We’ve all seen the hype around Enterprise Big Data and AI build up over the last few years, culminating in a record year of investments, conferences and implementations in 2017. But how real is AI when it comes to building value for your business today and over the next five years?
Although we are certainly many years away from a human-like AI as we see in the movies; today, narrow or domain-specific AI technologies are already making an impact on bottom lines. Companies that have been smart about adoption and able to quietly implement AI-aided solutions into various functions such as Demand Planning and Inventory Management, Back Office Processes, Sales and Marketing are reaping the benefits.
Because AI can help companies find competitive advantages, demand is increasing at an incredible pace. New companies offering AI enabled software, and other technologies seem to pop up almost daily. Considering the amount of money and brainpower poured into AI research, it won’t be long until commercializing and monetizing data using AI as well as transforming internal processes becomes a necessity to remain competitive.
According to the recently published Teradata report State Of Artifical Intelligence For Enterprises, the majority “see AI as being able to revolutionize their businesses, automating repetitive processes & tasks and delivering new strategic insights currently not available.”
But with most enterprise software initiatives taking on average 21 months to implement and with Big Data and AI being at the complex end of the spectrum, it is no surprise that 91% see barriers ahead with lack of IT infrastructure (40%) and lack of talent (34%) as the most significant.
So how do you quickly adopt AI successfully across different business functions, driving real and immediate ROI?
AI as a Service
AI Software As a Service (SaaS) adoption is a clear trend that is taking hold in enterprise technology stacks. Adopting SaaS solutions can help companies smooth out their revenues, leading to more resilient and flexible organizations, ultimately allowing a company to deliver better service and products to their clients. With a shortage of talent in this arena and the large data sets required to effectively train artificial intelligence algorithms and implement them into production software, the SaaS model has clear advantages versus trying to develop all capabilities in-house.
The reasons for moving to SaaS offerings can be different for each organization. One of the primary drivers is the potential to create a technology advantage over established competitors and potential disruptors. Others find they’re increasingly dissatisfied with the way their legacy functions and processes run, and want a better and faster way to see improvements.
Services are defined based on business results and can be expected to produce value quickly, be flexible, implemented quickly, and paid for based on value, business outcomes, or on a seat/consumption basis. This approach leaves more room for pivoting if the ROI is not there as promised, in contrast to traditional capital investment projects where teams often fall prey to the sunk cost fallacy or have a hard time measuring the ROI of their investment.
Enterprises that transition to this model will have a definitive advantage over those that don’t. Companies that don’t shift to aaS models will see their ability to compete diminished, and the same can be said about leveraging AI enabled technologies such as Robotic Process Automation and Automated Insights Generation to name a couple of tangible applications of AI in the enterprise today.
A SaaS tech stack also offers a company greater agility. Traditional industries are consolidating amid increasing mergers and acquisitions, and that means becoming more agile and lean to compete and continue to grow. Service-based models allow companies to trim infrastructure, creating flexibility to scale up or down depending on business needs.
A SaaS model also enables better analytics to derive business insight and help make performance improvements. With clear and contained costs and sometimes built-in analytics capabilities, it is easier than ever to evaluate business results and ROI of investments in services vs. traditional Capex expenditures.
Determining how to start adopting AI technologies as well as transitioning to a SaaS and multi-cloud based stack is not necessarily easy. Where to start? With a single problem, department or business need, or do you embark on an enterprise-wide effort?
It can be as simple as starting small with low-hanging fruit and then expanding from there. Is there a department that is last on the priority list for IT but could make some significant gains if given the right tools today? Is there an apparent cost, margin or process that can be identified for measurable improvement? Companies that have seen immediate success often start small and then build on that success. Technology moves too fast these days to allow for extensive planning and execution timelines.
No matter how they get there, in the long run, businesses that transition to service-based models have incomes that are more consistent over time, allowing them to make better and more agile decisions that lead to robustness, flexibility and therefore long-term sustainability.