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Staying Ahead in the Agentic Era (or Any Era) Comes Down to 1 Key Priority

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Published 10/22/2026 1:30:00 PM

Staying Ahead in the AI Era Comes Down to 1 Key Priority

Opinions expressed by Entrepreneur contributors are their own.

Key Takeaways

Incumbents don’t lose simply because startups use newer AI technology. They lose when they fail to adapt to customers’ evolving needs and leave opportunities for competitors to solve emerging problems.
Regularly review customer feedback, renewal and churn calls, and unmet needs to uncover changing pain points. A strong product strategy focuses on solving the customer’s problems, not simply adding features.
Companies need to build technology for adaptability and remain students of the customer’s evolving problem.

I’ve sat through many conversations lately about which incumbent software categories AI-native startups are about to wipe out. The narrative typically goes like this: Incumbents are bolting a chatbot onto a 10-year-old product while new startups have built AI-native solutions from day one; therefore, incumbents are losing their edge.

And yet, many incumbents are thriving in the AI era. What gives? While the age of the software stack can certainly drive customer relevancy, I think that’s often the wrong paradigm.

At this point in my career, I’ve watched this dynamic play out across three technology curves: from web to mobile, then to cloud, and now to AI. In each, some incumbents thrived, some decayed. Some startups redefined categories, some were side-swiped by incumbents.

In my experience, the winners often aren’t the ones with the newer stack. They’re those who stayed close enough to the customer (from their first dollar of revenue to hitting $500M+ in ARR) to notice when the problem the customer hired them to solve quietly changed shape — and they kept innovating around that problem set.

A relevant example

Consider a cloud-based SaaS company selling small-business accounting software today. It built a great market position by giving a business owner a single place to track transactions, send invoices and, most importantly, close the books accurately. For years, that was enough.

But as the company grew and gained market share, its customers’ problem set quietly changed. The software platform had made closing the monthly books accurate, but not more efficient. People still spent hours on repetitive tasks instead of strategic financial work.

At that point, this example incumbent has two paths. It can decide efficiency “isn’t what we do” and let someone else solve that problem, or it can treat the shift as its own problem to solve. Companies that take the first path rarely get disrupted in one dramatic moment. It happens piece by piece: A startup builds an automated revenue recognition tool, then an automated forecasting tool, and so on. Eventually, customers start asking why they’re still paying for the original accounting software when other, higher-ROI tools are solving today’s problems.

So how do incumbents stay product leaders through innovation curves?

The playbook is simple to say and harder to run

Pay close attention to the evolution of the problem. This sounds obvious, but I’ve seen countless startups get worse as they succeed. Go-to-market optimizes the funnel, and product optimizes the roadmap, but nobody, not even the product manager, owns the job of asking whether what the customer hired you to do three years ago is still what they need today.

It’s easy to fixate on the next feature instead of asking how well your solution addresses the evolving problem. Though history wasn’t necessarily my strength in undergrad, I’ve come to see the tremendous value in leaders understanding the history of a customer’s problem. Where did it start 10 years ago? How did it evolve before our solution, and how has it evolved since? How will it change as new technology is adopted?

Ask this instead

The one thing I’d encourage you to pin above a product roadmap is this: “Stop asking customers what else we can build for them.” Customers are generally bad at answering that question, and it’s not their fault. People feel frustration long before they can describe the tool that would fix it, and if they could, they’d build the solution themselves. Ask these questions instead:

Why did you adopt us in the first place, and how has that problem changed?
How has adopting our solution changed the pain points that you experience?
What are we not thinking about?
What recurring tasks do your employees hate?
What will your department look like in five years, both people and technology? Why?

Ask consistently across enough conversations, and patterns will emerge that transition your product strategy to be as forward-thinking as the startups you compete with. Stop treating a feature pipeline as a product strategy. Both are critical to long-term innovation, but they aren’t the same thing.

A few tactical ways to approach this:

Pull your last 10 renewal or churn calls and listen for “still” and “but now” (“We still use the product for X, but now we also need Y.”). “But now” may contain some gold.
Ask your customer success team what requests never become a support ticket because it “isn’t really what we do.” Look for patterns.
Once a month, sit in on customer calls, not to sell but to hear how customers describe the problem in their own words, and help them articulate their frustrations.
Spend a day with an advisory customer who fits your ideal customer profile. Walk through the history of the problem and the questions above. They’ll appreciate the attention, and you’ll leave with real product insight.

Build your technology for adaptability. If you can extend the product only by tearing up its original core, you’ll always move too slowly. Incumbents that stay relevant build modularly enough to absorb an adjacent problem, like automated revenue recognition or forecasting capabilities, without a multi-year rebuild putting them behind in the race for customer retention.

Never fully let go of product strategy. Great founders remain students of the customer’s evolving problem, even after hiring a strong VP of Product or Chief Product Officer. They’re students of the customers’ historical, current and future needs. That doesn’t undercut their Head of Product. It gives the company a healthy check on the product team’s assumptions when it matters most.

By following this playbook, your company can stay innovative, relevant and forward-thinking at every stage of scale. Complacency is a competitor’s opportunity. When leaders get comfortable with success and overconfident about their customers’ needs, a startup down the street starts asking the right questions and solving today’s problems.

After all, when does a startup become an incumbent? In the AI era, almost immediately.

Opinions expressed by Entrepreneur contributors are their own.

Key Takeaways

The AI market is experiencing rapid growth and transformation, with significant opportunities emerging for startups, particularly for those offering generative B2C AI services.
Success in the AI startup space requires identifying specific audience needs and developing unique, innovative solutions that address real-world problems.
While there’s excitement around AI, startups face challenges in proving their advantage over the burgeoning SaaS market. Investors are looking for more grounded market fits and unique competitive advantages in AI ventures.

Artificial intelligence is set to become a swelling ocean of radical change, altering many facets of society. Within the business world, AI is already driving significant and far-reaching innovation. And within the B2C arena, significant opportunities for startups offering generative B2C AI services are starting to emerge.

Generative AI, a machine learning system, capable of generating text, images, code or other types of content, provides startups with a strong platform to launch new ideas and services into an area that is ripe for development. Some of the more obvious B2C areas include:

Personalization and recommendation engines for ecommerce and content platforms
Chatbots and virtual assistants for customer support and engagement
AI-powered health and wellness apps
Intelligent home automation and IoT solutions
AI-driven financial services and tools for personal finance management

That said, it’s also a question of imagination and identifying opportunities. A striking example is Aithor.com, an AI startup that has created powerful waves. Aithor.com is a writing tool for academic and creative writing. Following its launch in May 2023 and its first $1 million in revenue, it made a return in under 10 months. It has rapidly become a global operation, gaining subscribers from 95 countries.

There are competitor AI-based tools, but Aithor has some unique features. It helps with content editing, formatting and reference creation for short and even lengthy documents. At the same time, it allows users to make edits that are genuinely undetectable by evaluating text with the two most popular tools (GPTZero and ZeroGPT). It is a unique AI writing tool that helps overcome the inability to write by providing seamless edits to papers.

According to the Global Artificial Intelligence Industry – Forecast and Analysis 2023 report, the global artificial intelligence market size was valued at $62.35 billion USD in 2020 and is expected to grow at a compound annual growth rate (CAGR) of 40.2% from 2021 to 2026. While this report covers the overall AI market, a significant portion of this growth is expected to come from the B2C sector.

B2B is showing the way for AI in B2C markets. According to the Mckinsey Global Survey 2023, a third of organizations are already using generative AI in some capacity, and with some businesses willing to pay up to $800,000 for candidates with ChatGPT and AI skills, it’s clear a new future is being forged. We’re already seeing this in sectors such as healthcare, education, the automotive industry and so on. It empowers startups to develop innovative solutions that automate tasks, optimize processes and enhance the overall customer experience.

Market movements

Statista claims the overall AI market reached approximately 200 billion USD in 2023 and is projected to surpass 1.8 trillion USD by 2030. These are dizzying figures, however, to put these predictions in context a comparable analogy is the still burgeoning SaaS market.

SaaS is a highly profitable sector for venture capitalists. However, since the advent of ChatGPT, AI and Machine Learning (ML), private company valuations in this field are outpacing those of SaaS companies. But that said, early-stage SaaS businesses are still likely to outperform AI companies.

Furthermore, outsized deals like OpenAI’s $10 billion late-stage round are greatly impacting the “supply” of capital for AI and ML startups. Despite these market moves, there’s no denying that AI stocks have emerged as some of the most sought-after investments in the public market. The remarkable 239% surge in Nvidia’s stock price, along with the impressive debut of Astera Labs, illustrates the seismic impact AI and ML are having. And as new AI and ML-based tech emerges, there is likely to be a potential surge in VC investment.

AI startup steps

Despite all the excitement, AI and ML startups have not yet fully proven their market advantage compared to SaaS offerings. While AI businesses effectively raised $50 billion worth of interest in 2023, there was a reasonable decrease in ventures before the year ended, revealing that the initial excitement is waning. Investors started to look for more grounded market fits and unique competitive advantages.

Identify needs

Going back to Aithor.com, the operation has been so successful because it identified its specific audience and provided them with a tool that addressed needs. Of course, this is the secret of success for any startup: Who are you aiming at, and what are you giving them that will make their lives easier? It’s no different for AI B2C startups. Once you’ve identified how you can solve real-world problems, there are technical aspects that need to be addressed to ensure commercial success.

Robust data strategy

You need to develop a robust data strategy that includes data acquisition, cleaning, labelling and management. Ensure that you have access to high-quality, diverse and relevant data sets to train and validate your AI models. Data quality and quantity will significantly impact the performance of AI models.

Algorithms of choice

Towards this end, it’s also essential to understand which algorithms are best suited for your B2C applications. This means selecting the most appropriate AI techniques and algorithms based on the problem you’re solving. For instance, which algorithms such as regression, classification, clustering, reinforcement learning and deep learning are appropriate for your business?

Continuous learning

It’s an obvious point, but AI systems that can continuously learn and adapt to changing user preferences and market dynamics are also essential for long-term success in the B2C market.

Scalability and low latency

You also need to prioritize scalability and performance so your architecture can handle increasing data volumes and user requests as your business grows. Startups should focus on optimizing model inference speed and ensuring low-latency responses to user queries so your users are receiving super fast responses.

Data security and privacy

Data security and privacy is also a critical consideration. Any AI model requires data privacy and security measures to protect sensitive customer data and comply with relevant regulations such as GDPR or HIPAA, depending on your industry and target market.

Intuitive and friendly

And of course, you need to make it easy for users to interact with your AI system and interpret the results in real time. This requires a friendly, intuitive interface that is easy to use. Furthermore, collecting user feedback and analyzing system logs will identify areas for improvement so you can regularly update and fine-tune your models based on new data and user insights.

Ethical considerations

And last, but certainly not least, awareness of ethical considerations and biases in AI systems is crucial. Fairness, transparency and accountability in AI algorithms and decision-making processes need to be prioritized, informed by the nature of your business.

The secret sauce is your team

By focusing on these technical aspects and integrating them into a comprehensive business strategy, AI startups will certainly increase their chances of success. But of course, there needs to be the foundation of a strong and diverse team with expertise in AI, software engineering, data science, and domain knowledge. Within the team, there needs to be a culture of innovation, collaboration and continuous learning to stay ahead of the curve in the rapidly evolving AI landscape.

Opinions expressed by Entrepreneur contributors are their own.

Key Takeaways

A growing imperative arises to safeguard sensitive information as businesses, especially LLMs, actively embrace and integrate AI into diverse spheres.
Here’s how to empower firms to adeptly navigate the complexities of AI and foster creativity while ensuring strategic, ethical, and responsible use of AI.

Companies venturing into the artificial intelligence (AI) sector are at a watershed. In 2023, AI startups collectly raised close to $50 billion, while global enterprise spending on AI, including generative AI software, related hardware, and IT and business services, is projected by IDC to reach north of $150 billion in 2027.

This momentum underscores the significance of AI, which is anticipated to contribute a whopping $15.7 trillion to the global economy by 2030, as projected in PwC’s Global Artificial Intelligence Study. With the increasing integration of AI into various firms’ operations and decision-making, especially Large Language Models (LLMs), the need to safeguard sensitive information has become increasingly urgent.

Nonetheless, the effective implementation of AI privacy policies, whether a generic model or a vertical approach, remains challenging. As companies utilize AI to optimize operations, it is crucial to understand the complexity of the global regulatory landscape and ethical concerns to ensure data protection.

This article explores three essential tips to help companies effectively address the challenges of promoting innovation while strategically leveraging AI’s capabilities in the evolving global landscape for sustained progress and business success.

1. Keep track of regulatory shifts

2023 has been a monumental year for AI governance; nations and economies worldwide have issued or are en route to drafting AI regulations: the European Union, China, and the United States, for instance, have all released AI-related regulations in 2023.

The three major economies have chosen different approaches to AI regulation, reflecting the lack of consensus in this field. While certain economies may prioritize risk control and privacy protection, others might be inclined toward leveraging AI to promote economic growth and tackle pressing societal issues.

As a result, multinational tech conglomerates would face divergent regulatory landscapes as they operate globally. Ensuring companies’ AI applications comply with local market regulations is crucial; firms should take careful consideration of the differentiated regulatory landscapes across various regions to strike a cautious equilibrium between innovation and privacy.

Successfully navigating this intricate landscape requires companies to monitor the evolving regulations in relevant markets closely. Proactively updating internal policies and operational guidelines is imperative, ensuring seamless alignment with the latest effective rules and standards. Conducting a meticulous legal risk analysis before launching products and services is also essential.

At the same time, the establishment of robust AI governance and risk management frameworks in companies, complemented by the implementation of internal mechanisms for risk alert and accountability, will ensure compliance and foster innovation and responsible business practices.

2. Consider ethical concerns

The use of AI in daily life could potentially lead humankind to two divergent results: a more diverse, inclusive world or a more polarized, divisive one. Achieving an equilibrium between embracing scientific breakthroughs and tackling ethical dilemmas is crucial as society wrestles with the potential advantages and difficulties arising from the extensive integration of artificial intelligence.

The 2023 AI Index Report by Stanford University provides insight into the extent of AI’s influence, indicating a nearly 6.5-fold increase in references to AI in legislative proceedings worldwide since 2016. The report is based on a review of parliamentary records from 81 countries and signifies that both businesses and political leaders are confronting new risks amid this powerful technological wave.

Hence, it is crucial to recognize the concurrent rise in ethical issues among the global population. Additionally, the AIAAIC database reveals that since 2012, there has been a 26-fold surge in ethical dilemmas related to AI concerning a variety of topics, such as bias in algorithms and gender representation, shedding light on a complex environment where the benefits of AI coexist with an increasing awareness of misuse possibilities.

The remarkable surge in AI use is notably apparent as artificial intelligence becomes further incorporated into many domains of our lives, encompassing healthcare, banking, education, and other sectors. Nevertheless, this integration also intensifies the requirement for meticulous oversight and surveillance to guarantee the responsible utilization of AI.

Hence, It has become imperative for businesses and other stakeholders to join forces in ensuring that AI use follows a nondiscriminatory and nonbiased principle and that the public is well-educated and informed before making active use of AI.

3. Use AI for good

Just as with the rise of large language models today, similar to other significant technological advancements in history, including the advent of the internet, there was a comparable apprehension that this emerging technology might overshadow and potentially replace established mediums like television, magazines, and radio.

However, as we have seen over time, despite initial fears, technologies like the internet have not only coexisted with but also significantly enriched these traditional forms, enhancing their scope and impact rather than rendering them obsolete.

Thus, the core of this apprehension is rooted in the fear of disrupting the pre-established order, which is equally pertinent to businesses. Consequently, for businesses, the critical issue today is ensuring the “Controllable, Customizable, Deliverable” of Large Language Models, preserving the balance between innovation and the maintenance of existing systems, privacy and safety.

AI companies, including Nasdaq-listed Xiao-I, have been pioneering the implementation of LLMs in business scenarios to create a commercialization path for B2B customers while actively ensuring compliance with a variety of factors in practice to close the potential loopholes. Some of the practices include storing data locally and ensuring adherence to ideology, laws, ethics, and data security at national and enterprise levels.

To bring the business to the next level, AI-based LLMs should adapt to customers’ specific requirements by going beyond simple customization of the model to include personalized material, elements, and scenarios, enabling enterprises to align the model with their needs perfectly for further value creation.