At the end of 2020, we described ServiceNow's strategy to boost appeal across the C-Suite and build high-value vertical-focused solutions. The firm's ongoing work to develop capabilities that cut to the core of industry challenges, while simultaneously scratching the itch of issues outside of IT has helped push the workflow management giant's platform into new and exciting areas of the modern enterprise. And with AI creeping further into boardroom discussions, it's somewhat inevitable that ServiceNow's automation roadmap is becoming an essential part of the conversation.
The company closed the year by announcing an acquisition that will see it jump much further along with its AI and automation roadmap. Historically, ServiceNow has been reluctant to follow its contemporaries' path by making big bang acquisitions, instead focusing on smaller tech tuck-ins and its capacity to expand across business departments and services organically. Often with a helping hand from an extensive partner ecosystem that's pushed the platform well beyond its ITSM heritage. Nonetheless, under CEO Bill McDermott, ServiceNow has made several carefully selected acquisitions with the latest, Element AI, joining Loom Systems, Passage AI, and Sweagle as the fourth AI-focused acquisition in the past year. All vital building blocks on the firm's journey to embed intelligence and automation across the platform.
Element AI has a compelling history, lauded as a leading light in AI innovation, and closing several successful funding rounds, attracting investment from firms including Microsoft Ventures. And many of the firm's projects have pushed the power of analytics and AI to tackle real business challenges. Such as an engagement with the Port of Montreal, where the firm aimed to make better predictions about how long trucks will have to wait before they pick up or drop off goods at the port. In effect, delivering data and insights that can help solve logistics challenges and drive up utilisation rates of finite business resources – a challenge faced by most businesses.
Beyond specific use cases, the firm also boasts solutions that tackle many of AI's biggest roadblocks – such as explainability – an area of increased focus from businesses, regulators, and consumers as they seek to uncover bias and mitigate adverse outcomes associated with the technology. Indeed, we can expect regulators across industry to tighten the screw as they push for greater responsibility and ownership from tech giants in a bid to calm public nerves. A fact compounded for ServiceNow, where the success of the platform's shift across business services and industries places the firm in direct contact with critical data sets, through which AI-based decision making and insight generation will undoubtedly face scrutiny.
But solving these initial challenges will be a far-reaching conversation for the tech industry as a whole, which means, for now, we can focus on the significant upside of ServiceNow pushing further along its intelligent workflow roadmap. More often than not, the value automation and AI offers to enterprises sits in two large buckets. The first is to drive insights and actions from data, and the second is to replace unnecessary and manual workloads by automating processes and workflows. The key ingredients for generating value, then, are data and processes – which ServiceNow has in abundance.
Let's take data as a starting point. Depending on the client, ServiceNow's platform stretches from IT, to HR, Customer Services, Legal and even Governance, Risk, and Compliance teams. In effect, the platform links and manages the flow of work and data across many of an enterprise's core business functions, becoming a critical 'engine room' for the business. It's conceivable, then, that an enterprise with the inclination and ambition, can map an entire customer journey from one end of the enterprise to the other. And use AI and analytics solutions to highlight blockages, touchpoints, and activities that impact the desired outcome. Without the same 'engine room' in place, mapping the business and building a holistic dataset is more challenging, if not impossible.
From here, it's not difficult to see how the second bucket of work comes into play. With data and workflows already mapped out in a single system, using AI to start taking actions isn't as far-fetched as some would believe. Suppose consistent processes are in place and mapped from IT, HR, and Payroll, for example. In that case, we can quickly whittle down the dreaded new-starter process to a few mandatory manual inputs, with the rest automated by an AI concierge – distributing different buckets of work from the relevant teams based on pre-designated pathways and historical data sets already held within the platform.
Of course, ServiceNow hasn't been sitting idly waiting for a company to buy. The core platform already boasts AI and automation capabilities. But with Element AI brought into the mix, the firm has a much stronger talent base and toolset to leverage. AI skillsets are hard-fought in the enterprise market – with enterprises and vendors competing for the same talent pools. Buying a leading light in the AI market is a smart move to bring the brains and brawn in at the scale needed to make an impact. And with an experienced team with a track record of going toe-to-toe with real business challenges, ServiceNow is better positioned to build AI solutions into the platform and continue its journey to become the engine room powering the modern enterprise.