Jun 2025|Adrenalin

A Practical Guide for Bringing Gen AI into Your Team

It has been two and a half years since ChatGPT and Generative AI (Gen AI) entered our lives, sparking a wave of excitement, bold predictions and endless possibilities. Alongside the early enthusiasm came scepticism, wild imaginations and countless debates about what this technology could mean for the future. Fast forward to today, and the landscape feels more complex - some are disappointed by the limits they’ve encountered, some are feeling the fatigue of constant AI talk and some remain firmly resistant.

So, what is Gen AI to us now - and more importantly, what will it be moving forward?

The reality is, Gen AI is here to stay. We no longer have the luxury of ignoring it or waiting for it to ‘settle down’. What we do have a choice in, however, is how we adopt it. And many of us are not getting it right - especially when it comes to bringing Gen AI into our teams and embedding it meaningfully into our operations.

Product leaders are no exception. They do not need to become AI specialists, but they must develop a sharp, working understanding of what Gen AI can and cannot do. This is essential to making informed decisions, guiding their teams and building the right strategies for the future.

Reality check

At Adrenalin, we have been exploring, learning, trialling and iterating Gen AI usage - from general daily operations to niche expert division tasks. Based on our experience, while generative AI holds immense potential, it remains a powerful tool - not a replacement for expertise or human creativity. Here’s what we have discovered:

Gen AI performs best with expert guidance - Generative AI shines when guided by domain experts who can craft precise prompts, validate outputs and refine results. Without this input, outputs risk being generic or misaligned with specific business objectives.

It requires contextual knowledge - AI lacks situational awareness and cannot independently adapt outputs to nuanced contexts, such as brand tone or audience needs. Human expertise bridges this gap, ensuring outputs are relevant and impactful, especially for complex or high-stakes scenarios.

Collaboration is key - Generative AI works best as a partner to human teams, enhancing tasks like drafting, brainstorming and analysis. However, it cannot replace critical thinking, creative ideation or the human ability to connect disparate ideas in innovative ways.

Customisation demands human input - AI-generated outputs often require significant tailoring to meet unique client or project requirements. The more specific or specialised the task, the greater the need for human oversight and intervention.

Strategic thinking remains human - While Gen AI excels at tactical execution, it struggles with strategic foresight, empathy and complex decision-making - skills essential for digital product leadership and delivering exceptional customer experiences.

While AI may be more cost-effective, efficient, and potentially more impartial solutions than humans, it still relies heavily on the human elements that you and your team bring when it comes to decision-making: an understanding of the company's purpose, vision, mission, culture and ethics.

So, does this mean we stop with Gen AI and wait? Absolutely not. As Heraclitus famously said, "The only constant in life is change." Change is inevitable, and while we approach it with consideration, we also embrace it. We're not waiting for perfection but looking at how we can effectively combine human expertise with AI to create augmented solutions.

The way forward: augmented Gen-AI

Augmented Gen AI is all about enhancing human capability with AI-powered tools, rather than relying solely on AI to drive decisions or outcomes. The key to success with this approach lies in setting your team up for success by ensuring they are well-equipped to work in tandem with AI. However, it’s essential to recognise that achieving this synergy requires thoughtful integration, the right mindset, and continuous learning to leverage AI’s capabilities effectively. In this section, we'll explore the strategies and best practices to maximize the benefits of augmented-gen AI.

Mindset: leading the shift with confidence and clarity

Successful AI adoption begins with the right mindset. Leaders must cultivate confidence in generative AI’s potential while preparing for challenges such as scepticism and uncertainty. Embracing patience and clear communication lays the foundation for a positive cultural shift that empowers teams to engage with AI constructively.

Stay determined and believe in the shift - Generative AI is shaping the future of work. Confidence in its potential - despite imperfections - motivates teams and stakeholders to see AI as a valuable enabler, not just another tool.

Prepare for scepticism and resistance - Not everyone will share enthusiasm. Leaders may question costs, while staff might fear job changes. Address concerns with empathy, emphasising AI’s role in augmenting, not replacing, human expertise.

Practice patience amidst uncertainty - The volume of AI information can overwhelm. Accept the learning phase, focus on incremental progress, and foster a culture that encourages experimentation and growth.

Taskforce: building a team

Implementing generative AI effectively requires a dedicated, cross-functional team. This task force brings together technical experts, internal champions, change managers, and ethics advisors to ensure AI tools are integrated smoothly, responsibly, and aligned with organisational goals.

AI specialists - Technical experts who understand AI capabilities and limitations, manage tools, and integrate and optimise use cases aligned with organisational goals.

AI ambassadors - Champions across teams promoting AI adoption, communication, and culture, ensuring grassroots acceptance beyond top-down directives.

Change manager - Manages resistance, addresses concerns, smooths the transition, and supports teams adapting to AI-powered processes.

AI legal and ethics advisor - Ensures compliance with regulations, privacy and ethical standards, reducing legal and reputational risks.

Trust: engaging employees and securing buy-in

For AI adoption to succeed, it is crucial to address employee concerns, especially around job security. Communicating a clear vision centred on value creation and involving staff early helps reduce resistance and fosters a culture of collaboration and enthusiasm for AI-powered innovation.

Addressing the fear of redundancy - Job loss fears are natural. Still, AI is a tool to enhance human capabilities, freeing time from repetitive tasks for strategic, creative, and problem-solving work.

Create a vision centred on value creation - Focus on AI as a means to drive innovation, improve customer experiences and support employee growth, not just productivity gains.

Discovery: identify opportunities and goals

Aligning AI capabilities with business processes and objectives ensures that adoption delivers a meaningful impact. Mapping workflows and defining measurable goals help organisations target AI implementation where it adds the most value, supporting strategic decision-making and operational efficiency.

Integrate AI into systems and processes - Identify where Gen AI can seamlessly enhance existing workflows - whether by streamlining customer communication, automating routine tasks, or speeding up ideation.

Optimize workflows for AI - Refine and simplify internal processes to reduce redundancies and clarify roles between AI and humans, ensuring AI boosts efficiency instead of complicating operations.

Leverage past work for AI innovation - Review previous projects to spot opportunities where AI could have added value, fostering creative ideas and confidence in AI’s potential.

Set clear AI goals with OKRs or KPIs - Use KPIs to track measurable benefits like cost or time savings, and OKRs to maintain flexibility amid AI’s evolving landscape.

Learn: building AI literacy

Widespread AI literacy is essential for unlocking generative AI’s full potential. By fostering a culture of shared learning, providing accessible resources, and offering role-specific training, organisations empower their teams to confidently and responsibly use AI tools to enhance their work.

Community and collective learning - Foster collaboration through AI learning groups or communities of practice to share insights and accelerate adoption.

Resources for continuous growth - Provide a curated knowledge hub with guides, tutorials, FAQs and case studies in varied formats to suit different learning styles.

Access to tools - Give team members hands-on access to AI platforms and test environments to build familiarity and confidence.

Tailored training programs - Offer role-specific training sessions, partnering with AI experts to ensure quality delivery.

Clear communication and information distribution - Use newsletters, town halls and meetings to share updates, best practices and success stories, reinforcing AI’s value.

Policies and documentation - Develop clear guidelines for ethical AI use, data privacy, bias mitigation and intellectual property. Maintain accessible, up-to-date documentation on workflows and troubleshooting.

Empower employees through engagement - Involve teams in pilots, gather feedback, and encourage AI-driven improvements to foster ownership and trust.

Gen AI tools

Now that we've covered the strategic approach to integrating generative AI, here is a curated list of popular Gen AI tools your team can explore (Tools are listed in alphabetical order).

General tools

ChatGPT (OpenAI) – A versatile conversational AI for text generation, problem solving and coding support.

Claude (Anthropic) – A conversational AI assistant designed with a strong focus on safety and helpfulness.

Gemini (Google) – Google’s flagship Gen AI model for search, writing and productivity integration.

Grok (X) – Elon Musk’s conversational AI assistant, integrated with X (formerly Twitter).

Llama (Meta) – Meta’s open-source large language model family, supporting advanced AI applications.

Pi (Inflection AI) – A personal AI companion focused on supportive, thoughtful conversations.

Perplexity (Perplexity AI) – An AI-powered answer engine combining search and conversational responses.

Writing tools

Copy – An AI writing assistant for marketing, content creation and copywriting tasks.

Jasper – A popular AI writing platform for creating marketing copy, blogs and social media posts.

Rytr – A lightweight AI writing tool designed for quick content drafting and ideation.

Sudowrite – An AI creative writing assistant focused on story development and editing.

Writesonic – An AI-powered platform for generating marketing content, articles and ads.

Design tools

Firefly (Adobe) – Adobe’s Gen AI suite for generating and editing images within creative workflows.

Canva AI – AI features integrated into Canva for fast, accessible design and content creation.

Craiyon – A simple, web-based AI image generator formerly known as DALL·E mini.

DALL·E 3 (OpenAI) – A powerful text-to-image generator with high-quality visual outputs.

FLUX.1 – An AI-powered design and image editing tool with generative features.

Leonardo AI – A fast, high-quality image generator popular in gaming and concept art design.

Midjourney – An advanced AI image generator known for its artistic, stylised outputs.

Stable Diffusion – An open-source AI image generation model that offers flexible, high-resolution visuals.

Audio tools

ElevenLabs – An industry-leading AI voice generator with lifelike speech synthesis.

Lalamu Studio – An AI tool for creating personalised music and audio content.

Otter.ai – A popular AI transcription tool for meetings, interviews and live events.

Wondercraft – An AI audio platform for creating podcasts and audio stories with natural voices.

Video tools

HeyGen – An AI video generator that creates talking avatar videos from text.

Peech AI – An AI-powered video editing tool for automatic content repurposing.

Runway AI – A leading AI video editing platform with powerful Gen AI tools for creatives.

Sora AI (OpenAI) – OpenAI’s text-to-video model capable of generating realistic video clips from prompts.

Coding tools

Blackbox AI – An AI coding assistant for code generation, search and debugging.

Cody (Sourcegraph) – An AI coding assistant that offers deep codebase understanding and context-aware suggestions.

Cursor – An AI-powered code editor designed for seamless in-editor coding assistance.

GitHub Copilot – A widely adopted AI coding partner for auto-completing and suggesting code.

Replit AI – An AI-augmented online IDE for collaborative coding with instant feedback.

Tabnine – An AI code completion tool that supports multiple languages across various IDEs.

Generative AI is not a fleeting trend - it is a foundational shift in how we create, collaborate, and compete in the digital age. Yet, it is not a silver bullet, nor is it self-sufficient. Its true value lies in thoughtful application, guided by human judgment, creativity and purpose.

Explore our tailored

These workshops are collaborative, one-day sessions (virtual or face-to-face) that integrate powerful AI tools into the proven design thinking methodology.

They provide your team with a practical framework for applying AI to real business challenges in ways that deliver measurable value.