. He paid $44 billion to buy a data source. While Twitter appears to be a social media company to its users, most people miss that to its owners; Twitter's value is as a data source. Let me explain. Free API access revoked & data scrapers sued In February 2023, Twitter announced that free access to its API, which allows external software to access Twitter's tweets and user data automatically, would be revoked. Instead, those who wanted API access to Twitter's data would need to pay. In July, Twitter began limiting the number of tweets users could view daily in response to extremely aggressive data scrapers - programs extracting large volumes of tweets and user data violating the terms of service. The company also sued 4 Texas-based individuals who they claimed illegally scraped data from Twitter, a sign of how seriously they were taking the data scraping issue. Charging for API access and clamping down on data scraping shows the value Musk assigns to Twitter's data. Now that he is controlling the Twitter data supply, the question we need to ask is - will there be a demand for Twitter's data? We need to look to a different part of the Musk empire for the answer. AI models need data. LOTS of data Interest in Artificial Intelligence tools has exploded since ChaptGPT launched in late 2022. Billions of dollars have been invested in AI companies and start-ups, particularly in the Generative AI space. These companies build and deploy complex AI models and algorithms that are essentially data-hungry engines. These AI models require vast amounts of data to create accurate, insightful, and valuable outputs or predictions. Without data to input, the sophisticated frameworks underlying them would be worthless. Elon Musk understands the AI game Musk was a co-founder of OpenAI, the research lab behind ChatGPT. AI is a crucial part of Tesla's, the electric car company Musk runs, efforts to develop fully autonomous, self-driving vehicles. Musk also co-founded Neuralink, a neurotechnology company that uses AI to create brain-computer interfaces. Musk's involvement in AI is deep, and it has spanned several years. He is no AI bandwagoner. Is Twitter data valuable? Twitter is the source if you want real-time data on sentiment, trends and discussions. Users express their opinions and engage in debates, an online town square, making it a rich source of timely insights you cannot get from any other platforms. Marketers can use Twitter data to gauge sentiment about products or brands. Politicians and organizations can assess public opinion on policies and candidates. Investment banks can analyze tweet patterns to predict market trends. In the new AI-driven world, clean and high-quality data from Twitter is a valuable commodity. Bots and spam lead to dirty Twitter data Musk's criticisms of bots and spam before and during his Twitter takeover can be linked to his understanding of AI models' need for "clean" Twitter data. 'Clean data' is accurate, free of irrelevant noise, like bot-generated tweets, and ready for processing by AI models. AI engineers and data scientists often talk about "data cleansing". This involves collecting raw data from sources, like Twitter, and "cleaning it" to remove noise and inaccuracies before it is fed into AI models. Musk's focus on bots and spam makes even more sense in the context of his buying Twitter as a data source rather than as a social media platform. Musk has been encouraging people to add to the conversation In July, Musk announced that Twitter would begin paying verified content creators for the ads displayed in their replies. Soon afterwards, screenshots started appearing from excited Twitter content creators confirming the first payouts they would receive from Twitter. For several reasons, starting a monetary incentive to create content or data makes sense. It encourages users to create more content on Twitter and potentially move their content publishing from other platforms, YouTube & Spotify, for example, to Twitter. It encourages users to create high-quality content that others engage with, as payments are based on ad views in replies. Musk wants users to discuss and debate with each other in replies, as these are quality insights for the Twitter dataset. High-quality content = high-quality data. More interesting content will drive new users to the platform, users who, in turn, can create or engage with content, adding to the conversation on Twitter. Musk has killed off the Twitter brand Musk did not buy Twitter because it was "Twitter". When he dropped the Twitter name and logo in July, branding and advertising agencies went into overdrive about the foolishness of the move and the billions in brand value he wiped out. But this is because they are still thinking of Twitter/X as a social media platform for advertisers to advertise on. Musk is not. Companies stopped advertising. But are they paying Twitter for data? Sure, advertisers left, and Musk no longer receives their advertising revenue. But as mentioned earlier, Twitter data is extremely valuable to marketers. The question is, are any of the advertisers who left Twitter quietly paying Twitter for data? Marketers are thinking about Twitter revenue from the visible ads they see on the platform. They need to shift their perspective and think of income from the data side. If they did, they would see that companies can pay for and use Twitter data discreetly, including via 3rd party apps, without outsiders ever knowing. Firms who stopped paying for "advertising" on Twitter may still be paying Twitter for a far less obvious product - their data. Musk is not playing the conventional social media or advertising game Charging for API access, killing off data scraping, combating bots and spam, and offering monetary incentives to encourage the creation of more content. He even went as far as killing off the Twitter brand. These moves make it clear that Musk did not buy Twitter/X to generate revenue from advertising or subscriptions. His game is different. In a world where data flows like oil, Elon Musk didn't just buy an oil field; he crowed himself a Data Sheikh.

Pickleball is one of the fastest-growing sports in the USA and around the world. The appeal is clear - it is easy to get started but takes years to master and there is always room to improve your game. There are now almost 9 million players in the United States alone, not bad for a sport that was invented in the 1960s . The sport is often described as a combination of tennis, ping-pong and badminton. It’s a great workout, beginners can become competitive almost immediately and it is suitable for players of almost all levels. And now, with the help of Pickleball Vision AI , it is part of the AI revolution. Their patent-pending software uses computer vision and machine learning to turn videos of pickleball games into training material, match reporting and insights. Computer vision is a branch of Artificial Intelligence most commonly known for its use in facial recognition software. It takes video feed or camera inputs and transforms the data into numerical representations that can be more easily processed and analyzed by algorithms. This is then processed via convolutional neural networks ( CNNs ) and Machine Learning algorithms are applied to identify patterns and garner understanding. " Convolutional Neural Networks (CNNs) are to computer vision what Large Language Models (LLMs) are to natural language processing. Just as CNNs extract hierarchical features from images to recognize patterns and objects, LLMs parse through vast amounts of text to understand and generate coherent language." PB Vision appear to be in the early stages of their AI journey, at least in terms of their release schedule . We recommend you follow them on Twitter to stay up to date with them, and maybe pick up a fun new hobby along the way. This software might seem a little more frivolous than the sort we usually cover here at Aiifi but it is a great example of outside-the-box thinking when it comes to Artificial Intelligence applications. Most instances of computer vision we see in the news related to facial recognition and employee monitoring , but here is a company putting that software to use in a much more positive manner. It is also useful to see the application of AI in new areas rather than the simple automation of tasks that humans currently carry out.