With data becoming the principal source from which businesses can gain a clear advantage in the digital economy, it is important to understand the scope and scale at which AI can transform operations, both inside of the organization and out. In that sense, AI isn’t just a technical transformation; it’s a business one as well.
It’s often the case that businesses in the Nordics have struggled to successfully marry up these two sides. AI initiatives, for instance developing neural networks or enabling process automation, often sit in the domain of the company’s data, analytics, or engineering experts and are consequently shuttered off from broader business purposes.
In today’s digital landscape, leaders have a critical role in reshaping their organizational structures and priorities in order to put AI at the heart of their business operations and decision-making processes. Combining the technical infrastructures that support AI with clearly defined business objectives and a cross-functional strategy will allow these areas to work in harmony, not in isolation.
Building a technical foundation
While enthusiasm around AI is strong in the Nordic region, the mass of spending to date has typically been on experimental projects and proof of concepts with less evidence of full-scale implementations. The importance of these ‘toe in the water’ projects shouldn’t be under-estimated, however. Every organization needs to start somewhere and even the most basic AI projects help in highlighting the gaps that exist and the various building blocks that need to be in place for sustainable, longer-term progress.
The initial building block for many organizations will involve the implementation of the hardware and software layers required to run AI applications. This investment, of course, will need to be inextricably tied to use cases, requiring a foundational level of expertise in order to aid application selection and best direct resources, both financial and human. There’s also an important skills component in customizing, maintaining and improving upon these applications over time. Here, familiarity in open source can be beneficial in relieving the development burden by leveraging pre-existing tools and templates.
Making a plan for growth
Alongside improving technical awareness, businesses also need to think about how their initiatives will grow. While certain sandbox environments may suit the earlier stages of developments, eventually decisions will need to be made around the optimum environment for these applications to run at scale. Typical considerations here may be factors such as accessibility across teams, the compute requirements for increasing numbers of users and the cost of managing data. This brings focus on the need for hybrid cloud capabilities in order to aid these decisions, as well as offer access to essential cloud services for ongoing development.
Becoming a data-driven organization invariably brings a greater emphasis on the role of data science, and data literacy more broadly. More organizations are recognizing the need to have these skills available natively, serving tasks ranging from modeling and analysis through to supporting efforts in customer service, sales and marketing. For data skills to be most effectively applied they must first be tuned into corporate priorities, which requires a broader business understanding of how data links to tangible outcomes.
Plugging into business imperatives
AI presents not only an IT transformation but also a cultural shift for the organization. The utility of applications needs to be woven from back-office departments all the way through to improving customer interactions in order for results to be tied to key business imperatives such as reduced expenditure, greater efficiency or improved customer understanding.
In effect, AI should be the glue that brings together data from across the business and enables collaboration across departmental lines. This cannot happen in a siloed operational structure and needs leadership to drive a cross-functional strategy.
Two key factors to consider in this regard are responsibility and accountability. As a matter of convention, businesses will often put technical-minded people in charge of the AI initiatives. While clearly, this has its benefits – those running AI operations need to have a firm grasp of the technical details – this mindset can lead to tunnel vision and a lack of awareness of the wider business challenges. Businesses that value non-technical leaders, working in partnership with technical experts and sharing accountability for driving positive outcomes, tend to see a greater symbiosis of these two elements, which in turn helps to produce more holistic solutions that benefit the business more broadly.
Starting the journey
For businesses seeking to get started with AI developments, or to expand upon the experimental projects they already have in place, coordinating these various building blocks presents a daunting challenge. This, however, shouldn’t be seen as a burden but as the start of a multi-year journey that’s never ‘finished’.
AI initiatives don’t start and stop, instead, they continually evolve alongside the growth of the business as more information is collected and greater AI knowledge is accrued. These building blocks allow businesses to explore the power of data-led decision-making, rapid iteration and continual process improvement over time. Getting to this stage involves a fusion of technical and business capabilities that may seem unfamiliar within some organizational structures, but when they work together it can quickly mature into a winning combination.
If you would like to understand how AI can become the galvanizing force in your business, our eBook highlights common challenges and shares practical insight on how you can get started.