How We Use ChatGPT Today

Most people use ChatGPT to save time in daily life. At work, it’s often about polishing texts: making emails more formal, shortening reports, or making presentations more compelling. In private life, it’s recipe ideas, travel planning, or a faster way of googling for facts.

That’s valuable, and for many, ChatGPT has already become an essential tool. But the use is still fairly basic and far from what the technology can actually deliver. We settle for the flashlight when the entire app ecosystem is waiting to be explored.

So Much More Is Possible

To move from flashlight to full iPhone functionality, we need a higher level of ambition. Some examples of how ChatGPT can be used more powerfully: 

• Agentic Workflow: Many companies struggle with time-consuming manual processes. ChatGPT can act as a process engine orchestrating other systems. The value: freed-up time, fewer errors, and scalability.
• Decision-support copilot: Management teams often lack quick, high-quality decision input. ChatGPT can analyze data and create scenarios. The value: faster and better decisions.
• Hyper-personalized customer interactions: Customers often face generic messages. ChatGPT can create tailored communication based on customer data. The value: higher conversion, loyalty, and customer lifetime value.
• Compliance & contract watchdog: Lawyers and auditors are overwhelmed by agreements and regulations. ChatGPT can review and flag discrepancies. The value: reduced risk and lower compliance costs.
• Internal training and onboarding: New hires and teams often struggle to get up to speed. ChatGPT can function as an interactive coach and training partner. The value: shorter onboarding times and a more consistent knowledge level across the organization.

What We Risk Missing 

If we don’t raise our ambitions, we miss the real potential. According to research, generative AI could already today create welfare gains equivalent to hundreds of billions of dollars each year. But that requires moving beyond small efficiency gains in the inbox and instead using the technology for deeper analysis, better decisions, and stronger innovation.

It’s in the strategic, creative, and long-term use that the big difference emerges. Seeing AI as more than a digital assistant, and instead as a partner in how we learn, develop, and make decisions, is the way forward.

From Flashlight to Full Functionality

To level up, we need a new perspective on technology. We must start seeing ChatGPT not just as a service for quick answers but as a partner in our thinking. Once we do that, we can use it to drive processes, strengthen decision-making, and develop new ways of working.

When we limit ourselves to the flashlight, we never see what the phone can really do. But when we unlock the full ecosystem – the apps, the camera, the communication – it reveals its full potential. The same goes for generative AI. It’s only when we use it to support research, innovation, strategic decisions, and learning that we see the true power we have access to.

When AI Becomes Both Defense and Weapon

AI has already begun to redefine cybersecurity. Cybercriminals use generative models to create convincing phishing emails, deepfakes, and large-scale automated fraud campaigns. Tools that once required advanced technical skills are now commercially available for just a few dozen dollars a month.
AI is also used to develop and modify malicious code, discover vulnerabilities, and manipulate existing AI systems – all with increasing precision and speed.

“We are seeing in practice how the same AI technology is used on both the attacking and defending sides. As we strengthen our security capabilities, new ways for attackers to exploit the technology emerge.”
– Pernilla Rönn, Head of Cybersecurity, HiQ

But AI is not just a threat. As cybersecurity solutions become more intelligent, AI is used to detect and stop intrusions far faster than before. In Security Operations Centers, up to half of all incidents are now handled with the support of AI, which analyzes logs, identifies anomalies, and prioritizes threats in real time.

Three Ways AI Is Transforming Cyber Threats 

1. Attackers scale up with generative AI. Hyper-personalized phishing emails, voice clones, and deepfakes no longer require advanced tools – only access to ready-made solutions.
2. The attack surface expands rapidly. AI generates new code, finds vulnerabilities, and bypasses defenses in real time.
3. Defenders become smarter. AI strengthens detection, analysis, and response – but requires proper implementation to avoid new risks.

A Growing Attack Surface and New Vulnerabilities 

As AI becomes integrated into more parts of the organization, both efficiency and complexity increase. New attack surfaces emerge in data flows, code, and decision-making processes. At the same time, we see a growing phenomenon known as shadow AI – AI tools used within the organization without approval, risk assessment, or testing.

“Without standardized and secure AI solutions, employees turn to their own tools. The result is a shadow-AI landscape with fragmented data, low traceability, and increased compliance risks.”
– Sofie Perslow, Head of AI, HiQ

Preventing shadow AI requires three things:

• Clear ownership of AI – with a central authority coordinating strategy, risk assessment, and guidelines.
• A culture of awareness – where employees understand both the value and risks of AI, and receive training on proper tool usage.
• Good and approved alternatives – secure platforms that are as smooth and useful as open tools, so employees don’t feel the need to bypass the organization.

When these elements are missing, invisible risks arise around data leakage, bias, and lack of transparency. This is where governance and culture become crucial – not just to avoid incidents, but to enable sustainable, business-driven AI adoption.

AI as Part of the Cyber Defense

At the same time, AI enables entirely new defensive capabilities.

• Detection and response: AI-driven XDR/NDR solutions identify anomalies in networks and endpoints in real time.
• Incident analysis: Generative models summarize incidents, triage cases, and help security teams and SOC analysts act faster and more effectively.
• Threat intelligence: AI-based knowledge graphs map leaked data, trends, and dark web activity before attackers exploit them.

“AI can make security more accurate, but only if it is built the right way. It’s about combining technological innovation with clear governance and meaningful human oversight.”
– Pernilla Rönn

As a long-standing Atlassian Platinum Solution Partner for over 17 years, we’ve spent the last five years executing numerous Atlassian Cloud migrations—ranging from straightforward single-product instances to large, multi-product Atlassian landscapes with extensive portfolios of Marketplace and custom-built apps. Our specialists combine deep, hands-on experience with a suite of in-house automation scripts and enabling technologies designed to reduce risk, accelerate timelines, and ensure a smooth transition.

Not sure Cloud is right for you?

We are vendor- and model-agnostic.
While Atlassian Cloud is the best fit for many already in the Atlassian ecosystem, it isn’t the only path. We also advise on:

• Hybrid approaches where only specific workloads move to Cloud
• Alternatives beyond Atlassian and beyond the cloud, where your use cases, regulatory context, or TCO models point to different platforms

Our commitment is to help you make a confident, evidence-based decision. That includes objective trade-off analyses, pilots or proof-of-concepts, and a transparent TCO comparison across options.

Next steps

• Schedule a readiness assessment: a concise review of your current Atlassian landscape, risks, and quick wins
• Get an app and integration scan: understand Cloud parity, remediation effort, and viable alternatives
• Receive a migration or modernization roadmap: timelines, phases, and a clear delivery plan tailored to your constraints

The 2029 timeline is a valuable opportunity to plan deliberately rather than react under pressure. Whether you move to Atlassian Cloud or chart a different course, we’re here to guide you with proven methods, experienced specialists, and the right tools for the job.

Lessons from MIT’s Report

MIT’s NANDA initiative has analyzed over 300 AI implementations. The conclusion: successful companies share a common approach – they choose a clear pain point, execute quickly, and build partnerships instead of trying to develop everything in-house.

Those who fail often invest heavily in their own internal solutions that never truly become part of everyday operations.

“We often see companies building too big and focusing too much on the technology. AI works best when it’s woven into workflows and solves a concrete problem,” says Perslow.