Rodolphe Malaguti, Product Strategy and Transformation, Conga

With over 8 years of experience as Solutions Engineer, Rodolphe Malaguti works principally in product strategy and transformation at Conga. He is interested in the software development industry and specialises in product demonstration, SaaS solution deployment, and more.


2023 has been a big year for artificial intelligence (AI). Indeed, McKinsey has referred to it as ‘AI’s breakout year,’ with 40 percent of companies reporting that they are increasing their investments in in the technology in response to its advances and market pressures[1]. AI has become a disruptor, providing new ways of operating and doing business, moving ever closer to the centre of the enterprise as leaders recognise its potential. Companies, such as BT, have gone as far as saying that AI chatbots will soon be able to make new business process suggestions, even for features that might not yet exist[2].

According to Deloitte’s report, State of AI in the Enterprise, 94 percent of business leaders agree that AI will be critical to their overall success over the next five years[3]. However, that does not mean it is easy to implement as part of a company-wide digital change programme. The same report found that nearly half of all business leaders struggled integrating AI into their business’ daily operations and workflows. In fact, Deloitte found that those organisations starting new AI projects struggled when it comes to proving genuine business value.

Why AI Projects Fail – Strategy vs Technology

Whilst AI is a promising technology, it is by no means a ‘silver bullet.’ If there are bad processes in place, automation or AI will only accelerate these issues. Similarly, AI is entirely dependent on the data that it is provided. If data is poorly structured or left unaccounted for, how can leaders be expected to use these tools to make strategic decisions or structural changes to their organisation?

Digital transformation programmes of this size are incredibly complex, and businesses cannot afford to keep redesigning or remodelling their data architecture or systems. Instead, leaders should approach this strategically and in a phased manner, by adopting one digital tool at a time, across one system or department at a time. Only then will they be able to identify any operational concerns or bottlenecks and where systems need to be better integrated. This is important, especially when it comes to scaling these solutions across their organisation and delivering a true return on investment (ROI).

AI is just like any other technology and, like most technologies, it is not necessarily easy to implement. Too often, businesses get caught up in the ‘hype cycle’ and they roll out a complex solution almost overnight, with no idea of how it will improve their day-to-day services or revenue streams. Leaders need to think carefully about why they have chosen this tool and what they are trying to achieve with it, rather than implementing a new technology as if it is going to solve all their problems.

Most digital change programmes are extremely complex because there are so many teams, processes and systems involved, many of which are siloed or disconnected. A recent report by Gartner found that, while most businesses have started using digital tools like data analytics, less than half said they are using more advanced solutions such as predictive analytics to improve their operational efficiency[4]. In fact, only 20 percent of corporate strategists reported using AI-related tools, including machine learning or natural language processing. The same report found that leveraging analytics and AI for more efficient, insightful strategy decisions is one of the biggest challenges organisations face this year, especially as budgets tighten.

When it comes to implementing AI and using data to drive decision making, it means much more than data simply being collected. The problem is not a lack of data, but making sense of this data and producing actionable insight. If a company’s data architecture is plagued by inaccuracies, poor processes or data is unstructured and siloed between departments, how can leaders implement truly meaningful change and use AI effectively? Especially when the data that they do have is not a true reflection of how the business is currently operating. In fact, companies will likely implement an AI or automation tool, which will lead to further operational complexity and increased costs in the long run.

Delivering an Effective AI Programme

Although short-term technology spending is expected to increase, businesses cannot afford to keep redesigning and remodelling AI programmes. In 2024, companies will need to review their budgets and be able justify their chosen technologies. Businesses are entering a period of great economic uncertainty. Therefore, it is vital that businesses only purchase and adopt tech that they can truly reap the rewards of.

Digital transformation is all about reconsidering the relationship between people, processes and data. Technology does not need to be ground-breaking, integrating systems and streamlining processes should be the first priority – ensuring data and workflows are properly structured and fully optimised. It is crucial that organisations first establish their ‘digital maturity,’ which is where they currently stand in their digital transformation journey and how their data is currently being processed and stored.

Before committing to a transformation project, it is critical that leaders first look at their current business model and complete a situational analysis. By reviewing their current operational model, they will be able to identify the processes that work well, as well as areas of improvement – which will in turn, dictate the level of service that is required – or establish where a particular technology or solution would be better suited and where change needs to be made. In doing so, businesses will have a much clearer understanding of how data flows between their systems, teams and throughout various departments, and better align their go-to-market (GTM) activities.

Looking to the Future

Every year there is a hype cycle and organisations rush to adopt a new technology to keep up with their competitors or in fear that they might be left behind. In the last 12 months, businesses have started the recognise the benefits of AI and have invested heavily in this promising technology, but not all have been successful. Next year is when we should expect to see better results. However, it is important that companies continue to review their operations and data architecture on a regular basis, to ensure that all systems are aligned and all data is accounted for. Only then will they empower AI and take their business to the next level. 2023 was the year of AI; 2024 will be the year businesses reap its benefits if they understand how to use it effectively.




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