If you’ve been reading about business technology recently, then “AI agents” and “agentic AI” are terms you’ve probably come across with increasing frequency.

They’re often used interchangeably, and I’ve noticed this can cause a little confusion as they refer to subtly different concepts.

As it’s something I’ve been asked about, I thought I’d put together a little explainer.

Remember, AI is a quickly evolving subject, and the terminology around it is evolving, too.

So here’s an overview of AI agents and agentic AI, and most importantly, a guide to telling the difference.

So What Are They And How Are They Different?

Ok, both concepts are used to refer to AI that can work through multi-step problems independently, with little guidance from humans.

AI agents are specific applications created to do just this and are already widely in use today, even if we don’t often see them.

They’re used by banks and in e-commerce to verify our identities, automate transactions and record keeping, and learn about us in order to improve their service.

Agentic AI, on the other hand, refers to the field of AI that enables machines to operate as agents. Agentic AI is concerned with researching and developing AI models capable of the type of independent, autonomous work that agents can do.

Thinks of AI agents as specific medicines prescribed for particular conditions, while agentic AI is the entire field of pharmaceutical science that develops all medications.

Another way of looking at it is in the context of artificial general intelligence (AGI) – the future goal of one day creating AIs that are generalists rather than specialists, capable of any task we ask them to help out with.

Today’s AI agents are not AGI – and they probably won’t be for a few years yet. Yes, they can carry out complex tasks, but still only specific tasks they were created for. They can’t really apply their learning to doing other things in the same way humans can.

Agentic AI, however, is a field of AI research and development some believe will eventually lead to AGI. It includes building AIs that are capable of interacting with external systems – both digitally by interfacing with them and physically with robotics.

So, to put it in a very straightforward way – the term “AI agents” refers to a specific application of agentic AI, and “agentic” refers to the AI models, algorithms and methods that make them work.

Why Is This Important?

AI agents and agentic AI are two closely related concepts that everyone needs to understand if they’re planning on using technology to make a difference in the coming years.

As research and development into agentic AI continues, we will see increasingly sophisticated agents capable of automating many different tasks.

Truly useful and personalized digital assistants, capable of learning about what we need in detail and taking steps to help us achieve it, will just be the start.

Integrated with robotics, agents will also open the door to the automation of physical tasks, such as complex construction or engineering work.

With the massive and rapid advancements we’re seeing in AI, I think it’s likely these changes will take place at a speed that will take many of us by surprise.

Staying ahead of the curve now and understanding the latest developments in AI, robotics and automation is essential for anyone who wants to prosper in the new technological era.

Credit: Bernard Marr

The artificial intelligence revolution is no longer creeping forward—it’s sprinting. In my recent conversation with Brett Roscoe, Senior Vice President and General Manager of Data Governance and Cloud Operations at Informatica, we explored the findings from their CDO Insights 2025 report that reveal both the explosive growth in AI investment and the obstacles companies are encountering on their journey.

What struck me most during our discussion wasn’t just the sheer scale of financial commitment—with 87% of companies increasing their generative AI investments in 2025—but the widening competitive chasm between early adopters and those hesitating to embrace AI’s transformative potential.

The Investment Stampede Is Real

The enthusiasm for generative AI isn’t just talk—it’s backed by serious capital. As Roscoe explained during our conversation, “87 percent of our customers… are increasing their investments in gen AI in 2025. Now that was already on top of what they’ve done to increase it in 2024.”

This investment surge isn’t happening in isolation. Alongside their AI spending, 86% of organizations are simultaneously boosting their investment in data management practices to support these initiatives. This dual-track approach signals a growing recognition that successful AI implementation requires robust data infrastructure.

What are companies hoping to achieve? According to Roscoe, “the biggest areas were operational efficiency… and enhancing customer experiences or employee experiences.” These goals are already materializing in companies like CN Rail, which has slashed data preparation time from months to just two weeks, creating the foundation for accelerated AI project development.

The Early Adopter Advantage Is Widening

Perhaps the most compelling insight from our conversation was the growing evidence that AI early adopters are pulling ahead—and fast. As Roscoe noted, “Early adopters of AI had a big advantage. In fact, to the point where these early adopters saw something like 35 percent cost decreases and 58 percent growth attributed to their Gen AI projects.”

This isn’t incremental improvement—it’s transformative change. When McKinsey surveyed organizations already deploying generative AI, they found that, on average, these companies attributed 20% of their EBITDA to their AI initiatives. Numbers like that aren’t just competitive advantages—they’re existential threats to laggards.

I’ve observed this dynamic firsthand across industries: the gap between AI leaders and followers isn’t stable—it’s expanding daily. Every organization that doesn’t see itself as AI-enabled is falling further behind those that do. This creates a powerful incentive for companies to push ahead despite challenges.

The Hidden Obstacles To AI Success

While investment enthusiasm runs high, implementation reality is more sobering. The CDO Insights 2025 report revealed that a staggering 97% of organizations struggle to demonstrate business value from their generative AI investments—a significant roadblock to securing continued funding and executive buy-in.

Technical challenges are equally prevalent, with 92% reporting that issues like data quality, responsible AI use, and compliance concerns are delaying the progression from proof-of-concept to production. As Roscoe emphasized, companies are grappling with questions like: “How will this AI model behave? What risks are associated with putting this into production?”

In my experience working with organizations across sectors, I’ve observed additional barriers: executive teams not thinking ambitiously enough, scattered pilot projects without strategic cohesion, leadership knowledge gaps, skills shortages, cultural resistance, and data silos that prevent AI from reaching its potential.

According to recent research, 67% of business leaders believe that generative AI will bring significant change to their organizations over the next two years.

But in the rush to adopt and deploy this world-changing technology, it’s pretty likely that mistakes will be made.

The downside of this enormous potential is that when things go wrong, the damage can be quite serious too, from reputational harm to harsh fines and, perhaps worst of all, loss of customer trust.

So here’s my overview of five of the most common mistakes that I believe many businesses and business leaders will make in the coming year so you can plan to avoid them.

Omitting Human Oversight

Powerful and transformative as it undoubtedly is, we can’t ignore the fact that generative AI isn’t always entirely accurate. In fact, some sources state that factual errors can be found in as many as 46 percent of AI-generated texts. And in 2093, the tech news website CNET paused the publication of AI-generated news stories after having to issue corrections for 41 out of 77 stories. What this means for businesses is that proofreading, fact-checking and keeping a human-in-the-loop is essential if you don’t want to run the risk of making yourself look silly.

Of course, humans make mistakes, too, and any business involved in information exchange should have robust procedures for verification regardless of whether they use generative AI or not.

Substituting GenAI For Human Creativity And Authenticity

Another mistake I am worried we will see far too frequently is becoming over-reliant on genAI as a substitute for human creativity. This is likely to have negative consequences on the authenticity of a business or a brand voice. While it’s easy to use ChatGPT or similar tools to churn out huge volumes of emails, blogs, social media posts and suchlike super-fast, this frequently leads to overly generic, uninspiring content that leaves audiences feeling disconnected or even cheated. Video game publisher Activision Blizzard, for example, was recently criticized by fans for using “AI slop” in place of human-created artwork. It’s important to remember that generative AI should be used as a tool to augment human creativity, not to replace it.

Failing To Protect Personal Data

Unless a generative AI application is run securely on-premises on your own servers, there’s often no real knowing what will happen with the data entered into it. OpenAI and Google, for example, both state in their EULAs that data uploaded to their generative chatbots can be reviewed by humans or used to further train their algorithms. This has already caused problems for some organizations – Samsung stated that its employees had inadvertently leaked confidential company information by entering it into ChatGPT without being aware of the consequences. Incidents like this create a risk for companies that they will end up in breach of data protection regulations, which can lead to severe penalties. This is likely to be an increasingly common occurrence as more and more companies start using generative AI tools, and organizations – particularly those that handle personal customer data at scale – should ensure staff are thoroughly educated about these dangers.

Overlooking Intellectual Property Risks

Many commonly used generative AI tools, including ChatGPT, are trained on vast datasets scraped from the internet, and in many cases, this includes copyrighted data. Due to the lack of maturity in AI regulations, the jury is still out on whether this constitutes a breach of IP rights on the part of AI developers, with several cases currently going through the courts. The buck might not stop there, however. It’s been suggested that businesses using genAI tools could also find themselves liable at some point in the future if copyright holders manage to convince courts that their rights have been infringed. As of now, failing to assess whether AI-generated output could contain copyright or trademark-infringing materials is likely to land businesses in hot water in 2025, if they aren’t taking proactive measures to make sure it doesn’t.

Not Having A Generative AI Policy In Place

If you want to minimize the chances that anyone working for your organization makes any of these mistakes, then probably the best thing to do is to tell them not to. The potential use cases for genAI are so varied, and the opportunities it creates are so vast that it’s almost certainly going to be misused at some point. Perhaps the most important single step you can take to reduce the chance of that happening is to have a clear, defined framework in place setting out how it can – and can’t – be used.

As far as I’m concerned, this is a no-brainer for every organization that stops short of a blanket ban on generative AI, which would be a pretty big mistake, given the opportunities it creates. Without such a policy in place, you can almost guarantee it will be used without appropriate oversight, overused to the detriment of human creativity, and lead to unauthorized disclosure of personal data, IP infringement, and all the other mistakes covered here.

To wrap up – in 2025, we will see organizations take huge steps forward as they become increasingly confident, creative and innovative in the way they use generative AI. We will also see mistakes. Being fearful of the transformative potential of generative AI will most likely hand the lead to our competition, but adopting a careful and cautious approach can save us from costly mistakes.

Quantum computing is shaping up to be among the most transformative technologies of our era.

While still in their infancy, these powerful machines are expected to help us solve many problems by accelerating the speed at which we can process certain types of data by a factor of hundreds of millions.

But not all quantum computers are the same. Researchers are working on many different ways to apply principles of quantum mechanics to computing technology. This has led to a variety of methods, architectures and paradigms, all suited for different use cases or tasks.

So here I’ll overview some of the different categories, giving a brief explanation of what makes each one unique and what it’s hoped they will achieve.

First, What Is Quantum Computing?

Just in case you’re completely new to the topic – quantum computing refers to a new approach to computing that harnesses some of the strange and powerful properties of quantum mechanics, such as entanglement and superposition. Instead of using traditional “bits” (ones and zeros) like a classical computer, quantum computers use “qubits” that are spookily able to exist in more than one state simultaneously. This means they can potentially solve some very complex mathematical problems, such as those involving optimization problems or simulating complex real-world systems like molecular physics – far faster than existing computers.

So What Are The Different “Types” Of Quantum Computers?

Several distinct quantum computing methodologies have emerged, each leveraging quantum properties in different ways, making them suitable for carrying out different types of computation. Here’s an overview of some of the most popular:

Quantum Annealing

This is a quantum computing methodology that’s particularly well-suited to solving optimization problems. These are computations that require finding the best combination of a large number of variables. It can be of use in real-world scenarios ranging from planning the most efficient route for multi-drop delivery drivers to optimizing stock portfolios. D-Wave is recognized as a leader in this field of quantum computing and has worked with companies, including Volkswagen, to create systems that use annealing methodology to optimize assembly line packaging operations and delivery logistics.

Superconducting Quantum Computers

One of the most mature quantum computing methods involves building circuits from superconductive materials such as niobium or aluminum, cooled to near absolute zero temperatures. This allows qubits to exist in superposition states of both one and zero simultaneously, where they can be manipulated by microwaves. In simple terms, this lets them carry out computational logic operations (and/or/not etc) in a way that lets them explore multiple possible solutions to a problem in parallel, rather than one at a time. Superconductive quantum computing is being pioneered by companies such as IBM and Google and has real-world applications in drug discovery, artificial intelligence, and encryption.

Trapped Ion Quantum Computers

This involves using positively charged atoms (ions) trapped and held within a 3D space in a way that entirely isolates it from the outside world. This means that it can be held in its superposition state for a very long time rather than decohering into one or zero. Lasers are used to switch the ions between different states as required for calculations, as well as to retrieve the information that forms the “answer” to the question that needs to be solved. Leaders in this field of quantum computing include IonQ, which has worked with the United States Air Force to create secure quantum networking technology for communicating between drones and ground stations.

Photonic Quantum Computers

This involves harnessing photons, which are light waves, and manipulating them using optical components like beam splitters, lenses and mirrors. Having no mass, light waves are not affected by temperature. This means that photonic quantum computing doesn’t require super-low temperatures and a specially configured environment. Another benefit of being light beams is that the qubits encoded in photons can maintain their coherence over relatively long distances. Real-world applications for it have been found in quantum cryptography and communications, and leaders in the field include Xanadu.

Where Next For Quantum?

Although real-world use cases for quantum computing are increasing, much of the work in the field is still purely hypothetical, and various other methods are under development in labs and academic institutions.

Other research is focused on reducing the error rate of quantum computing caused by the delicate nature of qubits held in a quantum state.

It’s also worth noting that most quantum computing taking place today involves a hybrid model of quantum and classical methodologies.

As research and development continue, there’s no doubt we’ll start to see more breakthroughs in the journey towards practical, scalable and useful quantum computing.

AI promises to make businesses of all shapes and sizes more efficient, more innovative, and better prepared to deliver whatever their customers want.

However, it will also inevitably expose them to new dangers and risks.

In fact, the risks of implementing new technology often scale in line with the potential for positive transformation – and with AI, that potential is huge.

So here are some of the biggest mistakes that I believe businesses will make this year as they race to implement it:

1. Failing To Align AI Strategy With Business Strategy

One of the mistakes I see many businesses making is rushing to adopt AI simply because they’ve been told they should do so without understanding how it fits with their overall goals.

This approach – putting technology before business strategy – is probably the number one driver of failed AI initiatives and – worse still – disillusionment and giving up on AI altogether.

It’s easy for us to get so excited by the hype around AI that we rush out and start spending money on tools, platforms and projects without aligning them with strategic goals and priorities. This inevitably leads to fragmented initiatives that fail to deliver meaningful results or ROI. To avoid this, always “start with strategy” – implementing a strategic plan that clearly shows how any project or initiative will progress your organization towards improving the metrics and hitting the targets that will define your success.

2. Underestimating The Impact Of AI On The Workforce

Implementing AI is likely to mean big and possibly frightening changes for the people who make your business tick.

While it won’t mean they all become redundant, it may mean they need to learn new skills or adapt to new ways of working. Assessing the skills and possibilities of training or reskilling, ensuring there is buy-in across the board, and addressing concerns people might have about job security are all critical.

Many businesses will make the mistake of thinking solely about the technical steps they have to take while forgetting that humans will still ultimately be responsible for success or failure – and this is a serious error.

3. Giving Up Too Quickly – Or Too Slowly

Not every AI project is going to work – in fact, recent Gartner research puts the current failure rate of AI initiatives at around 85%. But getting it wrong the first (or second, or third) time isn’t necessarily a reason to give up. I firmly believe that just about any business can benefit from AI, but that isn’t the same as believing that they will benefit right away or that all they have to do is launch an AI project and they’ll immediately be successful.

On the other hand, being slow to pull the plug on projects that aren’t working out can also be a recipe for disaster – potentially turning what should simply be a short, sharp lesson into a long-term waste of time and resources. There’s a reason that “fail fast” has become a mantra in tech circles. Projects should be designed so that their effectiveness can be quickly assessed, and if they aren’t working out, chalk it up to experience and move on to the next one.

4. Failing To Properly Assess The Cost

Like the previous trap, this one is also two-pronged – it can be easy to both overestimate and underestimate the cost of developing AI initiatives, and both can be problematic.

Make no mistake, going full-throttle on AI is expensive – hardware, software, specialist consulting expertise, compute resources, reskilling and upskilling a workforce and scaling projects from pilot to production – none of this comes cheap. The question of how much it costs to get an enterprise AI initiative off the ground is similar to “How long is a piece of string?” but most estimates go into the millions.

At the same time, lots of smaller businesses will be put off because they think AI is only for big companies with huge IT budgets, ignoring the fact that there are ways to implement AI cost-effectively while still providing real benefits.

Again, the answer is thorough preparation and being as accurate and diligent as possible in your costing before jumping headfirst into AI.

5. Letting The Competition Pip You To The Post

Of course, despite all of the precautions covered here, perhaps the biggest single mistake would be deciding it’s too difficult, risky and expensive and choosing to sit out the AI revolution entirely.

Make no mistake: AI is set to transform every industry, and those who do stick their heads in the sand and pretend it isn’t happening are going to be left behind.

AI will make businesses more efficient, meaning those that don’t implement it at all are effectively throwing away money. But it will also drive innovation, meaning competitors that do adopt it will create new products and services, redefining customer expectations and leaving laggards looking decidedly old-hat.

Don’t Fall Into These Traps

I know from my experience of working with companies of all shapes and sizes on digital innovation strategies that these hazards and pitfalls will cause many businesses to experience big problems in 2025.

Understanding the risks and having the foresight to see them before falling into them headfirst is the key to success, as well as the secret to delivering AI-driven growth and transformation.

By aligning AI projects with business goals, ensuring your people are included in everything you do, accurately assessing costs, and knowing when to press ahead and when to quit, you give yourself the best chance of avoiding making these mistakes.

This will result in AI projects that deliver better returns and, perhaps more importantly, prepare your organization for the even bigger opportunities that are yet to come.

ChatGPT’s new Operator mode is its first step towards becoming an AI agent – a new type of AI tool that carries out far more complex tasks without the need for human intervention.

But what exactly can it do?

Currently, in preview mode, the latest upgrade to ChatGPT works by combining the GPT4o reasoning model with computer vision capabilities. This lets it “see” and interact with anything on a screen.

It does this with the help of a built-in web browser, and after telling it what we want it to do, you can just sit back and watch as it moves the mouse, presses buttons and inputs text.

So what can it do? Well, just from playing around with it, it’s evident that it is still at an early stage of development. Nevertheless, it has some impressive tricks up its sleeve.

Here’s an overview of some of the tasks I’ve seen it perform so far, as well as a look ahead to what this could mean for the future of “agentic” AI.

How To Access ChatGPT Operator?

First off, ChatGPT Operator is only available in the US right now and only to users who’ve taken out the $200-per-month Pro subscription.

This probably won’t always be the case – ChatGPT creators OpenAI tend to roll out new features to small groups such as Pro users first before opening them up to a broader audience.

But if you are lucky enough to meet those criteria, then you should be good to go. Simply head to operator.chatgpt.com to get started.

Things To Do With Operator

Operator is designed to carry out more complex, multi-step tasks than is possible using standard ChatGPT. It’s capable of carrying out up to three of these tasks at the same time.

While it’s designed to be autonomous, there are times when it will have to hand control back to you – for example, to log into websites or to solve CAPTCHA challenges.

One really useful feature is integrations. These are instructions on using specific sites or services, such as Airbnb or OpenTable, so Operator doesn’t have to learn how to use them from scratch every time it comes across them. Since these integrations are created using natural language prompts, businesses can easily develop and share their own with customers.

Here are some of the things it can already do:

Find And Book Accommodation Through Airbnb

Tell it to head to Airbnb to find a room based on your preferences, and it will search options, check reviews, and ensure you’re happy with its choice before going ahead with the booking.

Sample prompt: “Find an Airbnb room in [destination] from [check-in date] to [check-out date] for [number of guests]. Prioritize properties with good reviews, Wi-Fi, and amenities like [list preferred amenities, e.g., kitchen, balcony, pet-friendly]. Ensure the location is close to [landmarks or areas, if important]. Confirm availability and details before booking.”

Make Restaurant Reservations

Operator integrates with OpenTable and can scan services like TripAdvisor to research and book restaurant reservations.

Sample prompt: “Find a highly-rated restaurant near [location or city name] that serves [preferred cuisine or dietary preferences, e.g., Italian, vegan-friendly]. Book a table for [number of people] on [date] at [time]. Ensure the restaurant has good reviews and confirm the reservation before finalizing.”

Book Tickets To An Event

Want to watch live music or sports or take in a show? Operator will browse events directories and integrate directly with StubHub to find the best seats at the best prices.

Sample prompt: “Search for events happening near [your location] on [specific date or date range, e.g., February 10th or next weekend]. Include concerts, theater shows, festivals, or anything unique and interesting. List options with event details, times, and ticket prices. Once I choose one, book the tickets for [number of people] and confirm the booking.”

Plan And Shop For Meals

Not only will Operator plan your menu, but it will integrate with Instacart to order the ingredients and have them delivered to your door.

Sample Prompt: “Plan meals for a family of [number of people] for a week (breakfast, lunch, dinner). Take into account the following dietary requirements: [list requirements, e.g., gluten-free, vegetarian, nut-free, low-carb]. Include a mix of healthy and family-friendly recipes. Once planned, order the ingredients online and ensure delivery is scheduled for [preferred delivery date and time]. I already have [insert staples, e.g., milk, sugar, flour], so you don’t need to order them.”

Update Or Make Changes To A Website

How about something more challenging? From uploading blog pages to changing design elements and generating entirely new content, Operator can integrate with no-code building platforms like Wix to get the job done. While building an entire site from the ground up might be a little taxing (at the moment), it can carry out routine maintenance, design tweaks and updates with relative ease.

Sample Prompt: “Edit the website [website URL or description of the page] to update the following: [describe what you want to be changed]. Log into [insert no-code service, e.g., Wix] to make the changes. Ensure the design remains consistent and user-friendly. Once edits are made, show me the final result for review.”

The Start Of Something Big?

Although it’s early days, I believe that Operator could mark the start of a new chapter in the history of AI. Possibly even a change in the fundamental relationship between humans and machines.

Agentic AI is the first step towards the creation of truly autonomous thinking machines that can act as assistants to us in all fields of life.

Some believe it’s a step towards artificial general intelligence (AGI) – the “holy grail” of AI where machines can learn just like we do in order to perform tasks beyond those they’ve been programmed for.

The arrival of ChatGPT just two short years ago marked a watershed moment for AI. Now, the arrival of agentic AI marks another – and I’m just as confident it’s going to change the world in ways that are hard to imagine today.

ChatGPT just launched a new feature that, alongside OpenAI’s groundbreaking Operator agent, signals a future where AI is far more capable than today.

Tasks let users schedule actions to be carried out in the future or at regular intervals. While Operator can actively browse the web and complete complex tasks independently, Scheduled Tasks focuses on automated, time-based interactions.

A simple idea, sure – but one that complements OpenAI’s broader push toward agentic AI – autonomous AI that can fulfill complex, multi-step objectives without needing human input. This vision is already taking shape with OpenAI’s Operator agent, which can independently navigate websites and complete tasks on users’ behalf.

For now, though, ChatGPT users can get used to interacting with the scheduled task feature that can help us with day-to-day activities even when we’re not online.

What Are ChatGPT Scheduled Tasks?

The concept is pretty straightforward – Tasks lets us schedule prompts to be run in the future or at regular intervals, such as once a day, then wait for the results to be relayed back to us by notification or email.

What makes it exciting is that it’s part of OpenAI’s broader initiative to create truly autonomous AI assistants. While Operator handles real-time web interactions, Scheduled Tasks manages time-based automations – both representing different aspects of AI working independently on our behalf.

How To Use Scheduled Tasks

At the moment, Tasks are only available to users with a paid ChatGPT Plus, Pro or Teams subscription.

To get started, just click your profile picture and select Tasks, or select ChatGPT With Scheduled Tasks from the drop-down Model menu. OpenAI has created a page with more detailed instructions, including how to set up push notifications, on its website here.

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Five Helpful Ways To Start Using Scheduled Tasks Today

Just to give you an idea of the sort of thing it can do, here are five simple prompts that can be used to get regular ongoing assistance with day-to-day activities.

Personalized Morning Weather And Travel Briefing

This prompt sends helpful daily updates before you leave the house:

Every morning at 7 am, provide me with a weather report for [my location] as well as a travel report covering disruption on any local roads or transport networks, for the current day only.

Create Topical Social Media Posts For A Business

Finding ideas for social media posts can eat into your valuable time, particularly  if you’re running a business by yourself:

At 9 am every day, please generate the text for a social media post for my [insert type of business]. It should relate to something topical connected to my business and should either promote the importance of the products and services we supply or educate or inform the customer about something of interest to them.

Create A Personalized To-Do List, With Reminders

This will help you organize your day and keep track of jobs you need to get done:

Every day at 8 am, ask me what tasks I need to get done that aren’t already on my list. Generate a list of all my ongoing tasks along with tips to help me get them done. Remove tasks from my list as I tell you I’ve completed them.

Keep Up-To-Date On Topics That Matter To You

News briefings on any subject you want:

Every morning at 7 am, send me the latest news and content on the subject of [insert subject].

Meal Planning

Create a customized meal plan for the week:

Every Monday at 9 am, send me a weekly meal plan consisting of seven healthy dinners for a family of [insert family size] and provide a shopping list of all the ingredients I’ll need, broken down into categories.

Towards AI Agents?

We’re already seeing OpenAI’s vision of agentic AI taking shape. While Scheduled Tasks handles time-based automations, Operator demonstrates more advanced capabilities – actively browsing the web, making decisions, and completing complex tasks independently.

The Scheduled Tasks feature may be in beta, but combined with Operator, we can see OpenAI’s roadmap emerging. Future versions might integrate these capabilities – imagine scheduling an Operator task to automatically book your favorite restaurant every Friday or having it monitor prices and purchase items when they go on sale.

This integration would move OpenAI’s technology well beyond simply answering questions and giving advice. With Operator already handling web-based tasks and Scheduled Tasks managing time-based automations, we’re watching the evolution of AI into truly useful everyday assistants that can work independently on our behalf.

From creating enticing job descriptions to quickly sifting and assessing candidates, there are many ways that ChatGPT and similar generative AI tools can help HR and recruiters work smarter.

The secret lies in creating killer prompts – the instructions that tell chatbots and language models what we expect them to do. Just like when we’re asking humans to help us, the more precise we are, the more likely it is that the AI will know exactly what we want to do.

Prompt writing – sometimes called prompt engineering – is increasingly becoming a valuable skill in many areas of business. So, if you’re involved in HR or recruitment, here are some examples of prompts that can be used to help save time and cut repetitive work.

If you find them useful, you can use them as building blocks for creating your own prompts to help out with all sorts of tasks.

Conduct A Skills Gap Analysis

Identify areas where your organization may have to make new hires or upskill existing employees to meet its future needs:

I need your help to identify where there are skills gaps in our workforce that are preventing us from meeting our business goals. I will provide an overview of our strategic business priorities, as well as a skills assessment of our workforce and leadership teams. When you have enough data, use the information to generate a report highlighting areas where we should focus on recruitment or reskilling in order to achieve our objectives.

Write Great Job Descriptions

This prompt will take you through the steps of creating a job description that will attract candidates for any vacancy:

Act as a recruitment copywriter and help me write a job description optimized for generating applications. Start by asking me all the necessary details one question at a time (e.g., title, summary, location, responsibilities, skills, organization fit, growth opportunities, compensation, and benefits). Once you have enough information, craft a clear, professional, and engaging description using active language, bullet points, relevant keywords, and a strong call to action to improve search visibility.

Review Applicant’s CVs And Prepare Interview Questions

This prompt compares CVs against a job description, highlights how well they fit the role requirements and suggests questions to ask them at the interview. Be careful to comply with any data protection laws that apply to your business, for example, by removing personal data where necessary:

Please help me screen the CVs of job applicants. The job we are screening for is [insert job title], and this is the job description [paste job description]. I will upload CV files, and you will provide a summary of how each applicant matches up to the skills, experiences, and qualifications required for the role. Based on those insights, please also provide three questions I can ask the candidate in an interview to further assess their suitability.

Communicate A Company’s Enterprise Value Proposition

An Enterprise Value Proposition (EVP) outlines the benefits and opportunities that your company offers its employees. This prompt helps create material to let potential applicants know why it’s a great place to work:

Please help me create engaging EVP content for my company, [insert company name], which effectively and concisely communicates why it is a great place to work and is designed to help us be attractive to top talent. Ask questions, one at a time, to gather the information you need, and when you have enough, generate copy that can be used on our website, recruitment portals and social media channels.

Review Recruitment Processes To Identify And Remove Bias

This prompt helps identify and eliminate potential sources of bias in your hiring processes, ensuring your recruitment practices are fair and inclusive for all candidates:

Please act as a recruitment specialist and review our recruitment processes to ensure they are inclusive and free of bias. Ask me questions, one at a time, or ask me to provide information about any element of our recruitment processes that you need to know about in order to identify opportunities for bias to affect our recruitment outcomes. If you identify areas of concern in relation to the potential for bias, you can ask follow-up questions to clarify or ensure you have a full overview of the existing process. When you have enough information please provide an evaluation as well as advice on how we can further remove bias and achieve more inclusive outcomes.

As organizations continue to embrace AI tools in their HR and recruitment processes, the key to success lies in crafting effective prompts that align with your specific needs and objectives. By adapting and building upon these prompts, you can create a more efficient, inclusive, and strategic approach to talent acquisition and management. Remember that while AI is a powerful tool, it works best when combined with human insight and expertise.

If I told you that AI was going to make humans redundant and cause widespread unemployment, you’d probably think that’s a bad thing, right?

But what if the reality is that it’s going to make work unnecessary – and provide all the basic necessities for us to live comfortable lives without us having to work?

That’s the thinking behind the idea that AI will be the catalyst for the introduction of universal basic income (UBI).

The logic goes something like this: If AI can write software, draft legal documents, drive cars and diagnose illness today, what will it be capable of in 20, 30 or 50 years time?

Advocates argue that not only will AI make it necessary to provide some form of UBI, but it will also be the technological leap that makes it possible. But does this idea hold water, or is it just far-fetched (and highly optimistic) thinking?

So What Is Universal Basic Income?

UBI refers to unconditional payments made to citizens designed to cover or contribute towards the basic cost of living. The idea goes back to the first Industrial Revolution, when there were worries that industrialization would lead to large-scale human unemployment, resulting in civil unrest.

Although that isn’t exactly how things turned out, many countries did subsequently implement various forms of social welfare programs to provide assistance to those on low or no income. This was to try to reduce extreme poverty and the associated problems that come with it.

UBI is different from welfare, however, as it’s available to everyone regardless of their wealth, employment status or income. Proponents say that ensuring a basic standard of living for everyone would alleviate many other problems, including ill health, crime and homelessness. Critics, however, say it could lower the incentive to work and result in a decline in enterprise and productivity.

Experiments with UBI in the US go back as far as the 1960s, and more recently, there have been limited-scale pilots in countries including Finland and Canada.

Today, however, the dawn of the AI age has once again surfaced fears of widespread technology-driven redundancy and unemployment. However, it’s also been posited that the arrival of intelligent machines will make it possible to build the infrastructure necessary for a “post-work” society and administer the vastly complex financial framework needed to make it work fairly and efficiently.

Is AI The Answer?

Ok, well, first of all, we have to acknowledge that this is a rather utopian outlook. To believe that it could work, we’d have to take it for granted that many of the challenges currently associated with AI – like hallucination, model collapse and the sustainability problem – will be solved, which is far from a given. But let’s pretend for a moment.

In this hypothetical near future, where AI is reshaping economies and automating many tasks that could previously only be done by humans, productivity is optimized, waste is eliminated, and workflows are streamlined. In short, this means more output is created for less effort, leading to a surplus of value.

This value, so the theory goes, can then be reinvested in social programs like UBI. Imagine, for example, a world where all trains drive themselves. Currently, a lot of the money invested in their businesses by train companies goes towards paying drivers. Additionally, they have to build train cabins that are comfortable for humans to sit in for lengthy periods, implement health and safety measures to keep humans safe, provide them with facilities for their rest and downtime, and so on.

By eliminating this expense, the train company becomes more profitable and, in a future where UBI is a reality, contributes towards providing the now redundant driver (and everyone else) with a basic standard of living.

However, the potential of AI goes beyond making UBI possible (and indeed necessary) because it could also play an important role in its administration. Large-scale social programs usually require a vast administrative framework and comprehensive oversight to ensure they operate fairly, don’t infringe on human rights or privacy, and aren’t compromised by fraud or corruption. Traditionally, this involves implementing expensive and often inefficient layers of human bureaucracy.

AI, however, could automate many of these functions. For example, it could verify eligibility, route payments to the correct recipients, and detect fraud, cutting the need for labor-intensive administration and oversight.

Hype Or Reality?

So, while in theory, and given an optimal set of circumstances, it seems that AI could make UBI both necessary and possible – what about in reality?

Well, for a start, there’s uncertainty over exactly how widespread AI-driven unemployment and redundancy will be. Many – including the WEF – predict that while it will lead to the replacement of humans in many roles, new roles for us will also emerge.

Then, there will be political challenges to overcome. As previously mentioned, some are against the “something for nothing” philosophy behind UBI out of fear it will undermine economic productivity and disincentivize work. It’s uncertain whether there would be the will to push the kind of changes that would be necessary through political systems, even if technology does make it possible.

And then there’s the question of whether AI will ever actually be good enough to live up to the claims made about it. Will problems with hallucination and model breakdown be overcome, and will society at large trust it enough to hand over significant control of our industrial and economic activity?

Ultimately, while the dream that humanity will be freed from the shackles of labor and freed to pursue a life of leisure and creativity is attractive, the leap to this utopian state is far from guaranteed.

But that isn’t to say it’s impossible, and if we manage to solve the technological, societal and political factors, AI could be the key to building a world where poverty and destitution are a thing of the past.

At a time when the AI revolution is sweeing through most aspects of business, one area that has remained surprisingly untouched is business agreements and contracts. That’s about to change, as DocuSign, the company that brought electronic signatures into the mainstream, is now leveraging AI to revolutionize how businesses create, manage, and extract value from their agreements.

The Hidden Problem With Modern Agreements

While we’ve digitized countless business processes, agreements have largely remained stuck in the past. Yes, we edit them in Word and email them around, but as DocuSign’s CEO Allan Thygesen explains, “Everything about agreements remains as brittle, delayed, and unpredictable as it’s ever been.”

The problem goes deeper than just inefficient processing. As Thygesen points out, “Once you negotiate the agreement, the strange thing is you spend all this time on that, and then you put it in a deep, dark place, and there’s no visibility into what’s actually in the agreement.” This lack of visibility means companies often miss crucial deadlines, renewal opportunities, and chances to improve their agreements.

How AI Is Transforming Agreement Management

DocuSign’s approach to solving this problem coincided with perfect timing. “I joined DocuSign just as there was a step change in what we could do with AI, right around the time when GPT 3.5 launched,” shares Thygesen. This technological breakthrough has enabled DocuSign to transform unstructured agreement data into actionable intelligence.

The company’s AI-powered platform can now extract essential data from agreements, make it searchable, and compare it against actual outcomes from various business systems. But it doesn’t stop there. The AI can also assist in creating agreements, customizing templates, and even performing initial legal reviews of incoming contracts.

Real-World Applications That Are Changing Business

The impact of this technology is already being felt across various business functions. Thygesen highlights three key areas where their AI-powered platform is making a significant difference:

In sales, the platform enables businesses to track renewal dates, notice periods, and opportunities for renegotiation. This prevents missed opportunities and empowers sales teams with crucial information that was previously buried in agreements.

For procurement teams, who are typically resource-constrained, the AI helps manage vendor relationships more effectively. “Procurement teams are typically fairly small,” Thygesen notes. “Having tools that can make them more productive is very important.”

In HR and recruitment, where there are many employment contracts that need management and updating, the platform can streamline high-volume processes while ensuring compliance. It enables quick customization of agreements and packages while maintaining regulatory compliance.

The Future Of AI-Powered Agreements

Looking ahead, DocuSign envisions a future where AI could potentially handle entire agreement processes autonomously, particularly for simpler documents. “I think it’ll be technically possible to do that with higher accuracy for simple agreements in fairly short order,” Thygesen predicts. He suggests that standardized documents like NDAs could be among the first to see full automation.

However, Thygesen maintains a balanced perspective about AI’s role: “For a variety of reasons, including risk compliance, regulatory and others, I think it’ll be a while before anything but the most trivial agreements get released. I think there’ll always be a human in the end, at a minimum.”

The Bigger Picture

DocuSign’s ultimate vision is ambitious yet practical. “If we’re successful, we will develop the first system of record for agreements,” says Thygesen. This would replace the current scattered approach where agreements are lost in email threads or buried in various digital drives.

The transformation is already underway. With 1.6 million monthly paying business entities, DocuSign is well-positioned to lead this revolution. The evidence of progress is striking: Thygesen reveals that their “costs to process an agreement have dropped by two orders of magnitude in the last 15 months” thanks to continued technological advancement.

A New Chapter In Business Efficiency

As businesses continue to seek ways to improve efficiency and reduce costs, DocuSign’s AI-powered approach to agreement management represents a significant leap forward. By turning static documents into dynamic, intelligent assets, they’re not just solving a technological problem – they’re addressing a fundamental business challenge that affects organizations of all sizes.

The future of business agreements is being rewritten. But as with all significant technological advances, the key to success will lie in finding the right balance between automation and human oversight, between efficiency and control, and between innovation and reliability.