How and when to adopt AI – a digital maturity model

Navigating what is currently the most complex business landscape to date, leaders have had to rapidly transform their operations in order to deliver their services remotely. In their panic, business leaders have invested heavily in the latest technological solutions to keep their organisations running. From artificial intelligence (AI) to wider automation, such as robotics process automation (RPA) and natural language processing (NLP) or machine learning (ML). Chaos has been the driver of change.

And over the past few months, whole departments have undergone the most intense and complicated transformation programmes their senior leadership teams have ever delivered, but that does not necessarily mean they have been well executed.

How to approach automation – it is a process, not a race

Even before the pandemic, most companies would approach digital transformation projects all wrong. Many companies aspire to be disrupters, picking a technology and implementing it at speed. They want to adopt the latest AI programme and automate their business as fast as possible, with no real idea of how this will improve their services. These projects rarely result in success. In fact, according to Conga research, only half of all digital transformation initiatives of this kind are considered somewhat successful. Compared to Europe, where the success rate is even lower, only 43 percent of these programmes result in success.

The issue lies with how businesses approach automation in the first place. Many strategies are driven by the desire to use and incorporate the latest technology as opposed to identifying clear business goals or reconsidering their current operational model and where AI would be best suited. Covid-19 has only accelerated this issue. Whilst AI and automation offer many competitive advantages, that does not necessarily mean they are easy to implement or deliver as part of a wider digital transformation programme. Too many business leaders are prioritising technology over strategy and simply do not understand what AI, or digital transformation for that matter, really is, can achieve and should drive.

Without stepping back and reviewing their current operational model, what works or what needs improving, how can leaders really understand whether AI is best suited to automate their business? Companies have essentially adopted ‘transformational’ technology without having any clear business objectives in mind or considering where this technology may be better placed to improve overall operability. If there are bad processes in place, that fail to deliver real business objectives or real commercial outcomes, automation will only accelerate this issue.

In reality, businesses need to establish clear commercial objectives, before adopting any disruptive or automation technology. It is crucial that companies first establish where they currently stand in their own digital transformation journey, by considering their own digital maturity.

The digital maturity model – how to adopt AI

‘Digital maturity’ refers to where a business currently stands in its digital transformation journey. Before adopting any new technology or starting any transformation programme, and most importantly, automating areas of the business, companies need to take a step back and reconsider their current operational model. Given the speed at which businesses transformed last year, teams may have stumbled across a number of roadblocks and bottlenecks; the ‘older’ operational model likely did not translate well in the switch to remote

working. Complicated or unnecessary processes have more than likely limited the business’ performance and stunted any commercial growth.

By taking a step back and reviewing their business, leaders will have a clear picture of the current state and what the next stage of their company’s digital transformation journey should be, as opposed to simply guessing, or learning through trial and error. Only by identifying areas where there are operational issues or room for improvement, can businesses establish clear objectives and a strategy – then leaders can incorporate new technology such as AI and automation, to streamline their services and help them achieve these goals.

Once they have assessed their current maturity, leaders can accelerate the processes that work well and can add value to their business, instead of speeding up flawed processes or legacy systems. Automating a bad process doesn’t stop it from being a bad process and, by this same logic, AI isn’t a silver bullet or a ‘quick fix’ – rushing a transformation programme won’t bolster company growth. By assessing their digital maturity and approaching automation in this methodical way, companies can improve their overall operability and streamline the processes that matter – that is, improving the customer experience, generating revenue, and managing key commercial relationships.

As companies progress along their digital transformation journeys, they will streamline processes, break down silos and enable cross-team working across departmental boundaries. The maturity model framework does not prescribe a linear change programme. It is vital that every stage, from foundation and core business logic, to reevaluating current systems, fine-tunes basic workflows to ensure any inefficiencies are removed from the overall business process. By the next stage, leaders can consider the possibility of further integration between systems, such as customer lifecycle management (CLM) or enterprise resource planning (ERP) to deliver more multi-channel management.

Only then can organisations enter the next stage of transformation. As processes are streamlined, cross-team collaboration increases and leaders will begin to break down any departmental silos, establishing true data intelligence. Their operations will be seamless with end-to-end processes that inform decision-making. Following this, leaders can then perhaps consider further integrating their systems and exploring other areas to automation and AI across other areas of their businesses, because this is now clear to them.

AI is only as good as the data provided

Businesses will proceed through these stages of digital maturity at different rates depending on the complexity of their structures, and how many roadblocks they encounter across the business cycle. No doubt some will have to go back several stages to tackle any issues regarding the business operability or efficiency. But by no means can leaders prioritise technology over strategy. If organisations think technology has all the answers and AI will solve all their problems, they are approaching transformation all wrong. Organisations need to optimise the business process; it needs to be frictionless from end to end before they consider adopting AI or any form of automation technology.

It is vital that businesses understand their digital maturity – where they are and where they need to get to – to create a transformation programme that actually aligns teams and departments. It’s important to ensure that systems, teams and processes are working together smoothly. After all, it is about establishing cross-functional collaboration, not fine-tuning a process for a particular department – whether sales, legal or finance – but improving the overall business process. By reviewing every stage of the maturity model for their organisation, from foundation (data transparency and business logic) to full system integration, leaders can take their business to a truly intelligent state, where they are actually using data to make decisions to allow for further business growth. Companies can create a seamless enterprise and a fully connected customer and employee experience, which automation can then accelerate even further. From here, AI can actually add real value.