Artificial Intelligence (AI) is no longer a futuristic concept—it’s already here, and its capabilities are evolving at breakneck speed. Businesses are at the centre of this transformation, and those that embrace AI will undoubtedly witness profound shifts in how they operate. The role of AI agents is now emerging as the key to unlocking real, measurable returns on investment (ROI) from AI initiatives. As organisations seek to streamline operations and improve efficiencies, the introduction of AI agents will revolutionise the way we conduct business.

By 2030, between 400 million and 800 million jobs globally could be displaced by automation and AI, including AI agents. This represents a significant shift in the global workforce, with millions of people potentially needing to switch occupational categories and learn new skills​ (McKinsey & Company)​(McKinsey & Company).

AI agents are more than just tools—they are composite, autonomous applications that enable human-AI collaboration to complete tasks. Unlike the chatbots and virtual assistants that have become commonplace in recent years, AI agents are equipped with more advanced capabilities that allow them to handle complex workflows and integrate seamlessly with existing systems. This shift will dramatically change how businesses function, improving efficiency, lowering costs, and ultimately driving better ROI.

The Speed of Change: AI is Coming Faster Than You Think

The pace of change in AI is astonishing, and many business leaders may not yet fully grasp its scale or speed. This rapid acceleration is reminiscent of previous technological revolutions, such as the introduction of personal computers or the internet, both of which fundamentally altered the way we live and work. But AI promises to be an even more profound transformation.

As we look at the landscape today, companies like Google, Salesforce, and Microsoft have already begun integrating AI agents into their platforms, allowing businesses to automate a wide range of tasks. These AI agents can perform functions like gathering data, preparing reports, and even planning events, all without the need for constant human intervention. This not only saves time but also allows employees to focus on higher-level tasks that drive innovation and growth.

Despite the clear advantages, there is still a degree of uncertainty among the general public about the true impact AI will have on the workforce. While some worry about job displacement, it’s crucial to understand that AI, and AI agents in particular, are designed to work alongside humans rather than replace them entirely. The future of business will be characterised by collaboration between human intelligence and AI-driven automation, unlocking new opportunities for growth.

What Exactly Are AI Agents?

To understand the significance of AI agents, it’s essential to differentiate them from more basic AI tools like chatbots. While chatbots are designed to respond to specific user commands, AI agents go further by taking additional actions based on those responses, often autonomously. They are “composite” applications, meaning they integrate multiple reasoning and action capabilities into one system. For example, an AI agent handling a loan application could perform a credit check, analyse user inputs, and compare loan terms across various banks, all while crafting a tailored loan proposal.

In business, the value of AI agents lies in their autonomy. Many customer-facing processes, such as responding to frequently asked questions or processing standard transactions, can be handled by AI agents with minimal human oversight. By automating 80% of routine tasks, these agents free up employees to focus on the 20% of exceptions that require more nuanced, human judgment.

This autonomy and flexibility make AI agents an invaluable tool for improving ROI. Instead of relying on human labour for repetitive tasks, businesses can delegate these workflows to AI agents, allowing their workforce to engage in more creative and strategic activities that drive revenue.

How AI Agents Are Redefining ROI Measurement

One of the biggest challenges in AI adoption has been measuring its impact on ROI. Business leaders have long sought to understand the tangible returns on their AI investments, and AI agents are the key to making this possible. Because agents are built with specific workflows in mind, their effectiveness can be directly tied to business KPIs. For example, an AI agent automating data collection for a quarterly report can be measured by the time it saves and the accuracy it provides, both of which can be quantified in financial terms.

Moreover, these agents are designed to be accessible to non-developers. Low-code and no-code platforms like Salesforce’s Agentforce allow business users with minimal technical expertise to create their own agents. This democratisation of AI agent creation will enable more departments across an organisation to leverage AI, ultimately driving more value and higher returns.

AI agents also solve one of the biggest challenges faced by early AI adopters: context. By integrating with existing business systems and drawing from predefined data sets, agents can provide more accurate and relevant results than public AI chatbots. This ensures that the information they deliver is not only precise but also aligned with the specific needs of the business.

The Long-Term Impact: A Positive Outlook for the Future

While concerns about job displacement are valid, it’s important to approach the AI revolution with optimism. History shows us that every major technological advancement, from the industrial revolution to the digital age, has led to the creation of new industries and jobs. The rise of AI will be no different. While some roles will be automated, AI will also create new opportunities in areas like AI development, maintenance, and oversight.

Furthermore, AI agents will not replace entire jobs; rather, they will take over specific tasks, freeing up employees to engage in more valuable work. This shift will allow businesses to operate more efficiently while giving their workforce the ability to focus on innovation, problem-solving, and customer engagement. In many ways, AI will enable employees to do their jobs better, not replace them.

As we head towards 2025, we can expect to see rapid advancements in AI agent technology. Early adopters in industries such as finance, healthcare, and retail will lead the charge, using AI agents to automate complex workflows and improve customer experiences. However, the benefits of AI agents won’t stop there. Over time, they will become more widespread, and their use cases will expand into virtually every industry.

What Businesses Should Do Now

For businesses that haven’t yet started integrating AI agents, now is the time to act. The AI revolution is happening quickly, and those that don’t adapt risk falling behind. Here are three key steps businesses can take to prepare for the coming changes:

1. Start Small, Then Scale: Begin by piloting AI agents in specific departments or workflows. This will help identify any technical or organisational challenges before rolling them out more widely.
2. Designate a Leader: Assign a C-level executive to oversee AI initiatives and act as the “general manager” of AI agents. This role will be critical in ensuring that AI is implemented strategically and that its impact is measured effectively.
3. Invest in Training: As AI agents become more integrated into daily business operations, it’s essential to invest in employee training. By educating staff on how to use AI agents effectively, businesses can maximise the value of their AI investments.

Conclusion: A Bright Future Ahead

The changes AI is bringing to the business world are coming fast, but they also bring immense opportunities. AI agents will not only streamline processes and improve efficiency but also provide a clear path to measuring ROI from AI investments. As organisations embrace this technology, they will unlock new ways of working that are more productive, efficient, and innovative.

With the right approach, AI can be a force for positive change in the workplace, creating opportunities for growth and collaboration. The future of business is bright, and AI agents are leading the way. Now is the time to embrace this change and prepare for the transformative impact AI will have on the world of work.

Your new life is going to cost you your old one. It’s going to cost you your comfort zone and your sense of direction. It’s going to cost you relationships and friends. It’s going to cost you being liked and understood.

It doesn’t matter.

The people who are meant for you are going to meet you on the other side. You’re going to build a new comfort zone around the things that actually move you forward.

Instead of being liked, you’re going to be loved. Instead of being understood, you’re going to be seen. All you’re going to lose is what was built for a person you no longer are.

Brianna Wiest, The Mountain Is You: Transforming Self-Sabotage Into Self-Mastery

Stepping into the big league of enterprise sales is no walk in the park, especially for start-ups. With over five years under my belt managing a Data & Tools department at a leading CPG company and juggling a seven figure budget, I’ve seen my fair share of pitches from start-ups in the Marketing Tech and Research Tech spaces. From these encounters, I’ve picked up a mix of winning moves and common slip-ups when it comes to winning over enterprise clients. In this blog post I will share my experience of how the best startups nail enterprise deals.

1. The Maze of Enterprise Decision-Making: Don’t underestimate how complex the decision-making is in big companies. You’ve got loads of people to win over, each with their own agenda. It’s a game of patience. From your first pitch to the final nod, it can take ages to seek alignment amongst multiple internal stakeholders, so stick with it and keep nudging them along.

2. Tailor-Made and Scalable Solutions: Loads of start-ups miss the mark by not realising that big businesses need solutions cut out just for them. Flexibility in what you offer is key. Also, remember to focus on your product’s scalability and reliability. Bring out the case studies and stats to back yourself up. Nailing a pricing model that big companies will go for is key. Getting your demos spot-on, focusing on the specific challenges these big decision-makers face is important.

3. Security, Compliance, and Real Benefits: Skimping on security and compliance in your pitch is a no-go – these are biggies for big companies. Stick to standards like SoC2. Keep in mind regional regulations like GDPR, CCPA, etc.

4. Building Trust with a Solid Track Record: Breaking into the market and building trust is tough. Just flashing logos of big-name companies without showing the real impact you’ve made won’t cut it. Trust comes from proving your worth by showcasing successful partnerships that have clear client benefits. Sharing tales of your wins and introducing prospects to other clients in different industries can really boost trust. These real-world stories provide valuable lessons and insights for potential enterprise clients. It is better to showcase examples of real impact instead of flashy logos. Avoid showing logos of enterprise companies that are not current clients — this is a telltale sign that you may not be ready to manage an enterprise account.

5. A Crack Sales Team: Your sales team needs to be more than just good at selling; they’ve got to really get the complex needs and wants of big businesses. This means savvy strategic thinking, deep industry know-how, and the ability to understand and define the value benefit for the customer. And don’t just talk features – highlight the real perks and value your product brings to the table. This can either be in the form of savings or creating value.

6. Understand The Procurement Cycle: Knowing how big organisations handle their budgets and procurement is crucial. In my own experience, even with a big budget, there were rules, deadlines and a buying cycle. Getting to grips with these buying processes and compliance is a big piece of your sales puzzle.

7. What have I missed? Have your say!

I want to share a bit of my journey in the world of business and work – a journey that has been shaped by a fundamental choice we all face: the mindset we choose to embrace. As I reflect on the ups and downs, it’s become clear to me that the decision between an abundance mindset and a scarcity mindset is like choosing the right path in a dense forest. And opting for a mindset of abundance isn’t just a feel-good mantra; it’s an approach that has helped me in the past.

So, what’s this buzz about abundance versus scarcity? An abundance mindset is all about believing there’s more than enough success, opportunities, and resources for everyone. On the flip side, a scarcity mindset is like thinking we’re all fighting over crumbs in a shrinking pie. I’ve come to realise that choosing abundance isn’t just about being a perpetual optimist; it’s about adopting a mindset that opens doors to opportunities we might not even see with scarcity-tinted glasses.

One of the coolest things about an abundance mindset is its impact on the projects we choose to dive into. Forget the short-term gains and immediate needs – thinking in abundance has pushed me to think big and strategise for the long haul. It’s like choosing to build a rocket ship instead of a makeshift raft. This shift in focus, inspired by an abundance mindset, fuels innovation and sets the stage for sustainable growth – even when the short-term view is unclear and thre might be the temptation to explore and run many short-term pilots / projects.

Now, let’s talk about company culture. I’ve noticed that the daily vibe within a company is heavily influenced by the prevailing mindset. A scarcity mindset can turn colleagues into competitors, breeding mistrust and hindering teamwork. On the flip side, embracing an abundance mindset transforms the workplace into a collaborative hub, where success is high fived as a team sport. Believe me, a culture of abundance really boosts morale, sparks creativity, and cranks up overall productivity.

But here’s the thing – abundance isn’t just a wishful thinking gig. It’s about actively seeking and creating opportunities. While a scarcity mindset might make us cautiously stick to the familiar, abundance urges us to explore new territories, forge partnerships, and shake up our business game. It’s not just about adapting to change; it’s about staying ahead in the ever-evolving business landscape.

Making the shift from a scarcity mindset to an abundance mindset isn’t an overnight transformation. It takes some self-reflection, intentional effort, and a commitment to changing those thought patterns. I’ve found that simple things like flipping negative thoughts and practicing gratitude can kickstart this transformative journey. The impact on decision-making and problem-solving is profound.

As I’ve worn different hats in business, I’ve realised that mindset is a heavyweight champion, especially in leadership roles. An abundance mindset encourages leaders to view challenges as growth opportunities. It’s like having a superpower that lets you navigate uncertainties with resilience and creativity. This mindset trickles down, inspiring teams to tackle challenges with a solution-oriented swagger, fostering a culture of continuous improvement and adaptability.

In my experience, transitioning from scarcity to abundance means to celebrate wins, no matter how small. It’s like fuelling up the positivity tank. Also, learn from failures – they’re not roadblocks but stepping stones. Finally, surround yourself with positive vibes; it’s contagious.

In a nutshell, my journey screams out the case for choosing the abundance mindset as your compass in the business wilderness. Whether you’re leading a startup or steering a big ship, an abundance mindset brings a ton of benefits. It unlocks doors to strategic projects, builds a culture of collaboration, and positions you for the marathon, not just the sprint.

Market research analytics is a critical component of any successful business strategy, providing valuable insights that guide decision-making and shape marketing initiatives. With the emergence of advanced AI technologies, businesses now have access to new tools that can revolutionise their market research efforts. One such cutting-edge tool is ChatGPT’s code interpreter, a feature that allows users to execute code directly within the language model. In this blog post, we will explore how ChatGPT’s code interpreter can be harnessed to transform market research analytics, enabling businesses to automate data analysis, gain deeper insights, and make data-driven decisions with precision.

Data Analysis Automation

Data analysis lies at the heart of market research, providing crucial insights into customer behaviour, market trends, and competitor performance. ChatGPT’s code interpreter empowers researchers to automate data analysis tasks by running Python scripts directly within the language model. By streamlining data processing, businesses can extract actionable insights faster and respond more promptly to market changes.

For instance, researchers can implement scripts to perform statistical analyses, visualise data trends, and run machine learning models. This automation not only saves time but also enhances the accuracy and scalability of data analysis, making it easier to process large datasets and derive valuable insights.

Sentiment Analysis for Customer Feedback

Understanding customer sentiments is vital for businesses to gauge the reception of their products and services. ChatGPT’s code interpreter can be employed to execute sentiment analysis algorithms, which analyse text data to determine whether sentiments expressed are positive, neutral, or negative.

With sentiment analysis, businesses can effectively monitor customer feedback from sources such as social media, online reviews, and customer surveys. This invaluable information enables them to address potential issues promptly, identify areas for improvement, and develop customer-centric marketing strategies.

Text Mining and Topic Modelling

Unstructured text data, such as online discussions, customer reviews, and industry reports, holds a wealth of market insights. ChatGPT’s code interpreter allows researchers to perform text mining and topic modelling, uncovering key themes and trends within large volumes of unstructured data.

Topic modelling helps businesses identify relevant topics, customer preferences, and emerging trends. Armed with this knowledge, they can optimise their product offerings, marketing campaigns, and customer engagement strategies.

Social Media Analytics

Social media platforms have become a powerful source of market information, providing real-time insights into customer preferences and industry trends. By employing ChatGPT’s code interpreter to execute web scraping scripts, researchers can collect and analyse data from social media channels.

Social media analytics enables businesses to track brand mentions, measure engagement levels, and monitor competitor activities. This data-driven approach empowers businesses to make data-driven decisions, capitalise on opportunities, and engage with customers effectively.

Competitor Analysis

Staying competitive requires businesses to keep a close eye on their rivals. ChatGPT’s code interpreter can automate the process of gathering data from competitor websites, analysing pricing trends, product offerings, and customer reviews.

By understanding their competitors’ strategies, businesses can identify gaps in the market, benchmark their performance, and position their products more effectively. The insights gained from competitor analysis are invaluable for strategic planning and staying ahead in the market.

Data Pre-processing and Cleaning

Accurate market research heavily relies on high-quality, clean data. ChatGPT’s code interpreter can be utilised to automate data pre-processing and cleaning tasks, ensuring data consistency, and removing inaccuracies or duplicates.

Automated data cleaning enhances data integrity and reduces the risk of biased results. This streamlined data preparation process optimises the efficiency of market research analytics and ensures that the insights derived are trustworthy and reliable.

Dynamic Market Dashboards

Market researchers and decision-makers need access to real-time market insights to respond swiftly to changing market conditions. With ChatGPT’s code interpreter, analysts can create dynamic market dashboards that visualise real-time data.

These interactive dashboards provide a comprehensive overview of market trends, customer behaviour, and key performance indicators. By accessing real-time data, businesses can make informed decisions, adapt their strategies, and capitalize on emerging opportunities.

Market Segmentation

Personalisation is a key driver of marketing success. ChatGPT’s code interpreter enables businesses to implement machine learning algorithms for market segmentation, grouping customers based on various attributes like demographics, behaviour, and preferences.

Market segmentation helps businesses tailor their marketing efforts, content, and product offerings to specific customer segments. This approach enhances customer engagement, increases conversion rates, and fosters brand loyalty.

Predictive Analytics

Predictive analytics empowers businesses to anticipate future market trends, demand patterns, and customer behaviours. With ChatGPT’s code interpreter, researchers can implement predictive models to forecast outcomes and trends.

Predictive analytics enables businesses to proactively plan marketing campaigns, optimise inventory management, and make strategic decisions to stay ahead of the competition.

Price Optimisation

Pricing is a crucial aspect of marketing strategy. With ChatGPT’s code interpreter, businesses can implement pricing optimisation algorithms that analyse market conditions, competitor pricing, and customer preferences.

By optimising pricing strategies, businesses can maximsze revenue, maintain competitiveness, and align prices with customer expectations.

ChatGPT’s code interpreter opens a realm of possibilities for businesses seeking to enhance their market research capabilities. From automating data analysis to predicting future trends, ChatGPT’s code interpreter offers a powerful and versatile solution for gaining deeper market insights and driving data-driven decision-making. By leveraging ChatGPT’s code interpreter, businesses can streamline data analysis, access real-time market data, and make informed strategic decisions that propel them ahead in the competitive market landscape.

I struggle with new year resolutions. When setting goals, I am overly optimistic and ambitious. At the end of the year, when I review my goals, I realise I was not able to honour them.

The same was true of 2020. What a year it has been! Who would have thought we would find ourselves dealing with a global pandemic of such biblical proportions! To be honest, I struggle with my privilege of being minimally affected by it all. I am lucky to work at Unilever, where I have a very rewarding job and amazing colleagues. I am also thankful that my wife and I are healthy, and our families are mostly unaffected by the virus. However, I am aware of so many colleagues and friends that have lost loved ones or are directly affected by this pandemic. The scale is truly astounding.

I am trying to make more of an effort this year. While I have a larger to-do list, I will be happy if I can continue doing three things in 2021:

Enough about me. Here is wishing you and yours a safe and very successful new year!

There are three tectonic changes taking place in the world today that are accelerating us towards this future:

1. Reducing cost of Renewable Energy
2. Improving capabilities of Edge Computing
3. Increased interest in the Right to Repair movement

Renewable Energy

It seems to me that Renewable energy is going to be such a big deal for humanity in the coming years. The pace at which these technologies are improving year on year is just astounding. This progress has allowed renewable energy to become cheaper and cheaper at a rapid rate. This is evidenced in the cost of building various types of electricity generating plants and their cost of keeping them running, a.k.a Levelised Cost of Electricity. 

In the western hemisphere, renewable energy powered 47% of the UK’s energy needs in Q1 2020. This smashed the previously quarterly record of 39% set in 2019. Offshore windfarms provided a majority of this renewable energy to the UK. The UK’s goal to nearly double its renewable energy capacity from 33 Gigawatts to 64 Gigawatts by 2030 is on track to be achieved by 2026, spurred by a wave of mostly wind power investments. 

In the eastern hemisphere, India the third-largest fossil fuel emitter has set a target of raising its renewable energy to 175 gigawatts (GW) by 2022. Through 2018- 2020, the country took very rapid strides to shore up its renewable energy efforts which stand at 90 Gigawatts at the end of 2020. To meet significantly exceed its renewable energy goal by 2022, India has awarded contracts to the tune of 600 Gigawatts in June 2020. The companies selected have agreed to sell solar power back to the grid for a record low of ₹2 (£0.02) per kilo watt hour. Similarly, the Hornsdale Power Reserve in Australia — the largest lithium-ion battery energy storage system in the world with a total output of 100 Megawatts — has been playing a significant role in grid stability since its installation in 2017. It has single handedly saved consumers AUD116 million (£65.66 million) in 2019, a big jump from  AUD40 million (£22.72 million) savings in 2018.

Edge Computing

The year 2020 has seen a significant increase in semi-conducter chip design innovation. The processor performance gains we see in the most recent ARM designs from Apple are truly amazing across all performance benchmarks. Companies such as TSMC and Samsung are on the forefront of this 5 nanometer chip fabrication process. This fabrication process, along with the new System on Chip designs we see from Apple are allowing computing device manufacturers to create devices that can perform billions of calculations faster than their predecessors, while using lesser power than is traditionally required. 

These performance improvements have allowed manufacturers to create devices that are powerful and use little power. This capability has given a fillip to Edge Computing. Manufacturers have started deploying Reinforcement Learning capabilities in their devices which learn how consumers are using their devices and tailor the experience specifically to their usage.

Products such as the Apple Watch and Tesla’s self-driving feature learn the routines and preferences of their users over time in order to tailor the usage experience of the product specifically to that user. In 2022, the 3 nanometer fabrication process is going to further help improve the capabilities of Edge Computing in our products and services. 

Right to Repair

As the pace of innovation on energy storage improves and as manufacturers start building products that get better to use over time thanks to reinforcement learning, there is a real opportunity for consumers to breathe new life into their old products. We are already starting to see this with vintage cars, which are being converted from their internal combustion engines to electrical powertrains — making them faster and more responsive to drive. 

Manufacturers have noticed this and are starting to adopt practices that keep us locked into our existing cycle of consumerist & materialistic attitudes — planned obsolescence. One way to do so is to make it increasingly difficult to repair our products. Instead, manufacturers would prefer that we junk our products in favour of buying new models regularly. Today, this is solved by companies releasing products with marginal improvements that become obsolete a few months down the line! E.g. iPad Pro 2020. 

While companies will try and keep us in their annual purchase cycle, my hope is that this takes us back to more modular designs. The Right to Repair movement is starting to gather steam and is going to become such a big deal. Communities such as Makerfaire, where tinkering with products is encouraged are going to hold huge sway for the Open Source Community enthusiasts in the coming years. With the wide availability of reinforcement learning in edge devices, being able to swap out just the compute or storage parts is going to be really empowering. This will improve our ability to use products for a longer period of time. We are seeing some of this in force today. E.g we can now recycle older ASUS routers to create mesh networks. 

Conclusion

The combination of decreasing energy costs + better & cheaper computation and storage + the increasing consumer awareness around planned obsolescence are taking us into a world were the next few years will bring rapid changes to the way humans interact with technology. Furthermore, these changes are going to be tectonic — nearly comparable to the technological and societal changes the steam engine or the internet brought to our modern civilisation.