Winning Businesses Should Be Using AI To Drive Growth, Not Just Efficiencies
Businesses are doing AI wrong. Or at least they aren't using AI to its full potential. In fact, more than half are using the technology to drive efficiency when it should also be driving growth. But those business using AI as a growth driver are winning; it's possible to see revenue uplifts of 5–8% when AI is growth-oriented.
So why has AI got stuck with the efficiency label? Two words: quick wins.
When AI went mainstream a couple of years ago, quick wins, like automating repetitive tasks, were crucial to securing early approval, building trust, and, well, staying in the game. Completely understandable then but thinking like that now is a major error. No, actually, it’s self-sabotaging.
What follows is why businesses need to move on - and think of AI as a growth driver first and foremost. We explain why the shift matters, how to make it happen to realise bigger benefits, with some practical takeaways and a case study at the end.
The Real AI Story
Since AI took a seat at the table, businesses have focused on automating low-value tasks. By next year, more than 75% of companies will be using it for removing humans from routine work. Indeed, Generative AI has dominated headlines, accelerating tasks like desk research and report writing — but do any of these things really drive growth?
You should be ditching the low-value mindset and think human intelligence (HI) + AI. It’s time to go beyond copilots that churn out words, images, and code, and welcome agentic AI ecosystems that collaborate with employees on planning, execution, and fine-tuning. Think of it as climbing into an AI exoskeleton to work faster and smarter, not just cutting costs but increasing business.
Here’s the reality: moving from foundational AI assistants to AI agentic ecosystems can’t be done over a weekend. It’s challenging, and as MIT has reported, 95% of AI pilot programmes for revenue generation are failing. Their contribution to the bottom line has been zero, if not negative. But the issues are well-documented; we know what companies are doing wrong, and, therefore, we know how to fix them.
No AI Growth Jam Today
We’ve identified three principal challenges for why AI is holding businesses back.
A major one is data fragmentation, which makes it difficult to triangulate data and build agentic AI solutions for better decision-making. But there is also too much faith in tools and large language models (LLMs) and too little investment in developing AI skills and mindsets.
All of these problems were highlighted in the Ipsos Data Council Report 2025, which concluded:
- Lack of strategic alignment
Businesses aren’t aligning their strategies with data. Indeed, 4 out of 5 data council members said there were no clear policies in their organisation specifying when data should be used in decision-making. This misalignment often stems from a lack of clarity around what constitutes success for the business.
- Fragmented data and legacy systems
Disparate and disconnected tools still plague many businesses. 2 out of 5 council members said significant pools of data remain siloed, and just under two-thirds reported that siloes harmed their ability to manage data effectively. Accessing granular data quickly was a major challenge.
- Difficulties implementing AI and automation
Hiring the technical expertise to deliver an AI strategy is proving difficult; however, 50% of council members admitted they could do more. Many organisations are grappling with a skills gap and don't have the people to build, manage, and optimise their AI systems.
Your Solutions for AI-based Expansion
Making AI a lever for growth needs a different outlook. Businesses must take a holistic approach, establishing five AI intentions:
1. Developing tech-savvy people
AI transformation won’t work if employees are stuck in BAU mode. Companies need to increase AI literacy across the workforce, build confidence in the technology, and create hybrid roles that combine AI expertise with business acumen.
2. Defining your risk appetite
A business needs to set clear risk thresholds that determine the level of human oversight in AI-based decision-making. As confidence in AI increases and risks diminish, the need for human intervention will gradually reduce.
3. Building a solid data spine for expansion
Without a strong data backbone, AI can’t deliver growth at scale. A good spine connects customer, product, and financial data as well as event streaming for better decisions company-wide.
4. Adding ribs that bring AI to life
Data ribs connect AI out to the overall business, making it practical and relevant to the entire workforce. Now, every department can build AI use cases for their own needs, increasing the probability of AI successfully driving growth.
5. Taking an outside-in approach
Businesses need to understand the outside world before using AI for expansion. After all, growth comes from solving customer problems, not just automating processes. Begin with market insights, then ladder down to specific challenges AI could help solve.
Case Study
Taking a holistic approach to low-risk AI use cases paid off for one client. They wanted a chatbot solution that empowered stakeholders and freed up the business sales operations (BSO) unit. Enter ‘George the Chatbot’.
By triaging the ticketing system, the BSO team reduced query handling by 30%.
More importantly, the solution provided a foundation for future expansion - acquiring greater knowledge through strategic integrations to become a growth-building insights agent.
Practical Takeaways
So, we've discussed why businesses are getting stuck in 'AI efficiency' mode, as well as how teams can shift their focus to becoming more growth-orientated with their approach to AI, and the benefits when doing so. But, what does this look like in real life and how do you get started on this journey? Here are our four top takeaways:
Get people comfortable with AI so they can frame problems properly.
Make sure your data is connected, company-wide.
Build business cases that solve actual problems.
Start with low-risk use cases and build up from there.
Get in Contact
Organisations that purely place their focus on AI as an efficiency tool need to level-up their game. We'll grant it's a great starting point for your AI strategy but, given the competition businesses are facing today, it's not enough. The real winners will be those who shake off this mindset and embrace AI as a driver for expansion.
At Ipsos Data Labs, we’re helping our customers do just this. Our expertise spans multiple industries, enabling clients to maximise the value of their technology investments. Backed by a global team of statisticians, data analysts, scientists, and engineers, we empower businesses to make smarter decisions and deploy AI with confidence.
Get in contact if you’d like to discuss this further and understand how we can support you in driving growth through AI.
