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Top 6 Technology Trends to Watch in 2018

Sridhar Iyengar

Making predictions is always a gamble. But given the way 2017 played out and the way 2018 is shaping up, odds are that the technology trends discussed below will play a significant role in your IT department this year.

Growing use of artificial intelligence, machine learning with data analytics, and business intelligence

Business applications continue to churn out large volumes of data, and users are trying to mine that data to determine patterns and predict user behavior. In ecommerce, users want to know customers' buying patterns, which will help market products better. Website designers want to understand how visitors move through their sites in order to improve conversion rates. And companies want to analyze their sales data to correlate marketing dollars spent with sales dollars generated.

Business intelligence and data analytics activities are becoming easier to perform, and that's driving their adoption in mainstream businesses that are seeking to make better, faster decisions.

Rise of AI-powered chatbots in customer service and support

Over the past few years, chatbots — the automated, human-like chat responders — have been more of an experiment, with limited adoption. Now, chatbots are becoming more mainstream as people see the benefits of those experiments, especially in customer service and support.

AI-powered chatbots are learning how to respond to customers and predict what they want

Unlike human customer service and support reps, chatbots don't have the physical and mental inconsistencies that can degrade service levels. Moreover, AI-powered chatbots are learning how to respond to customers and predict what they want. Based on customer history or questions customers ask during a chat session, AI-powered chatbots can ask users what they need and even ask leading questions, all to improve the support experience.

Use of natural language processing as a new form of human-computer interface

"Star Trek" fans aren't the only ones who've been waiting for this prediction to manifest. Business users, too, are eager to have computers understand natural language.

Take a sales manager who wants to generate a quarterly report. If the manager has to ask for it from an analytics specialist, the manager has to explain what she's looking for and hope the specialist accurately translates her request into something the computer can process in order to generate the information she wants. Natural language processing bypasses the analytics specialist and lets the manager work with a computer directly via speech. In response, the computer may generate a visual or auditory response, depending on the manager's preference.

Tightening of data protection laws

Everything is heading toward digitization. Every business process, every technology, everything done with information — from storing, transmitting and processing it — it's all in digital form. Now, a lot of countries are recognizing that their citizens' personal data needs to be protected.

In addition, they're recognizing that users have to opt-in to these digital relationships, and they have to know the reason their personal data is being provided to a data process or data consumer and know what the consumer will do with their data.

Tighter data protection laws are designed to secure their citizens' privacy as well as prevent data abuse and outright criminal activity such as fraud or theft. The most recent example of this is the European Union's General Data Protection Regulation (GDPR). While some countries like India are also coming up with data protection frameworks, others will enhance their existing framework.

Continuation of cloud adoption in mid-sized and larger enterprises

Cloud is a mindset. Governments and larger enterprises have been slower to adopt that mindset, preferring a private cloud/private data center strategy as a starting point. Now, the biggest barriers to their cloud adoption — security and data privacy risks — are well understood and processes and mechanisms have been put in place to mitigate them. Enterprises now also recognize that most cloud companies invest heavily in the security of their cloud infrastructure, platforms and cloud applications. And they recognize that, in most cases, the security teams of the cloud companies are much larger and much more experienced than their own.

Overall, the larger enterprises are finally becoming comfortable and confident with cloud security and the cloud itself. Governments are also taking the steps to put citizen-facing, non-sensitive data and applications on the cloud.

Use of blockchain in enterprise security for identity management

Blockchain provides a distributed, secure and unique system of records, so you can have a strongly encrypted authentication mechanism that prevents malicious users from breaking in. This makes it a great choice in terms of enterprise security, especially for an identity access management system, which manages user logins and authentication.

In 2018, we'll likely start seeing blockchain adoption in areas such as banking, financial services and healthcare.

Hot Topics

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

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.

Top 6 Technology Trends to Watch in 2018

Sridhar Iyengar

Making predictions is always a gamble. But given the way 2017 played out and the way 2018 is shaping up, odds are that the technology trends discussed below will play a significant role in your IT department this year.

Growing use of artificial intelligence, machine learning with data analytics, and business intelligence

Business applications continue to churn out large volumes of data, and users are trying to mine that data to determine patterns and predict user behavior. In ecommerce, users want to know customers' buying patterns, which will help market products better. Website designers want to understand how visitors move through their sites in order to improve conversion rates. And companies want to analyze their sales data to correlate marketing dollars spent with sales dollars generated.

Business intelligence and data analytics activities are becoming easier to perform, and that's driving their adoption in mainstream businesses that are seeking to make better, faster decisions.

Rise of AI-powered chatbots in customer service and support

Over the past few years, chatbots — the automated, human-like chat responders — have been more of an experiment, with limited adoption. Now, chatbots are becoming more mainstream as people see the benefits of those experiments, especially in customer service and support.

AI-powered chatbots are learning how to respond to customers and predict what they want

Unlike human customer service and support reps, chatbots don't have the physical and mental inconsistencies that can degrade service levels. Moreover, AI-powered chatbots are learning how to respond to customers and predict what they want. Based on customer history or questions customers ask during a chat session, AI-powered chatbots can ask users what they need and even ask leading questions, all to improve the support experience.

Use of natural language processing as a new form of human-computer interface

"Star Trek" fans aren't the only ones who've been waiting for this prediction to manifest. Business users, too, are eager to have computers understand natural language.

Take a sales manager who wants to generate a quarterly report. If the manager has to ask for it from an analytics specialist, the manager has to explain what she's looking for and hope the specialist accurately translates her request into something the computer can process in order to generate the information she wants. Natural language processing bypasses the analytics specialist and lets the manager work with a computer directly via speech. In response, the computer may generate a visual or auditory response, depending on the manager's preference.

Tightening of data protection laws

Everything is heading toward digitization. Every business process, every technology, everything done with information — from storing, transmitting and processing it — it's all in digital form. Now, a lot of countries are recognizing that their citizens' personal data needs to be protected.

In addition, they're recognizing that users have to opt-in to these digital relationships, and they have to know the reason their personal data is being provided to a data process or data consumer and know what the consumer will do with their data.

Tighter data protection laws are designed to secure their citizens' privacy as well as prevent data abuse and outright criminal activity such as fraud or theft. The most recent example of this is the European Union's General Data Protection Regulation (GDPR). While some countries like India are also coming up with data protection frameworks, others will enhance their existing framework.

Continuation of cloud adoption in mid-sized and larger enterprises

Cloud is a mindset. Governments and larger enterprises have been slower to adopt that mindset, preferring a private cloud/private data center strategy as a starting point. Now, the biggest barriers to their cloud adoption — security and data privacy risks — are well understood and processes and mechanisms have been put in place to mitigate them. Enterprises now also recognize that most cloud companies invest heavily in the security of their cloud infrastructure, platforms and cloud applications. And they recognize that, in most cases, the security teams of the cloud companies are much larger and much more experienced than their own.

Overall, the larger enterprises are finally becoming comfortable and confident with cloud security and the cloud itself. Governments are also taking the steps to put citizen-facing, non-sensitive data and applications on the cloud.

Use of blockchain in enterprise security for identity management

Blockchain provides a distributed, secure and unique system of records, so you can have a strongly encrypted authentication mechanism that prevents malicious users from breaking in. This makes it a great choice in terms of enterprise security, especially for an identity access management system, which manages user logins and authentication.

In 2018, we'll likely start seeing blockchain adoption in areas such as banking, financial services and healthcare.

Hot Topics

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

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.