Preparing Now For The Technology Shifts Ahead In 2027

gettyTechnology priorities can shift quickly, and waiting until a new need becomes urgent often means higher costs, rushed implementations and operational disruption. With 2027 planning getting underway, leaders have an opportunity to identify where their systems, skills and budgets may need to evolve before next year’s demands arrive.

Planning early gives companies more time to assess their readiness, close gaps and make deliberate investments instead of reacting under pressure. Below, members of Forbes Technology Council share technology shifts leaders should be preparing for now to reduce the risk of an expensive scramble in 2027.

The shift I would prepare for now is AI moving from isolated tools into core business workflows. That changes the conversation from experimentation to architecture, governance, data readiness and operating model. Companies that wait until next year may find themselves scaling fragmented solutions instead of building an enterprise capability. - Thai Vong

Most planning cycles budget for AI tools, not AI architecture. That oversight gets expensive fast. Leaders should audit whether their systems can really share clean data across departments before adding another point solution. Disconnected tools create reconciliation work that erases the productivity gain. Fix the foundation in 2026 or you’ll be paying twice for the same transformation in 2027. - Eric Giesecke, Planet DDS

Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?

As organizations move beyond AI experimentation and into large-scale adoption, the challenge is no longer access to AI. It’s understanding consumption, forecasting costs and demonstrating business value. With agentic AI expected to accelerate, leaders should focus now on building visibility and accountability around AI usage so they can scale adoption with confidence. - Conal Gallagher, Flexera

AI use is already outpacing the policies meant to guide it. For healthcare leaders, putting governance in place now is far easier (and cheaper) than retrofitting it after AI is embedded across workflows or a problem emerges. Clear guardrails help organizations keep pace with innovation without compromising patient safety or trust. - Paige Kilian, Inovalon

AI adoption is definitely going to be top of the mind for most leaders. I believe the bigger underlying shift is going to be around preparing unstructured data for internal consumption and making it AI-ready, as well as ensuring governance of datasets. The success of AI models is directly dependent on the type and quality of data. - Sameer Zaveri, Datamotive.io

Do not plan on adopting any type of agentic AI initiative without first building intelligence on your current state and eliminating unnecessary workflows and processes. Many organizations are sitting on a goldmine of data in the form of tickets, logs, telemetry and other sources. Tapping into all of these sources, build intelligence on your current state and the impact of unnecessary workflows and operations models. Eliminate those unnecessary workflows, and you will be surprised by what is left for AI. - Murthy Malapaka, murthymalapaka.com

Leaders should budget now for a governed semantic layer—a shared set of business definitions AI can rely on. As agents move from answering questions to taking action across enterprise systems, inconsistent metrics, ownership and access rules become costly risks. Fixing that context during planning is far cheaper than rebuilding AI workflows next year. - Govinda Rao Banothu, Cognizant Technology Solutions

Security leaders need to plan for nonhuman identities. AI agents are becoming the largest identity population inside enterprises, each with its own access, permissions and blast radius, yet almost none of them are governed the way we govern people. Most security stacks were built to watch humans log in, not to watch agents act. Budgeting for that gap now will be far cheaper than retrofitting governance after an agent goes rogue or a breach makes the decision for you. - Mike Britton, Abnormal AI

Network segmentation is the shift leaders can’t put off much longer. Too many businesses still run POS systems, cameras and guest Wi-Fi on one flat network, which means a single breach can spread everywhere. Retrofitting that later means costly rework across every location right when leaders can least afford it. Better to plan it for now, before an audit or incident forces the issue. - Juho Sarvikas, Inseego

Prepare for agentic AI making changes to production systems. More teams are going to have AI acting on infrastructure, not just making suggestions, and most organizations have no framework for what those agents will be allowed to do. Leaders should prepare by building the guardrails, approval gates and audit trails now, while it’s “cheap,” because scrambling after an agent has caused an outage is not. - Yasmin Rajabi, CloudBolt

Retail leaders need to prepare for the convergence of search and conversational shopping. Amazon and Walmart were early adopters, but 2027 will bring a broader shift as shopping agents become more prevalent in retail and increasingly converge with search engines. E-commerce search bars will evolve into conversational entry points where customers can discover products, get personalized guidance and resolve customer service inquiries through a single point. - Raj De Datta, Bloomreach

AI slop is something leaders must prepare to handle now, before it gets out of hand. Leveraging AI products in a way that fundamentally improves the organization without getting into unmanageable slop is critical for any business today. Often, this comes down to being deliberate about what, where and how to use AI and building the guardrails needed to fuse it with the organization successfully. - Alekh Jindal, Tursio

One technology shift leaders should prepare for now is the move away from treating every endpoint as a traditional, Windows-dependent PC. As more workloads are delivered through virtual desktop infrastructure, desktop as a service, software as a service and cloud desktops, organizations should consider where purpose-built endpoints or repurposed hardware can reduce cost and dependency. In 2027, endpoint strategy should focus on flexibility. - Kevin Greenway, 10ZiG Technology

Generative AI is changing the economics of digital fraud. As fraud becomes easier to scale, bad actors will target automated workflows where low-value transactions receive less scrutiny. Audit those thresholds now, then layer in metadata checks, chain-of-custody tools and targeted human review. Detection must keep pace as fraud shifts from one-off manipulation to AI-enabled scale. - Matt Wielbut, Openly

Put AI spend and ROI on an equal footing. A tech leader’s job is to build the bridge between AI investment and ROI (measurable business value). AI adoption can scale quickly, but so can the token cost. The goal is not simply to control AI spending; it is to ensure every dollar of AI investment can be connected to a measurable business outcome. Leaders who establish this discipline now will avoid costly surprises later and be better positioned to scale AI with confidence. - Gaurav Vashisht, Kraken

Technology leaders should prepare for AI to shift from answering questions to taking action and for their knowledge foundations to become instrumental in determining whether that helps or hurts employees. An agent acting on fragmented, out-of-date knowledge creates a decision problem at machine speed. Technology leaders should spend the planning season on the foundational work: ownership, permissions, governance, one authoritative source and a system of record for knowledge. - Lokdeep Singh, Unily

The biggest scramble won’t be over AI models; it will be over context. Models are becoming easier to swap, but they still need accurate, real-time access to catalogs and data. As AI agents begin driving more commercial interactions, companies must ensure they can reliably find what’s in stock and relevant. Most companies aren’t currently prepared, and that is something that can’t be fixed overnight. - Stephen Lynch, Algolia

The most important technology decision leaders need to make is a human one: Where do people belong as technology changes? Keep reevaluating where human judgment matters, where people need to intervene and what they’re seeing that technology can’t. The people closest to the work will tell you where friction is building and where the operating model needs to evolve. - Craig Crisler, SupportNinja

The shift is agentic AI taking over routine planning decisions. Agents are taking on reordering, staffing forecasts and pricing adjustments, but they’re only as accurate as the data they’re given. Leaders who wait until an agent is live to clean up disconnected systems and inconsistent data will spend next year fixing bad decisions instead of scaling good ones. - Ben Hussey, Katana Cloud Inventory