From idea to reality: how to scale up a GenAI project

Plenty of companies have big ambitions for Generative AI. Fewer have managed to turn their plans into reality. 

Google Cloud research published in August found that 47% of 2,508 firms with revenue above $10m had taken GenAI from idea to rollout within six months, while 34% achieved this aim within three months. More than half of those surveyed reported at least a 6-10% rise in annual revenue.

The problem is that a number of companies are stuck in what Google Cloud calls “pilot purgatory”. The research found that 38% of the UK-based respondents admitted that they are yet to implement GenAI in production, indicating that they’re struggling to scale up the deployment. 

The report argues that firms struggling to scale up the use of AI systems should focus on aligning AI strategies with business outcomes if they’re to avoid pilot purgatory. But what does this mean in practice?

C-suite buy-in 

Dell Technologies launched its Dell AI Factory earlier this year, a collaboration with AI chipmaker Nvidia to help businesses integrate GenAI applications into their operations.

“Aligning GenAI strategies with business goals means moving beyond a fascination with hype and toward a deeper understanding of how the technology can enhance the business,” says Steve Young, UK senior vice president and managing director at Dell Technologies. 

Or take Expedia. In summer 2024, the travel tech firm launched an AI assistant, Romie, on EG Labs, where users can test and play with experimental products. 

While Romie is in the first stage of testing, the company is already learning from users’ feedback on how Romie’s features, including AI search, can be integrated into its product offering, says Shiyi Pickrell, Expedia’s senior vice-president of data and AI.

To successfully scale up a GenAI project, leaders must “build a strong test-and-learn culture”, says Pickrell.

For firms to know exactly what they want from GenAI, full support and buy-in from all C-suite executives is required. Despite the buzz GenAI has created over the past couple of years, there’s still plenty of hesitation around its adoption, whether that’s because leaders are stuck in their old school ways of thinking or they’re concerned about the return on investment. 

The key to winning over reluctant C-level executives is to show them how GenAI can solve real business challenges, argues Kristof Symons, CEO International at Orange Business. In March 2024, the company launched two GenAI products for French enterprise customers. “When leaders back AI, it sends a strong signal: this is important and we’re all in this together,” Symons says.

Paul Cardno, global digital automation and innovation senior manager at 3M, thinks GenAI must be “positioned as a strategic investment”. He recommends demonstrating its value by highlighting how competitors have used the technology to improve productivity and deliver efficiencies and cost reductions. 3M is “prioritising GenAI projects that are helping individuals to do their jobs, like content creation and process automation, as these directly support our core objectives”, adds Cardno. 

The C-suite must also tolerate failure. Young says some executives can be “paralysed by indecision” when it comes to investing in GenAI because of the potential for an idea to fail. “Investing in a project that fails could be damaging, but failing to act quickly enough could be more so,” he points out. 

‘Data quality is king’ 

In their haste to deploy GenAI, firms often rush the rollout and end up overlooking data quality and management. This inevitably leads to some pilot projects failing to take off. 

“Data quality is king,” says Symons. To perform reliably, GenAI algorithms need accurate and relevant data, so it’s essential to build a team that’s equipped with skills and knowledge in AI, machine learning and data science. Without the right expertise, firms can struggle to develop their ideas successfully. 

Algorithms can pose data privacy and security concerns; they could potentially bring legal consequences if things go awry. Navigating these challenges requires a risk mitigation strategy to ensure that GenAI solutions comply with regulations and can be seamlessly integrated into existing systems without causing a legal headache. 

Cardno stresses the need for all those involved in a GenAI project – from the engineering team to the legal affairs department – to pull in the same direction. This requires leaders to establish a culture of trust, not just in the GenAI solution that’s being built but in each other as well, he says. 

Train to empower

If a GenAI project is to be deployed successfully, all employees, not just engineers and data scientists, must believe in it. As Symons puts it, leaders need to “demystify GenAI and show it as a tool for everyone”.

This means ensuring the technology isn’t just for a select few, he says. “Democratise it. If only certain employees get access, others might feel left behind, which can create further resistance. There must be AI equity within the business, because without it you risk a disparity that may see some employees get ahead of those that don’t have access.”

Both Symons and Young emphasise the need for education and training to support those who aren’t confident in using GenAI. By empowering employees and arming them with knowledge, they’ll get a better understanding of the benefits the technology can bring to the workplace. This will likely help pilot projects be more successful, but there may still be some pushback.

“It’s important to acknowledge there may be some short-term trade-offs for long-term gains,” says Pickrell. “There are no quick wins with GenAI. For it to truly deliver on its potential, it requires large quantities of high-quality data and a highly skilled team. It must be seen as an essential part of the business infrastructure.”