There is a sea change happening in the Human Capital Management systems market. Historically, the predominant orientation of human resources departments has been about mission and goals from an employer’s perspective, spanning areas such as regulatory compliance, workforce costs, efficiency and effectiveness levels, and actions needed to improve skills and overall impact. This rather one-sided focus is now in the rearview mirror of many successful organizations. There’s a new orientation or operating lens as it relates to the enterprise’s workforce: “What does a worker need to be extremely effective but also have a high-quality ̶ if not positively memorable ̶ experience at work?”
There are many software components that facilitate contact center operations. Historically, the industry has relied, in part, on niche or best-of-breed applications but this is shifting in favor of broadly integrated suites or ecosystems. When we look at CX trends beyond the contact center, the shift is even more pronounced, with the bundling/collection of applications from martech to CRM-incorporating software that were formerly/previously purchased separately.
Observed both here and elsewhere, average sales quota attainments appear to be in an exorable decline. As I discussed in my recent Analyst Perspective, "The Art and Science of Sales from the 'Inside Out'," vendors of sales technology have reacted to this by adding a slew of new functionality including the potential for artificial intelligence (AI) to be a game changer for sales. One can argue that this use of AI is still relatively immature having been generally available only since 2014, but that is still over five years of market availability.
The joining forces of two sizable companies, in this case totaling over 12,000 employees, can be expected to elevate both business risk and business opportunity. The risk side of the ledger typically impacts employees and customers. Employees become distracted or have their productivity dip until they know exactly how they will be impacted and what is changing, or even leaving voluntarily. Similarly, a segment of existing and potential customers view a merger as a net positive down the road but face fear, uncertainty and doubt about when those benefits will be achieved. Delays can lead both employees and customers to hitch their wagons to other horses as it were.
Machine learning is valuable for organizations, but it can be hard to deploy. Our Machine Learning Dynamic Insights research identifies that not having enough skilled resources and difficulty building and maintaining ML systems are pressing challenges organizations face in applying ML. Traditional ML model development is resource-intensive, requiring significant domain knowledge and time to produce and compare dozens of models. And as the number of ML models grow, their management becomes difficult. By bringing automation to ML, organizations can reduce the time it takes to create production-ready ML models. AutoML can also enable organizations to make data science initiatives more accessible across the organization.