Proactive AI is Here - Investigating Proactor.ai, the new AI tool that thinks ahead

In this episode of the Inspiring Tech Leaders podcast I explore Proactor.ai, a new proactive AI assistant.  AI assistants like ChatGPT, Copilot, and Manus are powerful, but they're reactive. They wait for you to ask the right question at the right time. In a fast-paced meeting or a critical sales call, that moment is easily missed.

For too long, we've been limited by the prompt barrier.  What if your AI could think ahead?  Proactor.ai is the first proactive AI agent that listens to your conversations in real-time, anticipates your needs, and delivers insights before you even ask.

In this episode, I explore:

💡 The Problem with Reactive AI: Why the current model holds us back.

💡 What is Proactor.ai?: How it provides real-time transcription, proactive advice, and automated task identification.

💡 Real-World Use Cases: Transforming sales, recruiting, and strategic decision-making.

💡 The Tech Behind the Tool: A look at the advanced NLP and contextual memory systems that power this innovation.

💡 Market Landscape & Future Trends: Where Proactor.ai fits and what's next for autonomous AI.

Is proactive AI the next evolutionary step we've been waiting for? Will this technology become a standard feature in tools like Microsoft Copilot?

I'd love to hear your thoughts. Will you be adopting proactive AI into your organisation? Is it a game-changer or are you waiting to see how it develops?

Listen to the full episode to get the complete picture and decide for yourself.

Available on: Apple Podcasts | Spotify | YouTube | All major podcast platforms

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Chapters

• 0:00 Introduction
• 0:50 The Problem with Reactive AI
• 1:49 What is Proactor.ai?
• 3:02 Proactive AI Advice and Insights
• 4:08 Memory and Continuity
• 4:51 Automated Task Identification and Action
• 6:46 Enhancing Human Judgment with AI Insights
• 8:18 Real-Time Research and Fact- Checking
• 10:09 The Technology Behind Proactive AI
• 11:44 Contextual Memory and Learning
• 13:49 Privacy and Security Considerations
• 16:47 Ethical and Social Implications
• 19:08 The Cost of Proactor.ai
• 20:43 A New Era of AI Assistance
• 22:23 Wrap Up

Transcript

Welcome to the Inspiring Tech Leaders podcast, with me Dave Roberts.  This is the podcast that talks with tech leaders from across the industry, exploring their insights, sharing their experiences, and offering valuable advice to help technology professionals.  The podcast also explores technology innovations and the evolving tech landscape, providing listeners with actionable guidance and inspiration.

Today I am going to talking about Proactor AI, a brand-new AI powered tool launched last month.  This is an AI Assistant that is proactive and thinks ahead to anticipate your upcoming needs before you have even asked any questions.  Is this another evolutionary step in artificial intelligence that we are witnessing?

To understand why Proactor.ai is so interesting, we first need to acknowledge a fundamental limitation with current AI assistants since their inception, which is that they are reactive, not proactive.

Whether we are talking about Copilot, Manus, ChatGPT, or any other AI tool you have used, they all share the same basic interaction model, which involves you asking a question, and then they respond. You give a command, and they execute it.

This reactive nature creates what is called the "prompt barrier".  The prompt barrier requires users to constantly think about what to ask, how to ask it, and when to ask it.  In fast-paced environments like business meetings, sales calls, or educational settings, this barrier can be significant. By the time you formulate the right question and receive an answer, the conversation has often moved on, and the moment for that insight has passed.

This is where Proactor.ai aims to fill the gap. Rather than waiting for explicit instructions, it continuously listens, analyses, and acts based on contextual understanding of what is happening in real-time.

Proactor.ai positions itself as "the first proactive AI agent" and "the number one proactive AI teammate". At its core, it's a real-time AI assistant that listens to conversations, meetings, and calls, then proactively identifies needs and takes action without waiting for explicit commands.

But what does this actually mean in practice? Let's break down the core capabilities that make Proactor.ai different from traditional AI assistants.

The foundation of Proactor.ai's proactive capabilities lies in its real-time transcription and natural language understanding. Unlike simple transcription services that merely convert speech to text, Proactor.ai analyses the semantic meaning of conversations as they happen. It understands context, identifies key topics, recognises when tasks are mentioned, and detects when participants need additional information.

This isn't just about accuracy in transcription, although the company claims to provide "accurate, searchable text instantly”. It's about semantic understanding that enables the AI to recognise patterns and opportunities for assistance. When someone in a meeting says, "We should look into what our competitors are doing in the European market," Proactor.ai doesn't just transcribe those words, it understands that this represents a research task and begins gathering relevant competitive intelligence.

Perhaps the most revolutionary aspect of Proactor.ai is its ability to provide what the company calls "proactive AI advice”. The system thinks ahead, listening to conversations and understanding what participants need before they explicitly ask for help. This manifests in several ways.

When problems are discussed, Proactor.ai automatically generates potential solutions and actionable next steps. If a team is struggling with customer retention, for example, the AI might proactively suggest proven retention strategies, relevant case studies, or specific metrics to track.

Rather than providing assistance after meetings end, Proactor.ai delivers real-time support. It generates summaries, identifies key takeaways, and creates to-do lists as conversations progress. This allows participants to stay fully engaged in the discussion while knowing that important details are being captured and organised.

When topics arise that require additional information, Proactor.ai automatically conducts research and provides relevant context. If someone mentions a new technology, regulation, or market trend, the AI quickly gathers key points, solutions, and relevant resources, delivering them without interrupting the conversation flow.

One of Proactor.ai's most compelling features is its comprehensive memory system. The AI remembers everything across sessions, creating what the company describes as a "personal knowledge base". This isn't just about storing transcripts - it's about building a contextual understanding of ongoing projects, relationships, preferences, and historical decisions.

This memory capability enables several powerful use cases. Team members can ask questions like "What did we decide about the marketing budget in last week's meeting?" or "What were the main concerns raised about the new product launch?" and receive instant, accurate answers. The AI can also proactively reference past discussions when relevant topics arise, helping teams maintain continuity and avoid repeating previous conversations.

Perhaps most impressively, Proactor.ai can identify potential tasks mentioned during conversations and automatically provide actionable results. The company describes this as "From Mention to Action, Automatically”. When someone receives a sudden task during a meeting, Proactor.ai recognises this and immediately begins working on solutions, often providing results before the meeting ends.

This capability represents a significant leap beyond traditional task management tools. Instead of requiring users to manually create tasks, assign them, and track progress, Proactor.ai automatically detects when work needs to be done and begins the process of completing it.

Proactor.ai has identified several key markets where proactive AI assistance can provide significant value. Each of these represents a different application of the core technology, tailored to professional needs and workflows.

For sales professionals, Proactor.ai offers capabilities that could fundamentally change how sales conversations unfold. The AI suggests impactful questions and provides personal tips during calls, helping salespeople navigate conversations effectively. More importantly, it can identify buying signals and those subtle cues that indicate a customer is ready to make a purchase decision.

Consider a typical sales scenario: A prospect mentions they're frustrated with their current solution's reporting capabilities. Proactor.ai would immediately recognise this as a pain point, research the prospect's current solution to understand its limitations, and suggest questions to uncover the depth of this frustration. It might also prepare talking points about how your solution addresses these reporting challenges, complete with relevant case studies or ROI calculations.

The AI also handles follow-up communications automatically. After identifying key discussion points and commitments made during a call, it can draft personalised follow-up emails that reference conversation details and include relevant resources. This level of personalisation and speed in follow-up communication can significantly impact conversion rates and customer relationships.

In the recruiting space, Proactor.ai listens to candidate interviews in real-time, providing suggestions for follow-up questions and comparing candidate responses to job requirements. This application is particularly valuable because it allows recruiters to focus entirely on building rapport with candidates while the AI handles the analytical aspects of the interview.

The system can identify when candidates mention certain skills, experiences, or achievements that align with job requirements, prompting recruiters to dive deeper into these areas. It can also flag potential concerns or gaps in real-time, ensuring that important topics aren't overlooked during the interview process.

Perhaps most valuably, Proactor.ai can help standardise the interview process by ensuring that all candidates are evaluated against the same criteria, while still allowing for the natural flow of conversation that makes interviews effective.  Of course, we have to be careful that there aren’t any biases in the AI that would affect the interview process.

For general business teams, Proactor.ai provides live strategy ideas and action items during meetings. It ensures that all participants are aligned by providing clear summaries and helps projects move forward smoothly by identifying next steps and potential obstacles.

The AI can recognise when teams are discussing strategic decisions and automatically provide relevant market data, competitive analysis, or best practices. If a team is debating whether to enter a new market, for example, Proactor.ai might proactively gather market size data, competitive landscape information, and regulatory considerations, presenting this information in a digestible format during the discussion.

For media professionals, Proactor.ai offers capabilities that could significantly enhance the quality and speed of content creation. The AI generates live ideas for research, checks facts during interviews, and suggests story angles, making the content creation process even more efficient and thorough.

During interviews, the AI can fact-check statements in real-time, flag inconsistencies, and suggest follow-up questions that might reveal important details. For investigative journalism or complex topics, this real-time research capability could be invaluable, allowing journalists to pursue leads and verify information as conversations unfold.

In educational settings, Proactor.ai goes beyond simple note-taking. It finds relevant resources in real-time and explains difficult concepts immediately when they arise during lectures or discussions. This capability could be particularly valuable for students who struggle to keep up with fast-paced lectures or complex material.

The AI can also identify when students might be confused based on their questions or comments, automatically providing additional explanations or resources to help clarify difficult concepts. For educators, this real-time feedback could help them adjust their teaching approach on the fly to better serve their students' needs.

Understanding how Proactor.ai achieves its proactive capabilities requires examining the underlying technologies that make this possible. While the company hasn't released detailed technical specifications, we can infer several key components based on the described functionality.

At the foundation of Proactor.ai's capabilities is sophisticated natural language processing that goes far beyond simple speech-to-text conversion. The system must be able to understand context, intent, and semantic meaning in real-time. This likely involves large language models trained specifically for conversational understanding, combined with real-time processing capabilities that can analyse speech as it happens.

The challenge here is significant. Traditional AI assistants have the luxury of processing complete queries before responding. Proactor.ai must analyse partial conversations, understand incomplete thoughts, and make intelligent predictions about what assistance might be needed - all while the conversation is still in progress.

Perhaps even more challenging is the system's ability to conduct research and provide relevant information in real-time. When Proactor.ai identifies a topic that requires additional context, it must quickly search through vast amounts of information, synthesize relevant findings, and present them in a format that's useful for the ongoing conversation.

This capability suggests integration with multiple data sources, sophisticated information retrieval systems, and the ability to quickly summarise and contextualise information. The AI must also understand what level of detail is appropriate for different situations, providing a quick overview during a fast-paced meeting versus more detailed analysis for strategic planning sessions.

The memory capabilities described by Proactor.ai represent another significant technological achievement. The system must not only store conversation transcripts but also understand the relationships between different pieces of information, track ongoing projects and decisions, and learn from past interactions to improve future assistance.

This requires sophisticated data modelling that can represent complex relationships between people, projects, decisions, and outcomes. The AI must also be able to quickly retrieve relevant historical information when current conversations touch on related topics.

Proactor.ai enters a crowded market of AI assistants and meeting tools, but its proactive approach represents a potentially significant differentiation. To understand its market position, we need to examine both the current competitive landscape and the broader trends driving demand for AI-powered productivity tools.

The AI assistant market includes established players like Microsoft's Cortana, Google Assistant, and Amazon's Alexa, as well as newer entrants focused on business applications. Meeting AI tools include companies like Otter.ai for transcription, and Zoom's AI Companion for meeting summaries.

However, most of these tools remain fundamentally reactive. They excel at tasks when prompted but don't proactively identify opportunities to provide assistance. This represents a significant opportunity for Proactor.ai to establish a new category of proactive AI assistants.

Proactor.ai's approach aligns with broader industry trends toward more autonomous AI systems. As large language models become more capable and reliable, there's growing interest in AI that can take independent action rather than simply responding to prompts. This shift is evident in developments like AI agents that can complete complex tasks, autonomous coding assistants, and AI systems that can manage entire workflows.

The key challenge for autonomous AI systems is trust. Users must be confident that the AI will take appropriate actions and won't cause problems by acting without explicit permission. Proactor.ai's focus on providing suggestions and information rather than taking direct actions helps address this concern while still delivering proactive value.

Any AI system that continuously listens to conversations and has access to sensitive business information raises important privacy and security questions. Proactor.ai must address concerns about data storage, access controls, and the potential for sensitive information to be inadvertently shared or compromised.

The company's success will likely depend partly on its ability to provide strong security guarantees and transparent privacy controls. Organisations considering adoption will need assurance that their confidential discussions remain protected and that the AI's proactive capabilities don't create new security vulnerabilities.

The value proposition of proactive AI assistance is compelling, but organisations will ultimately evaluate Proactor.ai based on measurable returns on investment. The potential benefits include reduced meeting time, improved decision-making speed, better follow-up on action items, and enhanced knowledge sharing across teams.

However, quantifying these benefits can be challenging. Unlike tools that automate certain tasks with clear time savings, proactive AI assistance provides value that's often intangible, such as better insights, improved preparation, and  enhanced collaboration. Proactor.ai will need to help organisations measure and demonstrate these benefits to justify adoption costs.

While Proactor.ai's proactive approach represents an exciting advancement in AI technology, several challenges could impact its adoption and effectiveness.

Proactive AI systems face a fundamental accuracy challenge that reactive systems can avoid. When an AI responds to a certain prompt, users can immediately evaluate whether the response is helpful and accurate. With proactive assistance, the AI must make assumptions about what users need, and these assumptions might sometimes be wrong.

If Proactor.ai frequently provides irrelevant suggestions or misinterprets conversation context, users may lose trust in the system and disable its proactive features. The AI must achieve a high accuracy rate in understanding context and predicting needs to maintain user confidence.

There's also a risk that proactive AI assistance could create information overload. In a dynamic conversation, there might be multiple topics that could benefit from additional research or context. If the AI provides too much information or suggestions, it could become distracting rather than helpful.

Proactor.ai must carefully balance being helpful with being unobtrusive. The system needs sophisticated filtering mechanisms to determine when proactive assistance would be valuable versus when it might interrupt the natural flow of conversation.

For organisations to fully benefit from Proactor.ai, the tool must integrate seamlessly with existing workflows and systems. If using the AI requires significant changes to how teams conduct meetings or manage projects, adoption may be slow regardless of the technology's capabilities.

The company must also consider how proactive AI assistance affects team dynamics. Some team members might become overly reliant on AI suggestions, while others might find the constant assistance distracting or undermining of their expertise.

The development of increasingly autonomous AI systems raises important ethical questions. As AI becomes more proactive and capable of independent action, we must consider the implications for human agency, decision-making, and professional development.

There's a risk that over-reliance on proactive AI could diminish human skills in areas like research, analysis, and strategic thinking. If AI systems consistently provide answers before humans have a chance to think the problems through for themselves, we might see a gradual erosion of these critical capabilities.

Proactor.ai represents what could be the beginning of a significant shift in how we interact with AI systems. Rather than tools that respond to our commands, we're moving toward AI partners that anticipate our needs and proactively provide assistance.

As the technology matures, we can expect proactive AI systems to become more sophisticated in their understanding of context and more accurate in their predictions of user needs. Future versions might be able to understand non-verbal cues, emotional context, and complex organisational dynamics that influence what assistance would be most valuable.

We might also see proactive AI expanding beyond meetings and conversations to other areas of work. Imagine AI systems that proactively identify opportunities for process improvement, suggest strategic initiatives based on market trends, or automatically prepare briefing materials for upcoming decisions.

The future of proactive AI likely involves integration with other emerging technologies. Augmented reality interfaces could display AI suggestions and research in real-time without disrupting conversations. Internet of Things sensors could provide additional context about meeting environments and participant engagement levels.

Blockchain technology might enable secure, decentralised AI assistance that doesn't require trusting a single provider with sensitive business information. Quantum computing could eventually enable real-time analysis of vastly larger datasets, making AI suggestions even more comprehensive and accurate.

If proactive AI becomes widespread, it could have significant impacts on how we work, learn, and make decisions. Meetings might become more efficient and productive, with AI ensuring that all relevant information is available when needed. Educational experiences could become more personalised and responsive to individual learning needs.

So, what’s the cost of Praoctor.ai.  On the Proactor.ai pricing page, you will find a clear breakdown of their plans, designed to suit different team sizes and needs. They offer Basic, Pro, Business, and Enterprise plans.

The basic plan is free and provides you with 30 minutes of transcription time and a hundred AI credits. However, the Basic plan only provides a preview of the proactive AI Advice functionality.

To gain full access to the Proactive AI Advice during meetings, you will need to be on at least the Pro plan, which costs $15.99 USD per month or $191.88 annually.  This plan provides a whooping 16,200 minutes of transcription time and comes with 40,800 AI Credits.

If that doesn’t feel like enough then there is also a Business plan that increases the transcription time to 43,200 minutes and comes with just 109,000 credits with an increased cost of $39.99 USD per month or $479.88 annually.

Beyond this level there is an Enterprise plan, with unlimited recording for the entire organisation and allows for custom integrations.  This plan comes with custom pricing, where you need to speak with a sales representative to gain further information.

If you need more transcription time, it is also possible to purchase additional hours at discounted rates with add-on data packs, which range from $9.99 to £99.99.

So, in a nutshell, Proactor.ai has a tiered pricing structure that scales with your team, from a basic plan to a customisable enterprise-level solution.

Proactor.ai represents a fascinating glimpse into the future of artificial intelligence, a future where AI doesn't just respond to our requests but actively participates in our work and decision-making processes. The company's vision of proactive AI assistance addresses real pain points in how we currently interact with AI systems and offers compelling benefits for productivity and collaboration.

The technology faces significant challenges, from accuracy and integration concerns to broader questions about the role of AI in human decision-making. However, if Proactor.ai can successfully navigate these challenges, it could establish an entirely new category of AI tools and fundamentally change our expectations for AI assistance.

Whether Proactor.ai succeeds or not, the concept of proactive AI assistance is likely here to stay. As AI systems become more capable and reliable, the shift from reactive to proactive assistance seems inevitable. The question isn't whether this transition will happen, but how quickly and how successfully companies like Proactor.ai can make it a reality.

For organisations considering adoption of proactive AI tools, the key is to start with clear use cases and measurable goals. While the technology is promising, it's still emerging, and early adopters should be prepared for a learning curve as they figure out how to best integrate proactive AI into their workflows.

As we continue to explore the frontiers of artificial intelligence, tools like Proactor.ai remind us that the most significant advances often come not from making AI more powerful, but from making it more thoughtful about when and how to apply that power. In a world where information is abundant but attention is scarce, proactive AI assistance could be exactly what we need to work more effectively and make better decisions.

Proactor.ai is certainly a fascinating new tool but the big question is will you be adopting proactive AI into your organisation?  Do you see it as a game-changer for productivity, or are you a little more hesitant about the idea? Or perhaps intend to wait until the mainstream and established AI tools start to offer the same functionality?  I’m sure it won’t be long before we start seeing this functionality in tools like Microsoft Copilot.  I would love to know what you think.

Head over to the social media channels, you can find Inspiring Tech Leaders on X, Instagram, INSPO and TikTok. Let me know your thoughts.  Are you a "yes," a "no," or a "maybe"?

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Thanks for listening, and until next time, stay curious, stay connected, and keep pushing the boundaries of what is possible in tech.