SwissCognitive Guest Blogger: Eleanor Wright – “Navigating the Adoption of AI by the Public Sector”

Perfectly positioned to transform government efficiency and public services, governments globally are investing heavily in AI. From the UK’s plan to ramp up AI adoption to the Emirati investment in project Stargate, no government wants to be left behind.

AI however has more to offer governments than transforming public services, and government contracts will accelerate AI companies to industry dominance.

The public sector adoption of AI will require infrastructure, expertise, and a risk appetite. Data centers will be built, and vast amounts of energy will be used. Beyond the financial and material investment, engineers will be needed to code and develop these systems, and government expertise will be required to procure and integrate AI into antiquated legacy systems.

AI, however, has more to offer governments than transforming public services, and governments have the power to transform the business of AI. By gatekeeping access to data and procuring long-term contracts, public sector contracts can rapidly accelerate AI companies into big businesses and deliver the capital needed to beat out the competition, enabling a new wave of incumbents.

This model of public sector procurement from the private sector, however, may not be in the best interest of the citizens and taxpayers who will ultimately fund these large contracts. As AI efficiency and capabilities develop and public sector jobs are replaced, the greater the dependency will be on these companies to maintain critical public services. Thus, it is fair to assume that a critical point will be reached where these companies become too big to fail. If public services become reliant on the capabilities and services of a handful of providers, the balance of power will shift.

This dependency however should not discourage the adoption of AI by the public sector, but shape how contracts are procured and the business model underpinning them. Whether it be public-private partnerships, state-owned or implementing a cooperative structure, the business models underlying the roll-out of AI into the public sector could determine how AI is procured and implemented.

Whilst state-owned assets or companies can be inefficient, open to political interference, and lack a drive for innovation, they offer public-focused interest. Capital saved can be reinvested into the impact of public services and jobs that will have been outsourced to the private sector can be internally generated.

In the same way, state-owned companies operate in the interest of the public, public-private partnerships and cooperative companies may represent a strong middle ground between purely public or privately sourced contracts. Public-private partnerships will limit the amount of control private companies exert, and cooperative companies could enable the development and procurement of AI systems that meet a common economic and social goal.

It should be noted however that neither public-private partnerships nor cooperatives are fully resilient against political or private interference. Decisionmakers will always be susceptible to desiring increased control and securing financial gain.

Finally, another alternative may be to implement an open-source procurement model. By procuring solely from companies utilising open-sourced base models, public service contracts built on open-source models could help mitigate incumbency dominance and level the playing field. These base models could even use university knowledge and expertise to drive and maintain innovation.

No matter how public service agencies and providers choose to procure and maintain AI contracts, the business model underpinning the procurement both internally and externally will heavily shape the future of AI. A carefully thought-out business model could provide a strategic advantage and deliver greater value to stakeholders.

About the Author:

Holding a BA in Marketing and an MSc in Business Management, Eleanor Wright has over eleven years of experience working in the surveillance sector across multiple business roles

Der Beitrag Navigating the Adoption of AI by the Public Sector erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Zachary Amos – “How Countries Are Using AI to Predict Crime”

Industry 4.0 and 5.0 is using robotics to bring supply chains into the future. The last decade has been fraught with challenges, including delays, worker shortages and market volatility. Mitigating costs and enhancing the workforce are the goals of swarm robotics, and artificial intelligence (AI) is making them even more competent. See how these workers make supply chains resilient and competitive.

What Are Swarm Robotics?

Swarm robotics is a field focusing on large quantities of simple yet practical robots. These robots work best in groups to achieve straightforward tasks, making them optimal for reducing labor burdens. They also shine in industries like supply chains, where repetitive tasks take up a major portion of the working day.

Supply chains need to use swarm robotics because they are easy to manage simultaneously. They are autonomous, respond to environmental stimuli and are easy to reprogram to new tasks. The collective efforts of these machines can make decisions on the fly, covering ground from last-mile delivery to utilizing resources in a smarter way.

How Do Supply Chains Use Swarm Robotics?

These robots enhance operations while allowing supply chains to overcome common pain points. Each application for swarm robots is also made better by AI. What does this look like?

Dynamic Operations

Because swarm robots take tedious tasks away from workers, they allow people to focus on more high-level processes. In the meantime, the bots can tally inventory, navigating complex warehouses in large numbers. They are immediately deployable to do automatic updates, sending instant notifications to procurement, fulfillment and distribution teams.

Swarm robots are also ideal in changing, unstructured environments. With AI and sensor technology, they can map areas no matter how complicated they are. As they learn to navigate, they become more proficient when interacting with similar environments because of machine learning algorithms. This informs routing and navigation and allows perpetual scaling potential.

Cost Reduction

Delegating tasks to robots saves supply chains tons of money. Human error costs corporations between $50-$300 for every mistake. The increased accuracy is only one aspect of the financial savings. The robots save businesses time and money in talent acquisition processes, which take efforts away from fulfilling client needs.

However, the most prominent financial gain may be from warehouse savings. Refined inventory management prevents objects from taking up square footage and energy as they collect dust. Instead, there is detailed metadata on each item, their expiration date, market values and more, which swarm robots can collect with AI.

Productivity Gains

ot only do AI-powered swarm robots save money, they make everything more efficient. Preventing errors, defects and more can shorten lead times from suppliers. In one study, several industries experienced shortened fulfillment lead times by an average of 6.7 days.

They can also allow parallel task execution. While some robots pick up objects, others can transport them and even more can pack them. This yields numerous time savings across lengthy processes with multiple intermediaries.

There are also other productivity gains because swarm robots make supply chain environments safer for workers. They can constantly monitor unsafe conditions in real time, saving employees the trouble of entering dangerous circumstances. This means fewer workers experience injuries and incidents, allowing them to work with higher morale in safer conditions.

Preparing the Swarm

Much like swarms of ants group together to achieve a common goal, these types of robots optimize supply chains. Combining them with AI makes them even more powerful. As they advance, swarm robotics consistently prove they are a must-have fixture for supply chain management in the future.

About the Author:

Zachary Amos is the Features Editor at ReHack, where he writes about artificial intelligence, cybersecurity and other technology-related topics.

Der Beitrag How AI Enables Swarm Robotics in the Supply Chain erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Copyright: theconversation.com – “What is an AI Agent? A Computer Scientist Explains the Next Wave of Artificial Intelligence Tools”

Interacting with AI chatbots like ChatGPT can be fun and sometimes useful, but the next level of everyday AI goes beyond answering questions: AI agents carry out tasks for you.

Major technology companies, including OpenAI, Microsoft, Google and Salesforce, have recently released or announced plans to develop and release AI agents. They claim these innovations will bring newfound efficiency to technical and administrative processes underlying systems used in health care, robotics, gaming and other businesses.

Simple AI agents can be taught to reply to standard questions sent over email. More advanced ones can book airline and hotel tickets for transcontinental business trips. Google recently demonstrated Project Mariner to reporters, a browser extension for Chrome that can reason about the text and images on your screen.

In the demonstration, the agent helped plan a meal by adding items to a shopping cart on a grocery chain’s website, even finding substitutes when certain ingredients were not available. A person still needs to be involved to finalize the purchase, but the agent can be instructed to take all of the necessary steps up to that point.

In a sense, you are an agent. You take actions in your world every day in response to things that you see, hear and feel. But what exactly is an AI agent? As a computer scientist, I offer this definition: AI agents are technological tools that can learn a lot about a given environment, and then – with a few simple prompts from a human – work to solve problems or perform specific tasks in that environment.

Rules and goals

A smart thermostat is an example of a very simple agent. Its ability to perceive its environment is limited to a thermometer that tells it the temperature.[…]

Read more: www.theconversation.com

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SwissCognitive Guest Blogger: Ashwin Tambe – “Enabling a Smart Consumer with AI based Search Experience”

A smart consumer search experience empowers shoppers to make informed decisions quickly and confidently. It transcends traditional keyword matching to grasp the deeper intent behind a search query. This includes anticipating needs by suggesting relevant products or services before the consumer even realizes they need them, offering personalized recommendations based on past browsing behavior and purchase history, and seamlessly comparing products and prices across different retailers.

Advances in semantic search, AI, and natural language processing are enabling search engines to better understand user intent, deliver personalized recommendations, and facilitate conversational interactions.

Semantic Understanding and Organized Responses

Traditional keyword-based search is giving way to semantic search, where engines try to understand the intent and context behind a query. Search is getting a major upgrade! Imagine a giant encyclopedia that not only stores information but also understands how different concepts are connected. This is what search engines are building: massive knowledge graphs that link people, places, things, and ideas together. By analyzing these connections, search engines can grasp the deeper meaning of your queries, even if you don’t use the perfect words. For example, if you search for “best running shoes for beginners,” the search engine can understand that you’re not just looking for any running shoes, but for shoes that are specifically designed for people who are new to running. This allows the search engine to deliver more insightful results, such as reviews that focus on comfort and support for new runners, or comparisons that highlight features like shock absorption and breathability.

Personalized Recommendations

Search engines are getting to know you better! By remembering your past searches, location, and other bits of information, they can curate results that fit your interests. Imagine searching for “hiking trails” and seeing suggestions for beginner-friendly paths near your city, based on your previous searches for outdoor activities.

Behind the scenes, powerful algorithms are sifting through mountains of data, like detectives looking for clues. They recognize patterns and make predictions to personalize your experience. This might mean suggesting new cookbooks based on past recipe searches, or recommending movies similar to ones you’ve enjoyed before.

Even chatting with search engines is getting a makeover! Instead of clunky text interfaces, AI-powered assistants are emerging that can have natural conversations. These chatbots can answer your questions and offer help in a more conversational way, just like talking to a friend. Imagine asking “What are the best things to do in Paris?” and having a friendly AI chat back with personalized suggestions based on your interests and travel style.

Unified Experience – One destination Endless possibilities

Imagine a world where interacting with technology feels effortless and intuitive, anticipating your needs and desires before you even express them. This is the vision of Artificial Intelligence (AI) and its role in crafting a unified user search experience.

Traditionally, navigating the digital world has often been a disjointed experience. We juggle between various apps, websites, and devices, each with its own logins, interfaces, and functionalities. AI has the potential to bridge these gaps, creating a seamless and unified experience across all interaction points.

One way AI achieves this is through personalization. By intelligently analyzing our behavior, preferences, and past interactions, AI can tailor the user experience to our individual needs. For instance, an AI-powered virtual assistant might proactively suggest restaurants based on our recent searches and past dining habits. Similarly, an e-commerce platform might curate product recommendations that align with our interests and purchase history. This eliminates the need to endlessly search through countless options, saving us time and frustration.

AI also fosters foresight. AI algorithms can anticipate our needs and provide assistance before we even request it. Imagine a smart home system that automatically adjusts the temperature based on your daily routine or a fitness tracker that prompts you for a workout when you’ve been inactive for too long. This level of anticipation creates a sense of flow and removes the need for constant manual interaction.

Furthermore, AI can break down language barriers. Imagine traveling to a foreign country and being able to have a natural conversation with locals through an AI-powered translator that understands context and subtleties. This removes communication hurdles and opens doors to richer cultural experiences.

The possibilities of a unified experience powered by AI extend far beyond personal use cases. In the healthcare industry, AI can analyze patient data to provide more personalized treatment plans and improve overall health outcomes. In the education sector, AI-powered tutors can adapt to individual learning styles, creating a more effective and engaging learning environment.

However, it’s important to acknowledge the ethical considerations surrounding AI and user experience. Data privacy concerns and the potential for prejudice in algorithms need to be addressed to ensure a truly unified and positive experience for all.

Overall, AI has the potential to revolutionize the way we interact with technology. By creating a unified experience that is personalized, proactive, and removes language barriers, AI can empower us to achieve more and unlock a world of endless possibilities.

About the Author:

Ashwin Tambe, (Delivery Management Google , Retail CPG) is a management professional with expertise in enabling customers with AI adoption and bridging business gaps with use of modern technology. Ashwin actively engages in the public discourse on Large Language Models (LLMs) by sharing his insights through articles published on various digital platforms, exploring their consumption, societal impact, and potential role in shaping the future. Beyond his professional experience, Ashwin actively contributes to the academic community.he is serving as a judge and student mentor in University of Arlington Texas.

Der Beitrag Enabling a Smart Consumer with AI based Search Experience erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Copyright: bmmagazine.co.uk – “Bosch’s €2 billion gamble: Investing in employee retraining amid the AI revolution”

What is ‘quiet hiring?’ – Organizations that invest in reskilling and upskilling can fortify their workforces for the coming seismic changes wrought by technology, globalization, and markets.

Not long ago, Bosch announced a staggering plan to invest €2bn in retraining a portion of its 400,000 employees. As Europe’s largest car parts supplier, Bosch aimed to mitigate further job losses as the automotive industry transitions from traditional combustion engines to electric vehicles. The issue extends far beyond car-making.

McKinsey & Company forecasts that by 2030, one in 16 workers – totaling over 100 million across eight economies – may need to change occupations. This underscores the pressing need for reskilling and upskilling initiatives, driven primarily by rapid technological advancements automating jobs and generating demand for new skills.

Additionally, globalization and shifting market dynamics necessitate workers to adjust to new industries and roles. This interconnectedness has boosted trade, communication, and mobility across borders, often resulting in the outsourcing of jobs to countries with lower labor costs, displacing or rendering jobs in traditional sectors obsolete.

This process is also driven by shifts that occur within markets over time, due to changes in consumer preferences or regulatory overhauls. Tasks within industries tend to become more complex as new procedures, tools, and regulations emerge.

In the financial services industry, the proliferation of complex financial products like collateralized debt obligations (CDOs) and credit default swaps (CDS) has heightened the complexity of risk management. Assessing credit, market, and liquidity risk for these instruments poses unique challenges, demanding specialized knowledge and skills from risk managers. Continuous learning and skill development are essential for these professionals to remain relevant in their field, a necessity that extends beyond banking.[…]

Read more: www.bmmagazine.co.uk

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Copyright: ibm.com – “The Most Valuable AI Use Cases For Business”

When thinking of artificial intelligence (AI) use cases, the question might be asked:  What won’t AI be able to do? The easy answer is mostly manual labor, although the day might come when much of what is now manual labor will be accomplished by robotic devices controlled by AI. But right now, pure AI can be programmed for many tasks that require thought and intelligence, as long as that intelligence can be gathered digitally and used to train an AI system. AI is not yet loading the dishwasher after supper—but can help create a legal brief, a new product design, or a letter to grandma.

We’re all amazed by what AI can do. But the question for those of us in business is what are the best business uses? Assembling a version of the Mona Lisa in the style of Vincent van Gough is fun, but how often will that boost the bottom line? Here are 27 highly productive ways that AI use cases can help businesses improve their bottom line.

Customer-facing AI use cases

Deliver superior customer service

Customer interactions can now be assisted in real time with conversational AI. Voice-based queries use natural language processing (NLP) and sentiment analysis for speech  recognition so their conversations can begin immediately. Using machine learning algorithms, AI can understand what customers are saying as well as their tone—and can direct them to customer service agents when needed. With text to speech and NLP, AI can respond immediately to texted queries and instructions. There’s no need to make customers wait for the answers to frequently asked questions (FAQs) or to take the next step to purchase. And digital customer service agents can boost customer satisfaction by offering advice and guidance to customer service agents.

Personalize customer experiences

The use of AI is effective for creating personalized experiences at scale through chatbots, digital assistants and customer interfaces, delivering tailored experiences and targeted advertisements to customers and end-users. For example, Amazon reminds customers to reorder their most often-purchased products, and shows them related products or suggestions. McDonald’s is building AI solutions for customer care with IBM Watson AI technology and NLP to accelerate the development of its automated order taking (AOT) technology. Not only will this help scale the AOT tech across markets, but it will also help tackle integrations including additional languages, dialects and menu variations. Over at Spotify, they’ll suggest a new artist for the customer’s listening pleasure. YouTube will deliver a curated feed of content suited to customer interests.[…]

Read more: www.ibm.com

Der Beitrag The Most Valuable AI Use Cases For Business erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger:  Zachary Amos – “How AI Is Helping Retailers This Holiday Season”

Artificial intelligence is helping retailers manage inventory, forecast demand and improve the customer experience this holiday season. As more businesses adopt this advanced technology, they see more significant returns.

1. Processing Holiday Returns

Retailers see a massive increase in refunds and send-backs during the holiday season. Consumers collectively return almost 18% of purchases, amounting to $171 billion in losses. Businesses feel motivated to adopt new policies like only offering store credit or charging for shipping. Consequently, brand loyalty and customer satisfaction have taken a hit. 

On top of return-related losses, retailers must absorb increased labor costs. In fact, around 44% of them hire temporary staff just to handle holiday returns. Instead, they could use AI to automate the process. Additionally, they could use it to analyze consumer history and predict send-back rates to improve their policy-making.

2. Managing Inventory

The massive increase in orders and returns during the holiday season makes inventory management complex. Unavoidable situations like supply chain delays caused by winter weather add to the confusion. As a result, overstocking and inaccurate stock reporting are common issues.

Fortunately, AI can optimize fulfillment and reduce excess stock to maximize revenue. It can minimize supply chain overhead costs by forecasting reorder points, increasing order accuracy and streamlining distribution. Retailers can use it to track shipments in real time or automatically order products, reducing human error during the chaotic holiday rush.

3. Personalizing Holiday Sales

Customers expect holiday sales. Often, retailers find accurately predicting those expectations challenging. Marking down unpopular items or incorrectly estimating the best discount rate leads to lower profitability, brand loyalty and customer satisfaction. In response, many businesses are adopting AI-led pricing decisions.

Stores that use algorithms to forecast demand in real time have a much higher chance of aligning their actions with consumers’ expectations. Because of this, experts believe AI-led pricing decisions could add $500 billion in value to the global market. Ultimately, this technology could maximize holiday revenue and brand awareness since people are more likely to purchase products when they receive personalized deals.

4. Preventing Seasonal Shoplifting

Even retailers who don’t typically suffer from shoplifting will experience it in full force during the holiday season. At this time, shrinkage — losses due to theft, fraud or administrative errors — increases by over 15% on average, accounting for nearly 40% of annual losses. Sudden increased inventory movement and customer bases make it challenging for workers to spot and stop thieves. 

This issue has only gotten worse with time. In 2022, shrinkage totaled over $112 billion, up nearly $20 billion from the previous year. Retailers feel forced to close stores, reduce operating hours, lock up products and alter in-store selections. Most don’t even allow their employees to physically stop shoplifters for safety reasons, proving the need for an automated system like AI.

AI-powered cameras, alarms and sensors can identify and detect thieves in real time without tipping them off. For example, one store’s model leveraged 100,000 hours of surveillance videos to monitor over 100 behavioral cues. As a result, police are more likely to arrive in time to prevent losses.  

5. Replacing Seasonal Workers

For decades, temporary workers have been the industrywide solution to demand spikes. In the United States, retailers must fill 400,000 seasonal positions to accommodate increased consumer demand. Most assume they must take on the heightened labor and administrative costs. However, AI presents a new solution. 

Retailers can use AI instead of filling positions with humans. It can automate management, customer service or in-store guidance. For example, an in-aisle kiosk could provide a product’s location and answer pricing questions. This minimizes overhead and labor costs.

6. Improving Search Functionality

Retailers can use AI to deal with sudden shifts in demand and large spikes in customer queries during the holiday season. For example, they can use a generative model to edit the frequently-asked-questions page in real time. Alternatively, they could use an algorithm to update auto-fill suggestions based on current interests. 

Companies are already experimenting with this technology to improve browsing. For example, Google’s new generative model lets people virtually try on clothes before ordering them. These customer experience improvements can maximize profitability, brand loyalty and consumer satisfaction.

7. Tracking Holiday Shipments

Winter-weather-caused delays and a sudden, massive spike in orders make retail supply chains incredibly hectic during the holidays. In response, many companies are adopting AI to monitor shipments and predict trends. Amazon’s new supply chain AI manages inventory and forecasts daily demand, increasing sorting and stocking speeds by 75% and lowering processing time by 25%.

Leveraging AI Leads to a Profitable Holiday Season

In-person and online retailers can use AI in almost every aspect of business, from customer experience improvements to order fulfillment. It’s clear this technology will lead to a uniquely profitable holiday season.

About the Author:

Zachary Amos is the Features Editor at ReHack, where he writes about artificial intelligence, cybersecurity and other technology-related topics.

Der Beitrag How AI Is Helping Retailers This Holiday Season erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Copyright: forbes.com – “How AI Can Drive Business Growth By Accelerating Process Automation”

That’s huge, especially in terms of what that means for enterprise growth and cost optimization. But what shape will these new gains in efficiency and productivity take? According to the survey of those business leaders:

• 53% anticipate better use of employee time

• 51% plan to benefit from a competitive advantage

• 50% expect to improve operational efficiency

• 59% anticipate better decision making

It’s clear that business leaders see the business benefits of applying AI to process automation. And while these individual anticipated benefits are good news for business operations, it’s also critical to consider what they mean for business strategy. The picture becomes clearer when we sort these benefits into three primary categories: process optimization; faster access to data; and improved user experiences.

Building Optimized Processes, Faster

Artificial intelligence (AI) makes it possible for teams to build the best version of any process in a matter of seconds. These new processes are constructed by AI from inputs and requirements defined by the user. These inputs can then be supplemented with data from existing processes. AI then takes this information and transforms it into a structured, efficient process.

For example, let’s say the IT team gets a request to build an approval process for the procurement team. Users can tell the AI what phases or activities need to occur, who needs to be involved and what kinds of rules apply to the new process. AI then takes these parameters and constructs an optimized process that can be implemented as-is or fine-tuned as needed.

This reduces the burden of manually building processes from scratch, which can reduce the burden on teams who manage many different processes that may change often as business strategies or internal requirements evolve.[…]

Read more: www.forbes.com

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Copyright: hbr.org – “How AI Can Help Leaders Make Better Decisions Under Pressure”

More and more businesses are turning to AI-powered technologies to help close the data-insight gap and improve their decision-making capabilities in time-critical, high-pressure situations. These technologies encompass a wide range of tools, including virtual assistants, virtual and augmented reality, process discovery, task mining, and an array of data analytics and business intelligence platforms. Recently, there has been tremendous interest in generative AI or large-language models, a whole class of algorithms that are able to ingest vast tracts of data — text, numbers, software code, images, videos, formulas, and so on — understand their probabilistic structure, and create summaries, answers, simulations, and alternative scenarios based on these data. This article addresses three critical questions faced by decision-makers in using these technologies: 1) In what contexts are AI decision-making technologies likely to be beneficial? 2) What are some of the challenges and risks of using these technologies? and 3) How can business leaders effectively benefit from these technologies while mitigating the risks?

Business leaders and managers face increasing pressure to make the right decisions in the workplace. According to research by Oracle and Seth Stephens-Davidowitz, 85% of business leaders have experienced decision stress, and three-quarters have seen the daily volume of decisions they need to make increase tenfold over the last three years.

Poor decision making is estimated to cost firms on average at least 3% of profits, which for a $5 billion company amounts to a loss of around $150 million each year. The costs of poor decision making are not just financial, however — a delayed shipment to an important supplier, a failure in IT systems, or a single poorly managed interaction with an unhappy customer on social media can all quickly spiral out of control and inflict significant reputational and regulatory costs on firms.

Against this backdrop, more and more businesses are turning to AI-powered technologies to help close the data-insight gap and improve their decision-making capabilities in time-critical, high-pressure situations. These technologies encompass a wide range of tools, including virtual assistants, virtual and augmented reality, tools for process discovery and task mining, and an array of data analytics and business intelligence platforms. Recently, there has been tremendous interest in generative AI or large language models (LLMs), a whole class of algorithms that are able to ingest vast tracts of data — text, numbers, software code, images, videos, formulas, and so on — understand their probabilistic structure, and create summaries, answers, simulations, and alternative scenarios based on these data. Well-known generative AI models include OpenAI’s ChatGPT, Google’s Bard, Meta’s Llama 2, and Anthropic, but there are many more.[…]

Read more: www.hbr.org

Der Beitrag How AI Can Help Leaders Make Better Decisions Under Pressure erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Asaf Yanai – “Will AI Replace Human Creativity?”

Not a day goes by that ChatGPT isn’t in the news. Artificial Intelligence (AI) now offers solutions for everyone, from high schoolers looking for homework assistance to chess players wanting to cut corners on their gameplay. Surgeons are competing against AI to game out whether they’ve made the right choices during surgery, and AI is being used for everything from determining what songs to suggest on Spotify to what flavors to put in various recipes.

Spoiler alert: Humans used to perform a lot of these tasks and jobs. So it begs the question: with AI rapidly becoming a feature of daily life, how much do we need to worry about it replacing human creativity and workmanship? Are we doomed to a future where robots are curating all of our playlists and menu choices? What would that future even look like?

What is AI?

Before prognostications get too gloomy, let’s assess what we’re really dealing with. The term “artificial intelligence” is a catch-all that encompasses various technologies that can perceive, synthesize, and infer data. Various computer programming languages, such as R, Java, and Python, are used for writing and training machine learning algorithms.

Simply put, these programming languages lay out a sequence of rigorous rules that the program must follow in any situation. Searching for something on Google? There’s an algorithm for that. Seeing a digital ad on Facebook or being served a playlist recommendation on Spotify? Algorithms run what and when you see these things, too.

Development of AI over the years

Although many of us are only just hearing about it, AI has actually been around for over 50 years. The term was first used in 1956 during a computer conference, but for those of us who are scifi fans, we know that writers and philosophers have long dreamt about days when automation and humanoid robots would rule the Earth.

For decades, engineers, scientists, and mathematicians toiled to make fiction reality but were constrained by challenges. For example, in the mid-20th century, it could cost upward of $200,000 (1950s value) to run a computer; such were the data processing capacity restraints. There were also issues with applying algorithms to real-life problems. Only in the past 30 years or so has processing capacity improved to the point where logic programming and thus true artificial intelligence is possible.

A graph showing the development of AI in relation to general processing speed

Now that the computer processing power is available, AI systems absorb and digest large amounts of information and analyze them for correlations and patterns. They then use that information to make predictions about future simulations of the same situation. In this way, AlphaGo, Google’s AI tool, can beat the world’s best chess players, and image recognition systems can recognize and describe objects in images after viewing millions of samples. Well-built AI even learns much like the human brain: It builds on top of itself, so it gets smarter with each piece of information it gathers and synthesizes.

AI implementations

In the past 10 years, AI has been used in most industries, with a surprising array of applications. In some cases, AI tools have been applied to situations where the tool can do things better than a human, and in other cases, tools have been applied to save time and money. Concrete examples are always useful when exploring complex topics, so here are a few you may be familiar with (and others you may not know about):

Driverless cars – Engineers have used a type of AI known as neural networks to build cars that recognize traffic signals, obstructions, road signs, and other features of travel.

Manufacturing robots – In some manufacturing processes, robots are used to generate purchase requests or handle complex orders that come in from various sales channels. They’ve even been used to detect damage and faults in the overall manufacturing process.

Automated financial investing – Algorithms can be applied to make investing decisions, which brings down the traditional costs of having a money manager who analyzes markets and makes those decisions instead.

Copywriting – ChatGPT and other AI tools can write essays, blogs, and articles on a range of topics. Many schools are currently grappling with how to penalize students who use AI in writing their assignments.

Chatbots – If you’ve ever used a chatbot to speak with customer service, you may be familiar with a robot attempting to  solve your problem before you’re connected to a real human being (if that ever happens).

As computer processing limitations have declined, AI tools have become more affordable, meaning that even very small businesses can use them. This has meant that companies of all sizes can cut the costs of humans doing work and invest in cheaper tools that can often do the same thing. For example, the cost of implementing a chatbot is much lower than paying humans to respond to customer queries – and chatbots are available 24/7, without the room for human error. It sounds pretty sweet, looking at it from a dollars and cents perspective.

AI and emotional intelligence

 And here we arrive at the million-dollar question: Will AI replace jobs and human creativity? If machines can learn to drive cars, manufacture products, write essays, and act as customer service agents indefatigably (and without pay!), what hope is there for those of us who need employment?

Firstly, it’s comforting to remember that AI systems are only as good as their foundational frameworks. Remember that AI is built by humans (although there are some new attempts to get AI to code itself, and that’s going well). Like all things we humans touch, the outcome is only as good as its inputs. For now, anyway, remember that AI builds upon itself much like human knowledge bases.

The more important consideration is the same one that drives literature, art, philosophical, and political debates: What makes us human? Can that experience be conveyed to non-human forms? While AI can learn, synthesize, and make decisions based on that learning, it cannot replace the human experience of creativity and emotion, at least not yet. And that’s what makes humans unique, layering on our experiences and human attributes – such as resilience, grit, tenacity, and a laundry list of others – to the mix.

While it may be scary that ChatGPT passed an exam given by a Wharton Business School professor, give an AI tool five minutes in a room with an angry client or a brief for a highly sensitive press release. It can handle the task, but it can’t add on the emotional intelligence that is still so necessary and valuable.

How creatives can harness AI

When it comes to marketing, emotional intelligence is crucial. It’s what allows us to make an impact, to tap into audiences, and to deliver creative that packs a punch. It’s a certain je ne sais quoi that makes audiences feel something and connect with content on a deeper level.

Alison is special because it lets humans do what they do best – create meaningful, thoughtful, and emotive ad creative that sparks responses in others. And it uses the AI for things best left to machines: What colors work best in ads? Text? Sounds and characters? Alison.ai can do all the analysis so your team can focus on creating the elements that sorely need the human touch to set it apart.

AI vs. People

In conclusion, AI has rapidly become a ubiquitous presence in our daily lives, offering solutions to a wide range of tasks and jobs that were traditionally completed by humans. However, the question remains: Will AI eventually replace human creativity and workmanship? While AI has been around for over 50 years, it has only been in the past 30 years or so that processing capacity has improved to the point where true artificial intelligence is possible.

AI systems are only as good as their foundational frameworks, and for now, they build upon themselves much like human knowledge bases. However, the more important consideration is what makes us human and whether that experience can be conveyed to non-human forms. While AI can automate many tasks, it cannot replace the human experience of creativity and emotion, at least not yet. Therefore, it is important to view AI as a tool that can assist and augment human creativity and workmanship rather than a replacement for it.

About the Author:

Asaf Yanai, a visionary entrepreneur and the Co-founder and CEO of Alison.ai, has been at the forefront of technological innovation for over 15 years, challenging conventional boundaries and transforming industries. Alison.ai, the third company Asaf has successfully founded, stands as a testament to his exceptional ability to identify opportunities, drive growth, and lead teams toward achieving remarkable outcomes. His previous roles, including VP of Growth, Business, and Marketing Optimization and Head of Media Buying at world-class online marketing companies, have honed his expertise and strategic acumen. An entrepreneur at heart, Asaf holds a B.A. in Business Administration and an MBA in Marketing from IDC Herzliya in Israel. His entrepreneurial journey, marked by a relentless pursuit of progress and visionary leadership, serves as an inspiration for many in the tech industry.

Der Beitrag Will AI Replace Human Creativity? erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.