As the second half of 2026 rolls forward, technology leaders are deciding which internal initiatives will best position their organizations for what comes next. AI is obviously shaping many of those decisions, but necessary work extends beyond adopting new tools to strengthening data, modernizing systems, improving security and redesigning how work gets done.
Tech leaders are challenged with readying their organizations for a more AI-intensive, interconnected and security-conscious business environment—one shaped by rising infrastructure demands, evolving cyber risks and the approach of quantum-era requirements. Below, members of Forbes Technology Council discuss the projects their teams will focus on in the latter months of 2026 and explain why those priorities matter now.
Proving AI Unit Economics And Scaling Digital Labor
Our first priority is proving unit economics. Are we adopting best practices and seeing outputs and outcomes rather than replacing human costs with token costs? The second priority is where the leverage lives: Expanding digital workers lets us scale delivery and multiply what a lean team can ship. Together, tight token economics and scaled digital labor make the ROI undeniable. – Kathryn Harrison, Concentrix
Building A Digital Twin Of Technology Operations
We are focusing on establishing a digital operational twin of our technology organization. Beyond systems monitoring, it will model dependencies between teams, platforms, vendors and business outcomes. The objective is to predict organizational bottlenecks before they affect delivery. As ecosystems become more interconnected, understanding operational cause and effect becomes as valuable as technical observability. – Jagadish Gokavarapu, Wissen Infotech
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Consolidating Fragmented Internal Tools
In late 2026, we’re focused on consolidating our internal tooling into a single integrated platform. Years of point solutions left us with fragmented data and duplicated effort. By unifying systems, we cut maintenance overhead, improve security visibility and give teams cleaner data to build on, laying the groundwork for reliable AI adoption rather than bolting it onto chaos. – Dennis-Kenji Kipker, cyberintelligence.institute
Automating Repetitive Manual Work
Automation of manual processes continues to be a key focus—not to reduce headcount, but to elevate the level at which our team members operate. By transforming previously mundane, manual and highly repetitive tasks into efficient, business-enabling processes, our team members can elevate their strategic impact on the organization. This will deliver a far greater ROI than when people are focused on delivering manual processes. – Mark Brown, The Mark of Security Ltd.
Preparing Networks For AI And Post-Quantum Security
Regulated enterprises face 2027 quantum-safe deadlines and growing AI inference demand at the same time, and both problems live at the network layer. My team is combining post-quantum cryptographic readiness with AI network modernization so customers can solve both in a single architectural motion. They need one path forward, not two. – Rishi Katdare, Amazon Web Services
Unifying Disconnected AI Systems
We’ll be focused on getting our AI integrations to actually talk to each other. Most organizations, including ours, have spent the last two years adding AI capabilities layer by layer. The next phase is making those layers work as a cohesive system rather than as a collection of independent tools. That’s where the real efficiency gains live, and honestly, that’s where most teams are going to separate themselves from the ones still just experimenting. – Nick Damoulakis, Orases
Building A ‘Company Brain’ For AI
We’re building a “company brain”: a shared data estate that pulls tribal knowledge out of people’s heads and turns it into infrastructure for deploying AI. Without it, companies are flying blind when allocating work between people and agents, and that’s why most AI investments aren’t paying off. That’s the problem we’re solving from the inside out. – Andrew Antos, Klarity
Inventorying Systems For PQC
We’re rolling out AI productivity tools more broadly and getting our post-quantum cryptography project moving forward. We did an AI pilot in March; it went well. Now everyone has it, and we are seeing a lot of success. The PQC journey is beginning with an inventory of our critical and high-impact systems. We’ll look at the medium- and low-impact systems in early 2027. – John Bruggeman, CBTS
Strengthening Trust In Enterprise Data
In the latter months of 2026, my focus will be on improving trust in enterprise data by strengthening end-to-end visibility, from source systems to analytics platforms. As organizations rely more on AI and self-service reporting, consistent data quality, lineage and governance become critical for making faster decisions with confidence. – Govinda Rao Banothu, Cognizant Technology Solutions
Deepening Client Workflow Research
Our main focus will be going deeper into how clients actually run risk assessment—not in a nosy way, but in a “let us understand your workstreams better so we can find the most effective use of our product and data” way. The real value comes from fitting intelligence into the right decision points. We are already doing this today but not yet at the scale we are planning for the second half of 2026. The goal is to help clients get more precise results from the same core capabilities. – Artem Lalaiants, RiskSeal, Inc.
Reinvesting AI-Driven Time Savings In Customer Growth
Our biggest focus in the second half of 2026 is not deploying more AI. It’s reinvesting the time AI gives us back into customer interaction and customer acquisition. – Rachid Labrik, Slimstock MEA
Making The Data Platform Truly AI-Ready
We’ll be making our data platform genuinely AI-ready, not AI-adjacent. Most enterprises bolted AI onto infrastructure never designed for it, resulting in brittle pipelines and data AI cannot trust. We are investing in metadata, lineage and the semantic layer so agents get consistent, well-governed answers wherever data lives. AI is becoming the primary consumer of enterprise data, and we are building for that. – Emma McGrattan, Actian
Building AI-Native Operational Platforms
In the latter half of 2026, our focus will be on building AI-native operational platforms that turn enterprise data into real-time decisions. The priority isn’t adding more AI tools; it’s embedding AI into workflows where actions happen. This matters because organizations that reduce the gap between insight and execution will outperform those that simply generate more information. – Narendra Lakshmana Gowda, Walmart Global Tech
Redesigning The Operating Model Around Human-AI Collaboration
In the back half of 2026, our focus is building a future-ready operating model: embedding AI agents into real workflows, with humans in the lead. The goal is not automation for efficiency’s sake. It is designing a system where AI handles the repeatable and humans own the judgment. That shift from tool adoption to operating model redesign is where transformation actually happens. – Anna Drobakha, Groupe SEB
Rebuilding Workflows For AI-Enabled Work
We are spending 2026 consciously discarding previously held beliefs about how our business operates. AI’s greatest value will only come once we enable new ways of working, not simply make existing processes faster. Our focus is redesigning workflows around this reality so our teams can spend more time on strategic, high-impact work. – Mike Gianoni, Blackbaud
Applying AI To Quantitative Modeling And Evaluation
Our primary focus for the remaining part of the year is better integrating AI into building fit-to-purpose quantitative models and writing reports. Our goal is to enable our clients to work with increased efficiency and to shorten timelines across the clinical and regulatory approval phase of drug development. We also aim to develop AI to be better able to evaluate these models and reports so we can cut back on the effort going into manual reviews. – Mirjam Trame, Certara
Maturing Third-Party AI Risk Management
Vendors are embedding AI into existing products faster than traditional assessments can keep pace, often without clear disclosure. The priority is building a continuous third-party AI risk program that’s aligned with recognized standards and integrated with procurement and that produces evidence-based assurance rather than questionnaires nobody trusts. – Nitin Agarwal, Luminace
Connecting Legacy Systems To AI Through Open-Source APIs
Our primary focus is on installing open-source API wrappers around core legacy infrastructure. Instead of executing high-risk, multimillion-dollar platform overhauls to adopt new AI capabilities, this approach allows us to securely stream clean, compliant, real-time data to modern models. It lets us scale AI safely, protect hospital margins and maintain complete technical independence. – Mahendran Chinnaiah
Tracking And Optimizing AI Costs
As teams adopt AI, inference and token spend is becoming the new unaccounted-for cloud bill. We’re building visibility that ties spend to ROI by token, workload and team, with GPU and Kubernetes right-sizing underneath. Most organizations still can’t say what their AI actually costs or returns, and that gap only widens as adoption increases. – Yasmin Rajabi, CloudBolt
