Rampant interest in mobile payments is expected by 2015, along with a significant increase in mobile commerce. Due to this, Gartner, Inc. predicts that by 2017, US customers' mobile engagement behavior will drive mobile commerce revenue in the US to 50 percent of US digital commerce revenue.
A recent Gartner survey found that mobile commerce currently generates 22 percent of digital commerce revenue.
"Some sectors will migrate more quickly than others to accepting mobile payments and promoting mobile commerce," said Jennifer Polk, Research Director at Gartner. "For example, big-box retailers may not need to move as quickly as other industries because the in-store experience is still a critical part of their value proposition and the customer experience, making digital and mobile commerce a smaller portion of their overall revenue. However, new credit card standards will cause a shift in liability for fraudulent transactions in 2015, requiring retailers to update their point-of-sale systems for safer credit card transactions. This opens the door for point-of-updates to also accept mobile payment."
Marketers with digital and mobile commerce initiatives need to focus on encouraging the development of cross-functional teams — including IT, sales, customer support and legal — to create seamless path-to-purchase experiences, and postpurchase relationships with consumers who are increasingly using mobile devices to research and purchase products and services. Mobile marketing teams should investigate how to leverage mobile wallets, with the expected reinvigoration of consumer interest in mobile commerce and payments.
Gartner also predicts that bBy year-end 2016, more than $2 billion in online shopping will be performed exclusively by mobile digital assistants.
Mobile digital assistant technologies, such as Google Now, Siri and Cortana, are already connecting pieces of need/want assessment, information gathering and evaluation — all elements along a buying process sans autonomous purchasing.
By year-end 2015, mobile digital assistants will have taken on mundane tactical processes such as filling out name, address and credit card information. Fixed events such as grocery replenishment will be common and will build trust for these types of assistants to take on more. By year-end 2016, slightly more complex purchase decisions, such as buying back-to-school backpacks and chained events — such as scheduling a highly rated, date-type movie along with dinner and car pickup on an anniversary — will be easily achievable. By this time, autonomous mobile assistant purchasing will reach $2 billion annually, representing about 2.5 percent of mobile users trusting assistants with $50 per year.
Hot Topic
The Latest
Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...
In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ...
Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...
Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...
Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...
The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...
The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...
In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...
AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.
The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...