Navigating AI adoption in technical teams - a practical guide

At Whitesmith, we’ve helped several of technical teams integrate AI into their workflows. This article shares practical insights from our experience as fractional CTOs, focusing on what works and what doesn’t when introducing AI to development teams.

The real challenge isn’t technical

Most teams can handle the technical aspects of AI integration. However, through our work with tech companies, we’ve found that the actual challenges are organisational and human. Our experience has revealed three core challenges that consistently emerge.

Challenge 1: Teams are already overwhelmed

As we’ve seen in our fractional CTO work, teams often struggle with “Urgency Overload” - where constant firefighting and technical debt make any new initiative impossible. When leadership pushes for AI adoption, teams naturally resist what they see as another burden.

There are a few ways we approach this, usually for teams of around 15 people maximum. First of all, we should get back to basics. Constant innovation, even if AI or not, isn’t new. What’s mainly different is the rate at which this innovation occurs, which causes fear and a bigger distance from what devs are used to.

If we think about digital transformation in the days of non-AI or, to simplify, automation, there was a threshold that people could easily predict when it was worth the investment to get a return on performance. So they could set expectations for themselves and keep an eye on opportunities to manage their time, invest and learn. Nowadays, with the external rate of innovation being much more significant than devs can do internally, it’s much harder for them to gauge and predict the necessary investment since these are unknown areas. On top of that, what we tend to learn in one week, will likely be outdated in a matter of weeks again.

Hackathons / Group sessions

To counterbalance this, the critical part is to provide people with time and space. Obvious, right? But this time and space isn’t for them to learn models in specific, but for them to learn how to learn in AI days. At Whitesmith, we often do 2h hackathons every month, which are prepared in advance, and foster people to go through the biggest unknown unknowns, and get them familiar with the different ways of approaching development. The preparation is quite relevant to scope and trimming down the area of research and preventing people from getting lost and immediately overwhelmed (analysis paralysis).

This learning process shouldn’t transpire immediately into their day-to-day. Still, it should provide them with a sense of the capabilities of AI so that, they can be aware of opportunities and take them. This is the crucial part. Opportunities will be everywhere, but we need to know what this tech can or cannot do so we can better judge when to invest time to compensate.

Lead by example

One of the things that quickly helps is to show examples of AI helping within our teams. If AI is correctly applied, good leaders will quickly and organically show the value, which should materialise in a better quality of life for their teams - eg.: simplification of processes, faster development, and more flexibility to focus on the interesting things.

As @levelsio stated, don’t sell how to build, be the one building.

Remove frictions

On a more management and pragmatic approach, provide budgets for tech with no questions asked so that people have no friction to try. From Cursor, Windsurf, No-Code platforms (Dify), Perplexity, Claude, O1, everything has value as long as people can quickly try, spend a couple of dollars and move on if that doesn’t work. 

Challenge 2: Unclear strategic value

Most leaders know AI is valuable, maybe even strategic, but struggle to put the pieces together of what it is capable of and what it means to them. This can lead to analysis paralysis or the failure to apply AI as a leverage.

As always, everything starts with understanding long-term objectives, current operations and capabilities, and where the bottlenecks lie. 

AI can be a huge multiplier when applied in the right place, especially in the case of bottlenecks in the pursuit of a major objective. It does not allow, however (yet?) to skip steps in terms of organisational transformation:

1. Team alignment
2. Operating Processes 
3. Digitalisation
4. Then AI

You don’t need to be perfect on either, that’s rarely the case, but the org needs to cover and, most of all understand the key concepts and habits in each step before moving on to the next one. 

The cases where AI fails are either because it’s not aligned with the strategy or because we are trying to apply AI before having a process or a rough understanding by the team of what needs to be done. Then, one would have to solve a complex multidimensional problem and, in some cases, impossible while keeping the business running.

Challenge 3: Fear of skill devaluation / replacement

Two years ago, and sometimes still now, our team were particularly resistant to AI.

Senior developers at an enterprise client were particularly resistant to AI. After several 1on1s and team discussions, we discovered one of the main underlying concerns: they worried that AI would replace their work.

We addressed this by reshaping how they viewed their role. We showed how AI could handle routine coding tasks, freeing them to focus on architecture, mentoring, and strategic technical decisions. Their expertise became more valuable as they learned to guide and validate AI-generated solutions.

Practical Implementation Steps

Based on our successful implementations, here’s what works:

• Start with one simple, high-impact area. At a recent client, we began with AI-assisted code documentation. The immediate time savings built trust for further adoption.
• Create protected learning time. As detailed in “The Empowered Leadership Fallacy,” we emphasise the importance of dedicated time for learning and experimentation. This helps teams integrate AI without compromising existing deliverables.

Measure concrete improvements. Track specific metrics like:

• Time saved on routine tasks
• Code quality improvements
• Sprint velocity changes
• Team satisfaction scores

One of our clients saw 66% faster code deployment to production, 90% faster copyright with the same conversation rate and analysis and pattern detection of 14.000 customer reviews in under an hour.

Signs of Successful Adoption

When AI integration works effectively within an organisation, we observe several patterns that indicate positive transformation. These patterns emerge across both technical operations and team dynamics, creating a holistic improvement in how work gets accomplished.

The technical benefits of successful AI adoption are substantial and measurable. Development teams typically experience routine coding tasks being reduced by 40-60%, allowing engineers to focus on more complex challenges. Code quality metrics show marked improvement as AI tools help identify potential issues and suggest optimisations that might otherwise be missed. Development cycles accelerate noticeably, with sprints completed more efficiently and deadlines being met with greater consistency. Additionally, testing becomes more comprehensive as AI assists in generating test cases and identifying edge conditions that require validation.

Beyond the technical sphere, successful AI adoption catalyses significant team evolution. Team members report an increased focus on strategic work as AI handles more routine aspects of their roles. This shift creates additional time for innovation, allowing professionals to explore creative solutions and improvements to existing systems. 

The key to successful AI adoption lies in starting with small, manageable initiatives and maintaining a sharp focus on immediate value creation. We strongly recommend beginning with:

1. Assessment of current pain points

2. Identification of one high-impact area for AI

3. Protected time for learning and experimentation

4. Clear metrics for success

Through our fractional CTO service, we guide teams throughout this transition process. Our approach ensures that AI becomes a valuable tool that enhances capabilities rather than a technological burden that creates additional complexity. We partner with your team to navigate the adoption challenges and help realise the full potential of AI integration in your development workflow.