In this exclusive interview, Jimmi Jakobsen, CEO at AI agent platform JeffreyAI unpacks what agentic AI truly means, why early adoption matters, how customisation is shaping business value, and what the next decade could look like as AI agents become deeply embedded in the way we work.
How would you define agentic AI, especially in contrast to traditional AI models and Large Language Models (LLMs)?
Agentic AI represents a significant shift from traditional AI and even from large language models (LLMs), which are typically reactive and require explicit prompts to generate responses. Unlike these conventional systems, agentic AI operates proactively, as it can take initiative and act autonomously on behalf of users within defined scopes.
The transition to agentic AI won’t be seamless for everyone. Smaller businesses, in particular, may find it challenging to adopt agentic AI due to limited familiarity or technical capacity. They’ll need guidance and support, and ironically, that support can itself be powered by AI agents.
For example, one of the solutions we’re currently developing involves an onboarding agent that interviews business owners, understands their needs, and builds a customised go-to-market strategy, helping them get the most out of their sales and marketing investments. We’re actively testing how to ensure these agents provide accurate, reliable guidance. It’s a fast-moving space, and we believe agentic AI will quickly become indispensable for business growth and operational efficiency.
What are some of the use cases for JeffreyAI?
One of the most practical and impactful use cases for JeffreyAI today is intelligent email engagement. Rather than sending out static newsletters, businesses can deploy JeffreyAI to engage recipients in meaningful two-way conversations at scale. For example, in a campaign targeting 100,000 contacts, a human team couldn’t realistically respond to each message—but an AI agent can. It analyses each reply and carries on the conversation based on the business’s offerings, tone, and strategic goals, resulting in highly personalised and effective outreach.
Beyond email, JeffreyAI also engages via SMS and WhatsApp, where open rates and response levels are often significantly higher. We’ve seen strong adoption from sectors like hospitality—restaurants, for example, use AI to send SMS campaigns to past customers, sharing a story from the chef or promoting a special dish. When customers respond with questions like availability, the AI can instantly check booking systems and provide a tailored reply, even completing reservations on the spot.
These are just current examples. As the technology evolves, we expect JeffreyAI to handle more complex interactions across social platforms and other channels, helping businesses scale meaningful customer conversations without compromising on personalisation.
How are customers engaging with the agents? Is there still a level of uncertainty?
Yes, adoption is still in a transitional phase, and it’s natural that some customers remain hesitant. There’s a general sense of uncertainty—some people are uncomfortable interacting with AI, and there are also regulatory considerations that require businesses to approach deployment carefully.
For instance, users must be explicitly informed when their communications are being analysed by AI or when they’re interacting with an AI agent. Consent is critical, particularly in regions governed by regulations such as GDPR, the EU AI Act, and ICO standards. Done properly, this transparency builds trust and allows AI to deliver real value while respecting privacy and user rights.
That said, we’re seeing a generational shift. Younger users are far more open to AI-powered engagement, while older generations may take more time to adapt. Much like how voice assistants like Alexa have become mainstream in many homes, AI agents will become a natural part of how people interact with services—but we’re not fully there yet. The transition will likely take another decade as societal comfort and legal frameworks catch up.
What is the biggest misconception people have about agentic AI?
One of the biggest misconceptions is that agentic AI is ready to fully replace human jobs today. It’s not. While agentic AI is powerful and evolving rapidly, it still has limitations – hallucinations, misinformation, and unpredictable outputs can occur. It’s not yet a fully reliable standalone solution.
That said, it is the right time for businesses to start engaging with the technology. The key is to start small – implement AI in simple, controlled use cases and learn from the results. Over time, this builds familiarity, trust, and a roadmap for deeper integration.
Agentic AI isn’t a magic bullet – it’s a tool with incredible potential. But it requires thoughtful implementation, responsible oversight, and a step-by-step approach to truly unlock its value. The real mistake would be waiting too long to begin exploring what’s possible.
Who will benefit the most from agentic AI adoption?
In truth, most industries stand to benefit significantly from adopting AI agents into their workforce. At its core, it’s about increasing visibility, attracting more customers, and remaining competitive, and these are fundamentals that apply across nearly every sector. Whether you’re selling products or services, success depends on how well you position yourself in the market, and agentic AI can enhance that effort.
The real advantage lies in early adoption. Businesses that move now will be able to continuously feed data back into their AI models, refining and optimising them over time. That creates a unique edge, and your AI becomes tailored to your operations and needs. This is the race we’re in right now: a race to the top. Those who invest in agentic AI early will lead the way. Those who hesitate may find themselves not just behind – but struggling to catch up for years to come.
From a user perspective, what does engaging with an AI agent look like?
From a user perspective, engaging with an AI agent will feel much like having a natural conversation via chat – simple, intuitive, and human-like. But behind that simplicity lies immense capability. Based on the dialogue, the AI agent will analyse your needs, develop a strategic approach, and define tailored engagement models for your business.
The more context and input the user provides, the more accurately the agent can identify growth opportunities – whether that’s refining your marketing message, optimising channels, or targeting the right audience. From there, the AI takes action: it sets up your campaigns, creates visuals and messaging, allocates your advertising budget, and starts driving your sales pipeline – all without requiring manual intervention.
What we foresee is a future where traditional systems like CRMs become largely obsolete. Instead of managing tools, business owners will simply receive outcomes: booked meetings where human interaction is needed, daily reports forecasting revenue, alerts on product stock requirements, and even hiring needs based on service demand.
In essence, users won’t have to learn how to use multiple platforms, they’ll interact with a single AI agent that orchestrates it all in the background. It’s not about navigating systems anymore; it’s about telling the AI what you want to achieve and letting it handle the execution.
Where do you see this technology progressing in the next 10 years?
Over the next 10 to 15 years, I believe AI technology will become fully autonomous. While it’s difficult to predict the exact timeline, I suspect this transformation will happen much faster than most people expect.
In the near term, AI agents will become widely accessible and deeply embedded in knowledge-based work, especially roles that involve sitting in front of a screen. We’re already seeing AI capable of conducting phone conversations, and soon it will feel entirely normal, even expected, to interact with AI agents as naturally as we do with people.
The next frontier will be the integration of AI with robotics. That shift will trigger a second wave of disruption – this time impacting manual labor and physically intensive jobs. So, we’ll experience two significant phases: the first dominated by intelligent digital agents handling cognitive tasks, and the second shaped by physical automation.
Imagine telling an AI agent, just like you would a seasoned marketing consultant: “I have 1,000 units in stock. I want to sell them at the best possible price over the next three months. Here’s my marketing budget.” The AI will take it from there – strategising, executing campaigns, and optimising in real time.
How will AI agents change the marketing industry?
Agents will dramatically reshape job functions. In marketing, for example, we’ll see teams transition into roles as AI trainers, specialising in fine-tuning models for specific industries and leveraging that expertise as intellectual property. These teams will create value by developing highly focused, high-performing AI systems tailored to niche sectors.
However, as general AI capabilities improve, the competitive advantage of highly specialised models will begin to fade. Within a decade, generalist AI may become sophisticated enough to rival or even surpass many domain-specific solutions. This will democratise access to powerful AI tools, allowing businesses of all sizes and industries to harness AI where it brings the greatest value, regardless of their in-house expertise.
As a UK-based company, do you think strong regulation in the region is stunting AI innovation?
Regional laws vary, which adds complexity. While Europe and the UK have robust regulations, other regions may not enforce the same standards. That creates an uneven playing field, where some countries may gain competitive advantages by operating outside the bounds of ethical data usage.
Businesses in regulated regions, like the UK, are at a disadvantage unless global standards begin to align. Without consistency in data governance, we risk an imbalance in AI adoption and innovation. Ultimately, it’s a reasonable expectation for individuals to control how their data is used – but this needs to be addressed through updated laws, international trade agreements, and clear policy frameworks to ensure fairness across markets.
