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Generative artificial intelligence (AI) has already transformed industries, from marketing and software engineering to customer support and education. But as we inch closer to the release of GPT-5, the expectations are shifting dramatically. This isn’t just about better performance—it’s about a whole new paradigm in how we collaborate with machines.

OpenAI CEO Sam Altman has hinted that GPT-5 will introduce significant upgrades: multimodal capabilities, persistent memory, agentic behavior and enhanced reasoning. These aren’t just technical milestones—they’re steps toward a future where AI serves as a proactive teammate rather than a reactive tool.

At SeniorExecutive.com, we asked our AI Think Tank to weigh in. What do these advancements really mean in practice? How will GPT-5 change the way executives, teams and entire industries interact with artificial intelligence? The responses reveal a common theme: a shift from command-based interactions to dynamic, intelligent collaboration.

From Reactive Tools to Intelligent Agents

“GPT-5 feels like the moment we stop just using AI and start working with it,” says Divya Parekh, founder of The DP Group, where she advises leaders on AI strategy and executive communication. “I’m expecting true multimodal integration—text, image, audio—all handled seamlessly. But what really excites me is the evolution in reasoning and tool use.”

Parekh anticipates a model that can adapt, remember and act on its own: “Persistent memory and adaptive behavior could push GPT-5 into agentic territory: delegating tasks, interfacing with [application programming interfaces], maybe even orchestrating workflows autonomously. That changes the game for enterprise, research and personalized experiences.”

“It’s the evolution from question-answering to an autonomous agent that plans and executes.”

Multimodality, Memory and Meaningful Conversations

Jim Liddle, Chief Innovation Officer of Data Intelligence and AI at Nasuni—a cloud-native data platform built for the explosion of unstructured data—believes GPT-5 will unify previously distinct AI functionalities into a single seamless experience.

“Rather than switching between models for voice, image or text, GPT-5 could intelligently adapt to the task across all of them. It’s the evolution from question-answering to an autonomous agent that plans and executes,” he says.

Liddle also highlights an important shift in functionality: better memory and longer context windows that allow AI to stay grounded in extended conversations and documents. Those longer context windows are technology with which other companies, like Google and IBM, have already found success. As Google explains, “Context windows are important because they help AI models recall information during a session.”

Liddle adds, “We’re talking about more analytical depth and the ability to manage workflows independently.”

Driving Real-World Outcomes Through Automation

Roman Vinogradov, VP of Product at Improvado, a platform that centralizes marketing analytics with AI, foresees GPT-5 as a gateway to modular, autonomous systems.

“GPT-5, shaped around a superagent architecture, could intelligently delegate tasks to specialized agents—optimized for specific domains like coding, marketing analytics or medical advice. That means better precision, integration with third-party tools and highly personalized outputs.”

In his view, this move would eliminate friction and increase trust: “You’re no longer guessing what prompt works best. The system knows what you need and gets it done.”

“Multimodal capabilities and personalization open the door for smarter contact centers, better healthcare diagnostics and scalable education.”

Scaling Intelligence Across Industries

For Egbert von Frankenberg, CEO of Knightfox App Design, a firm helping small and medium-sized enterprises (SMEs) integrate AI into operations, GPT-5 could create a unified intelligence layer that transforms entire sectors.

“GPT-5’s improved context retention, multimodal capabilities and personalization open the door for smarter contact centers, better healthcare diagnostics and scalable education solutions. Advanced voice mode and deep research functions could redefine accessibility and productivity.”He points to Altman’s suggestion that GPT-5 may surpass human-level performance in niche domains as a sign that AI is shifting from a helpful tool to an independent contributor.

Bridging Data, Speed and Ethics

Suri Nuthalapati, who leads Data and AI Practice for the Americas at Cloudera, a hybrid data platform managing vast enterprise datasets, expects significant leaps in performance and reliability.

“GPT-5 could enable real-time, persistent interactions that are contextually aware—like talking to an assistant who actually remembers you,” says Nuthalapati. He also expects lower latency, stronger reasoning and better safeguards for bias and misinformation.

“As it evolves toward smarter decision-making, GPT-5 becomes not just scalable, but trustworthy—key for enterprise-grade adoption.”

Enhancing Enterprise and Developer Experiences

Nikhil Jathar, CTO of AvanSaber, an enterprise software firm pioneering AI and XR-driven business tools, emphasizes GPT-5’s potential to handle increasingly complex tasks.

“More accurate content generation, better reasoning and perhaps native multimodal inputs will reduce the need for human intervention in repetitive workflows,” he says. “It’s going to enable higher-value work and tighter alignment between AI output and business needs.”

“It’s a shift from reactive AI to cognitive assistants—software that collaborates.”

The Human Factor: Personalized Collaboration

Sarah Choudhary, CEO of ICE Innova t ions, a tech company advancing intelligent mobility and smart automation platforms like ICE Ride and iChef, sees GPT-5 as the first model that behaves like a true partner.

“Instead of responding, it might proactively refine answers, ask follow-ups or integrate directly with systems to act,” she says. “It’s a shift from reactive AI to cognitive assistants—software that collaborates, not just computes.”

Looking Ahead: An AI Future Fueled by Partnership

As GPT-5 nears release, what becomes clear from these expert insights is that we are entering a new phase of human-AI interaction. This isn’t just about speed or convenience—it’s about trust, autonomy and the delegation of real responsibility to AI systems that can think, learn and act on our behalf.

From superagents to multimodal intelligence and dynamic task execution, GPT-5 promises to be more than just an upgrade—it may very well be a shift in how work gets done.

If GPT-4 laid the groundwork for generative AI’s mainstream adoption, GPT-5 could be the inflection point that makes AI indispensable—not as a tool, but as a collaborator.

The EU AI Act Is Here—But Will It Lead the World Forward or Hold It Back?

The long-anticipated EU Artificial Intelligence Act marks a defining moment in the global governance of AI. Hailed as the world’s first comprehensive legal framework targeting AI development and deployment, the legislation introduces a risk-based classification system and compliance obligations intended to ensure transparency, safety and ethical standards across AI technologies.

But among innovators, regulators and startup leaders, the act has stirred a deeper debate: Will regulatory clarity drive innovation, or will strict rules end up pushing progress elsewhere?

To explore both sides of this global inflection point, we turned to the AI Think Tank—a diverse collective of industry pioneers, technologists and strategists shaping the next generation of AI tools and systems.

“Clear rules help businesses operate with confidence, but if regulations become too restrictive, they might push great, worthy research elsewhere.”

Clarity vs. Compliance: Walking the Line Between Progress and Protection

For many, the appeal of the EU AI Act lies in its effort to impose order on an increasingly complex landscape. With AI now deeply embedded in everything from hiring algorithms to critical infrastructure, a regulatory framework brings sorely needed clarity.

Roman Vinogradov, VP of Product at Improvado, sees the risk classification model as a potential strength.

“The EU AI Act’s risk-based classification creates essential regulatory clarity, yet to truly accelerate innovation, policymakers must pair enforcement with targeted incentives… regulatory sandboxes, compliance grants and streamlined certification processes tailored for startups and SMEs.”

Indeed, small and mid-sized enterprises (SMEs) are at the heart of the innovation ecosystem, and without clear support structures, these companies could bear the brunt of compliance burdens.

Sarah Choudhary, CEO of ICE Innovations, echoes this concern from a technologist’s perspective.

“Clear rules help businesses operate with confidence, but if regulations become too restrictive, they might push great, worthy research elsewhere.”

The overarching fear? That Europe could unintentionally create an innovation outflow, where cutting-edge AI projects migrate to regions with fewer barriers.

“The EU AI Act, like much of the proposed regulation, is overly broad and burdensome to startups.”

The Compliance Burden: A Startup Dilemma

Startups, often strapped for resources and under pressure to move fast, may feel the weight of compliance most acutely.

Peter Guagenti, CEO of Integrail, argues that the EU AI Act may be a step too far for small players.

“The EU AI Act, like much of the proposed regulation, is overly broad and burdensome to the startups that represent the biggest opportunity for positive economic and social benefits from AI.”

He points out a dilemma many founders face: either comply and slow innovation—or shift operations to jurisdictions with lighter regulatory oversight.

Jim Liddle, Chief Innovation Officer at Nasuni, also warns of a possible bifurcated future.

“The worry is that the Act risks creating a two-tier development ecosystem where cutting-edge innovation happens outside EU borders while regulated AI evolves more slowly within them.”

And while the EU AI Act does provide structure, its effectiveness could be undermined without global alignment.

“No regional AI regulatory framework can achieve complete effectiveness due to the global nature of AI development and deployment,” Liddle continues. “Genuinely effective AI governance requires international coordination and standards harmonization, but geopolitical competition for AI leadership makes this very unlikely.”

A Global Domino Effect—or Diverging Roads?

While the EU may be first out of the gate, other regions are eyeing different paths to AI regulation. Some may follow Europe’s lead, while others could prioritize economic agility over caution.

Gordon Pelosse, EVP at AI CERTs, breaks it down by region:

“The United States is expected to maintain its competitive edge through a sector-specific regulatory strategy. China maintains stringent AI control measures… The UK, Canada and Australia probably prefer flexible, principle-based guidelines instead of strict regulations.”

This could create a fragmented global regulatory environment, where businesses must navigate a maze of region-specific rules—challenging the scalability of AI solutions and increasing operational complexity.

“Compliance costs and complex approvals could hinder rapid prototyping.”

Suri Nuthalapati, Data and AI Leader at Cloudera, points to the tradeoff this introduces.

“Strict regulations on high-risk AI may slow experimental innovation, particularly for startups. Compliance costs and complex approvals could hinder rapid prototyping and global AI deployments.”

However, Nuthalapati also sees potential upsides.

“By defining AI risk categories, [the EU Act] provides structured guidelines that can accelerate innovation by reducing uncertainty. Businesses can align AI strategies with compliance early, fostering responsible AI development.”

A More Balanced Approach: What’s Next for Global Regulation?

In contrast to the EU’s sweeping framework, some regions may take a more modular or sector-specific approach—especially in tech-heavy economies like the U.S.

Nikhil Jathar, CTO of AvanSaber Technologies, foresees a fragmented beginning but eventual convergence:

“I anticipate other regions will adopt a more nuanced approach, potentially focusing on sector-specific regulations or lighter-touch guidelines. We might see a fragmented global landscape initially, eventually converging on core principles.”

So, what might encourage that convergence? Open dialogue, global forums and cross-border cooperation on shared values like data privacy, human rights and algorithmic transparency.

“We might see a fragmented global landscape initially, eventually converging on core principles.”

Actionable Strategies for Business Leaders

As regulatory landscapes evolve, AI-driven companies must prepare for a future shaped by compliance. Here are five actionable strategies to stay ahead:

Map Risk Levels
Assess your AI systems against the EU’s risk categories (unacceptable, high, limited, minimal) to understand where your offerings stand.
Build a Compliance Culture
Start integrating compliance teams into the product development lifecycle. Don’t wait for enforcement to begin adapting.
Leverage Regulatory Sandboxes
Where available, participate in regulatory sandboxes to test innovations in controlled environments.
Invest in AI Governance Tools
Use platforms that support compliance automation, documentation, model explainability and audit trails.
Monitor Global Policy Trends
Track AI policy evolution beyond the EU to prepare for a multi-jurisdictional compliance landscape.

The Blueprint, the Burden, and the Balance

The EU AI Act may be a bold first step toward establishing global norms for artificial intelligence, but its ultimate impact depends on how well it balances innovation enablement with risk mitigation. The AI Think Tank members collectively emphasize that while regulatory clarity is welcome, overly prescriptive rules could suppress the very innovations they seek to protect.

The next chapter will be written not only in Brussels but in Washington, Beijing, London, and beyond. For now, one thing is clear: the race to regulate AI is underway—and every region must decide how to compete without compromising its values or economic potential.

Get the Power of AI—Without the Enterprise Price Tag

In today’s climate, staying lean isn’t just a strategy—it’s a mandate. Businesses are scrutinizing every expense, reworking workflows, and asking tough questions about where to invest. For many leaders, AI still feels like something reserved for enterprise giants with deep pockets and massive tech teams.

That’s no longer the case.

A new wave of affordable, accessible AI tools is quietly transforming how small and mid-sized businesses operate. From automation and customer service to hiring and logistics, AI is now built into many of the platforms you already use—or could use with a surprisingly light lift.

If you’re evaluating tools that help you do more with less, here’s a breakdown of where AI can make an impact without demanding a hardened tech budget.

Turn Data Into Decisions—Fast

Data is only valuable if you can actually use it. AI tools are making it easier to understand what’s happening in your business and why, without the need for analysts or complex reports.

PostHog offers AI-powered product analytics that help track user behavior, feature adoption, and retention. Great for startups and free up to 1M events/month.
Tableau brings your data to life through AI-enhanced dashboards and predictive visualizations. Designed for decision-makers, with scalable pricing.
Medallia uses AI to analyze customer feedback across channels—surveys, social, web, and support tickets—helping businesses respond quickly to trends and improve the customer experience.

Whether you’re trying to understand user behavior, improve product features, or extract insights from customer feedback, these AI tools make it easier to turn complex data into confident decisions—no analyst required.

Automate Repetitive Tasks (And Buy Back Time)

Every growing business has bottlenecks. These tools use AI to clear them—automating busywork so your team can focus on what matters.

Zapier connects tools like Gmail, Slack, and Trello to build automated workflows with no code. Its AI-enhanced logic builder makes setup easier than ever.
Notion AI turns your docs and projects into intelligent assets. It can summarize meetings, write content, or generate task lists from notes.
Linear uses AI to streamline issue tracking and product development. It’s popular with startups and built to stay fast and simple.
Lindy builds personal AI assistants that can manage your inbox, schedule, and support queries—customizable for your specific workflow.

These tools don’t just save time—they help you shift focus from repetitive busywork to the kind of high-value tasks that move your business forward.

Elevate Customer Experience Without a Large Team

AI lets you meet customer expectations at scale—without bloating your support staff or sacrificing personalization.

Intercom uses AI-powered chatbots and message flows to handle support tickets and onboard users automatically.
Survicate makes it easy to collect and analyze customer feedback with AI-enhanced surveys and integrations into your favorite CRMs.

With AI handling the front lines of customer support and feedback collection, you can offer personalized experiences at scale—without hiring an entire support team.

Professional Branding, No Designer Required

Creating high-quality visuals used to require a creative team or expensive freelancers. Now, AI tools put that power directly in your hands.

Secta Labs delivers high-quality, customizable headshots that are ideal for personal branding, team bios, or pitch decks. Whether you’re refreshing your LinkedIn profile or launching a new website, Secta helps you look polished without the studio fee.
Canva has introduced a suite of AI-powered design tools, including Magic Design (auto-generates layouts), Magic Write (AI copywriting inside your design), and AI image editing. It’s free to start, with affordable Pro plans for teams and businesses.

Whether you need branded content, social media graphics, pitch decks, or team headshots, these tools make it easy to look professional—without hiring an agency.

Smarter Operations & Logistics

If your business involves physical products, these tools use AI to streamline fulfillment and forecasting—helping you deliver faster with fewer errors.

Coupa supply chain solutions offer AI-driven supply chain design and planning capabilities, enabling businesses to predict and adapt to disruptions before they happen. By creating digital twins of your supply chain, you can run limitless scenarios to optimize performance and resilience.
ShipBob brings AI into eCommerce fulfillment, helping small brands automate inventory management and real-time shipping updates.

Managing fulfillment, inventory, and shipping doesn’t have to be overwhelming. These AI tools help you streamline logistics and optimize operations—so you can focus on growth.

Keep Your Business Secure

AI also helps you prevent fraud and manage risk—especially in fast-moving digital environments.

Sift uses machine learning to monitor transactions and detect suspicious behavior before damage is done.
Feedzai offers AI-powered financial crime detection, particularly useful for businesses handling payments or operating in regulated sectors.

From fraud detection to real-time risk monitoring, these tools help you safeguard your business—proactively and affordably.

Streamline Hiring and Internal Communication

Hiring and managing teams doesn’t have to be resource-intensive. These tools take much of the manual labor out of the equation.

Fireflies records, transcribes, and summarizes meetings with AI—perfect for interviews, team syncs, and client calls. Learn more
hireEZ uses AI to source and match candidates, accelerating hiring pipelines and saving hours of sourcing.
Zoho Recruit includes AI features that scan resumes and match candidates to job descriptions, streamlining your hiring workflow.

Hiring, onboarding, and internal collaboration gets faster and more efficient with the right AI tools—giving your team more clarity and your business a competitive edge.

AI Is Powerful, But People Make It Smarter

Adopting the right tools is just one piece of the puzzle. Making the best decisions—what to automate, how to scale, when to pivot—becomes much easier when you’re not doing it alone.

The smartest AI investments are often made alongside a community of forward-thinking builders, operators, and technologists who are experimenting, sharing learnings, and asking the same questions you are.

If you’re looking to explore the latest tools, compare notes, and find clarity in a fast-moving space, consider joining a professional community where AI believers gather to collaborate and grow. Innovation moves quickly. You don’t have to navigate it solo.

Just about every industry in America has been rocked by the Great Resignation, but as any CPA will tell you, staffing shortages are nothing new for most accounting firms. In a 2021 survey by the American Institute of Certified Public Accountants (AICPA), staffing topped the list of concerns for firms of all sizes and has been a top concern since at least 2015. Obviously, the pandemic and ensuing burnout have exacerbated matters, but there are industry-specific issues that make its impact on accounting a different case.

From stagnant wages to stressful tax seasons, the industry as a whole isn’t doing enough to entice younger generations to pursue a career in accounting or to convince seasoned professionals to stick around. While there is plenty of technology available to help firms begin to fill these gaps, accountants have historically moved slowly when it comes to adopting new methods, in part due to the various laws, regulations and tax codes that change nearly every year, making implementation of any new tech tricky at best. Another frequent barrier to would-be adopters is the specialized and ever-evolving jargon of these technologies.

As the founder of an artificial intelligence (AI) bookkeeping software company, I often find that clients’ hesitancy to adopt new technology is mostly due to a lack of understanding of how these developments work generally and how they can support their efforts specifically. Accountants need to understand the technology that’s being marketed to them and the language used to discuss these features in order to ask the right questions and make informed decisions on behalf of their firm—the future of the industry depends on it. Let’s dig in.

A Brief History of Software Development

In order to understand the technology behind AI and why it’s useful for accounting, it’s important to review how we developed AI by exploring the iterations of technology that came before.

Generally speaking, software development began as an effort to make manual processes faster and easier to replicate. A lot of first-generation softwares were built to aid a human user in doing specific things, not to do those things on their own on behalf of the user. From a labor standpoint, this was still a huge improvement on the old model and provided a measurable jump in the efficiency of storage and accessibility. Once those early softwares came around, we were largely able to say goodbye to shuffling papers and rifling through file cabinets.

Although it was impressive at the time, challenges quickly arose because one software was great at one task and another software great at another task, but neither could be used in place of the other. Suddenly, we had a bunch of siloed applications and people had to utilize multiple apps at a time to accomplish a single task. In the accounting sector, that meant you had an app to pay bills, an app to invoice clients, an app for task management, and so on.

“New technology can be incredibly intimidating, but don’t let that stop you from bringing your firm into the future.”

CEO & Founder of Botkeeper

Senior Executive Contributor

To solve for this, developers built systems that allowed you to move data between unrelated parties, making the data more accessible. This allowed us to skip the step of manually duplicating information across systems, a process that took more time and was obviously susceptible to human error. This was the first stage of real automation, which we call robotic process automation (RPA).

Then developers started to think, “Can we program scripts and softwares to replicate what the human is doing in the software?” So, for example, let’s say a lead comes in and the human takes the email address, copies it, types a message and sends it. Developers asked, “Is it possible to build a script that tells the computer to grab this email address, put it in this other email, type a message and shoot it off in a more automated fashion?” Obviously, it was possible, which was great, but at the end of the day, these softwares were designed to work the way that humans work. The downside to this approach is that every time a tool or an interface changes, the technology breaks down.

Here’s an example I like to use: Let’s imagine we’ve tasked a robot to put dishes in the kitchen sink. With RPA, we can tell the robot, “Go forward, take a left, turn right and drop the dishes in the kitchen sink.” But what happens if the kitchen sink moves? You or I, as a human, would understand that the sink moved and adjust accordingly. But the robot would drop the dishes on the ground or fail and do nothing. That’s RPA. And in an ideal world, if one thing changes, the script breaks, and nothing happens. Unfortunately, what tends to happen is one thing changes and the script still executes, but now it’s executing a mistake. When that happens, you have to find all the mistakes, stop them from happening and undo the damage which can be a nightmare.

Artificial Intelligence: The Next Frontier

This is where machine learning (ML) and AI came in. Once again, developers aimed to mimic the human user, but decided, this time, they weren’t going to mimic specific actions, but rather mimic the human user’s logic. So, going back to our example, how do we mimic the way a human understands that the sink moved? Well, it starts by understanding what the hell a sink is.

The way we typically learn as humans—and I’m oversimplifying things here—is by seeing lots of examples of different sinks. Even if they’re made from different materials, of various shapes and sizes, in all sorts of contexts, you and I would still recognize the core attributes of a sink. So instead of telling the program, “Drop the dishes at X location,” developers taught the software how to identify a sink so that even if it comes across a sink in an unfamiliar context, the software will still execute properly. Not only that, but it will continue to learn on its own, evolving and continuing to improve over time.

But what does this mean for accounting? It means that tasks like bookkeeping, forecasting, payroll and many more can be largely automated, freeing up your time to focus on other tasks, like communicating with your clients, that robots will never be able to do.

The Future of Accounting

New technology can be incredibly intimidating, but don’t let that stop you from bringing your firm into the future. Now that you have a base understanding of how these tools work, you can keep up with the jargon and ask the right questions of potential providers before you commit to implementing their software. Seek out technology that will help stop the bleed now and make workflows easier in the future, whatever that means for your firm.

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