Fast-track your AI project: avoid these 4 mistakes
AI projects often fail to deliver on their promise, held back by rigid management and misplaced caution. Olivia Fränti and Antti Taskinen highlight four common mistakes that organisations can avoid to turn the spark of change into a real blaze of productivity.
There’s often a striking gap between how people use AI at home versus at work. On their phones and personal laptops, people tap into AI tools with ease, while workplace software feels clunky and anything but user-friendly.
Services like ChatGPT can quickly help you replace a car key battery or decipher the error codes on your washing machine. And you don’t need to be an AI expert to set up a personal AI agent that orders the family groceries.
The difference isn’t really about technology, it’s about project management. When your car key stops working, the business case is clear and demands a fix. Similarly, a small entrepreneur can freely test different AI-based services to find what best fits their needs, without worrying too much about data security guidelines or trade secrets. In a small team, people also know their own skills and limits, and it’s easy to look up whatever additional knowledge is needed.
Scaling benefits through platforms
Large enterprises, on the other hand, face more constraints and complexity. That makes managing AI projects harder and explains why they don’t always yield the desired results.
Our market research shows that only 12% of large Finnish companies have achieved measurable productivity gains at the team or process level through AI projects. Yet among companies that have integrated AI agents into their business platforms, 70% report process-level benefits.
This proves that a rapid and scalable leap in productivity is possible if done right. By avoiding the following four common mistakes, you can ensure your project runs as smoothly as changing a battery and delivers benefits just as quickly:
1. We don’t understand how AI can be used
Very few companies’ core business involves building their own language models. What matters more for business is to understand that the platforms companies already use contain a wealth of AI features that make it easy to deploy AI agents to automate knowledge work tasks.
The most effective way to adopt AI is not to start from scratch but to leverage the investments platform providers have already made and the continuously updated features of those platforms.
2. AI is separate from our business model
The real business value of AI doesn’t come from automating isolated tasks, but from scaling automation to transform teams, processes, and entire companies. AI must strengthen and optimise the core business, making it more profitable. This means that leadership cannot outsource AI projects to the technology team or to an external partner.
This also calls for a new kind of collaboration between business and technology leadership. Change happens only when business understands technology, technology understands business and both speak a common language. A strategic partner must add value not just with AI, platform expertise and change management skills, but also with the ability to speak both business and technology fluently.
3. We focus on ownership instead of results
As technology advances, expectations for time-to-value have grown, and organisations now want tangible benefits from AI almost immediately. Yet technology leaders often still try to retain strict ownership and control of projects, slowing things down.
Stanford’s Institute for Human-Centred Artificial Intelligence suggests an alternative: run projects flexibly with a focus on outcomes, not ownership. That means setting measurable goals and letting teams own the entire AI project lifecycle from ideation to implementation. The fastest way to uncover real-world use cases and efficiency gains is to empower teams and individuals to experiment and get excited about automation in their own work.
4. We manage technology instead of change
New technology can’t just be bolted onto an existing organisation. Realising the benefits requires new ways of working. This means that everyone, from senior leaders to frontline employees, must have the skills to embrace the transformation from day one. Strong change management across the organisation is essential.
Expertise can’t simply be outsourced, but most organisations do benefit from a strategic external partner who acts as an AI coach. In fact, providing key people with personal AI trainers can be a powerful way to build confidence and accelerate adoption.
Turning sparks into a blaze of transformation
Modern business platforms provide an excellent foundation for productivity leaps, not just because of their built-in AI features and ready-to-use agents, but also because they keep organisational data and processes safe in one place. This reassures technology leaders while enabling business leaders to act quickly.
Most importantly, by building on platforms that already embed AI, organisations can achieve measurable business benefits far faster than before. What it takes is the kind of leadership that turns the spark of change into a blaze of transformation.
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