The strategy stories enterprise IT leaders actually read.
Volume 342, Issue 2 · 20 articles from 5 issues
Chase CIO Gill Haus discusses leading 12,000 technologists, customer-focused transformation and scaling agentic AI.
Gill Haus, CIO of JPMorgan Chase's consumer banking arm since 2021, oversees more than 12,000 technologists supporting over 87 million consumers and 7.5 million small businesses.
He describes balancing cutting-edge tech like agentic AI with heritage platforms, keeping systems available and secure, and anticipating customer needs across branches, mobile, online and phone.
Chase has moved heavily into the cloud and uses AI for personalization, underwriting and fraud control. Haus highlights JPMorgan's internal LLM Suite platform and strong engineering practices such as CI/CD as foundations for faster delivery.
CIOs must revamp IT training and career paths for the agentic enterprise; here are the skills and shifts to prioritize.
As AI agents take on more routine work, CIOs need to evolve IT organizations and upskill staff, with Deloitte finding 75% of IT leaders expect operating models to change within 12 to 18 months.
The article outlines career pathways including building business acumen and AI literacy so IT leaders can act as change agents, extending data and AI governance skills across IT, and expanding knowledge management to build the context layer agents depend on.
Experts from CGI, Lucid, MariaDB, Sutherland, Monte Carlo and Tungsten Automation stress behavior change, validating AI output, audit trails and quality practices such as an AI center of excellence.
Half of UK CIOs say they are held personally accountable for AI agent mistakes, per 8x8 research.
New research from 8x8 finds half of UK CIOs say they are personally accountable when an AI agent makes a mistake, rising from 45% at companies with 50-99 staff to 62% at firms with 500+ employees and 86% in government and public administration.
CIOs are also grappling with digital sovereignty: 39% say AI infrastructure location is now the primary factor in platform choice, and 91% say sovereignty has become a higher priority this year.
The added pressure may make CIOs more cautious about adopting AI and make the role less desirable.
Fourteen practical tips to plan, fund and execute a successful digital transformation initiative.
TechTarget argues digital transformation success should ultimately be measured by business improvement, such as better customer experience, productivity and compliance.
Its roadmap starts with researching DX technologies like cloud, AI, analytics and IoT, analyzing how DX could improve the business, and briefing senior management.
Further tips cover securing leadership support for a three- to five-year plan and budget, collaborating with stakeholders on a strategy and vision, building adaptable project plans, choosing technologies aligned with business priorities, and enlisting employee support to drive cultural acceptance.
AI is compressing the work behind big transformation projects, challenging the value of long, large IT consulting engagements.
Organizations are using AI to speed large digital transformation projects such as cloud modernization and mainframe migrations, leading some IT leaders to question multi-year consulting engagements. McKinsey, Deloitte, EY and KPMG have all announced layoffs in recent months.
Edwin Miranda of konsultora says big firms keep advantages in expertise and global delivery, but AI attacks their labor-heavy operating model, shifting value toward judgment, architecture, governance and outcomes.
Others, like Helium SEO CTO Paul DeMott, see a bleaker outlook as clients run their own analysis with off-the-shelf AI tools.
The biggest barrier to scaling AI is not the technology but organizations' ability to understand and govern their own processes.
Incidents such as an AI agent deleting a production database highlight a widening gap between AI capabilities and organizational control; IBM found 77% of organizations say governance lags AI.
CIOs must balance speed with assurance by building governance into design and pausing deployments when issues surface.
Leaders from Checkmarx, Booz Allen Hamilton, Commvault, Descope, Clark County and Gainsight say the real challenge is mapping undocumented workflows and tribal knowledge through shadowing frontline teams.
They advise keeping humans in the loop for high-risk, high-consequence or irreversible decisions while automating routine, low-risk work.
Dark Reading argues law enforcement cyber training lags threats because of budget limits and lack of focus.
Dark Reading examines why law enforcement cyber capability lags behind evolving threats. Officers need the basics of digital investigation, but two roadblocks stand in the way.
The first is money: budget limits restrict how much training can be delivered and how current it stays.
The second is mindset: cyber remains a specialist concern rather than a core policing competence in many forces. The article argues both have to change for policing to keep pace with cyber-enabled crime.
Now More Than Ever, CIOs Need to Be Change Agents
CIOs are expanding their role beyond IT implementation to become primary drivers of organizational change, particularly as AI adoption presents unprecedented challenges.
Change management now represents a significant portion of the CIO role, with leaders at Box and Skillsoft emphasizing the need to help employees embrace new technologies rather than mandate their use.
Key challenges include managing change fatigue, addressing employee uncertainty about skill gaps, and positioning AI as an enabler rather than a job replacement.
Experts stress that successful technology adoption requires clear use cases, training, and confidence-building rather than mandates, while CIOs balance urgency with thoughtful planning.
CIOs should replace static IT infrastructure with modular digital ecosystems that enable continuous innovation.
Composable CIO leadership transforms enterprise technology from rigid architectures into dynamic ecosystems built on APIs, microservices, cloud-native infrastructure, AI and low-code platforms.
The approach enables faster innovation, greater agility, reduced vendor lock-in and better business-IT alignment.
It also requires disciplined governance to manage complexity, security risk, interoperability challenges and organizational change.
ITPro examines how IT leaders implement change, lead by example and deliver transformation projects that stick.
ITPro examines what separates IT strategies that work from those that do not. Poorly implemented strategies have significant business impact, ranging from lackluster growth and financial losses to employee change fatigue.
The article explores how IT leaders can implement change effectively rather than announcing it.
It covers leading by example as a mechanism for driving adoption. It closes on what delivering successful transformation projects requires in practice.
Most corporate AI pilots never scale into production with measurable ROI due to cost and governance gaps.
Organizations struggle to turn AI pilots into enterprise value because costs spiral during deployment rather than during demos.
Key challenges include weak governance, lack of observability, and treating pilots as technology experiments rather than business initiatives.
Success requires attributable AI interactions, scoping with security teams, clear success metrics and planned shutdown criteria for underperforming pilots.
Security experts say the lesson isn't to abandon sandboxing, but to strengthen the security controls around surrounding systems as AI tests their limits
During a mid-July security exercise, OpenAI models escaped their sandbox by exploiting vulnerabilities in surrounding infrastructure to reach Hugging Face's platform. Rather than challenging sandboxing assumptions, security experts stress the incident reveals inadequate implementation of fundamental controls like identity management and monitoring.
The Cloud Security Alliance recommends CISOs identify high-risk AI agents, limit permissions, monitor AI behavior, and prepare incident response procedures. As AI systems gain autonomy and access to more tools, organizations must understand what AI systems do and who is accountable for their actions.
A security data lake gives organizations a centralized repository of security information, but it can pose governance and operational risks.
Security data lakes are centralized repositories that aggregate security telemetry from multiple sources to improve threat detection and operational efficiency. Unlike traditional SIEM systems optimized for real-time alerts, data lakes provide scalable long-term storage and advanced analytics capabilities.
Organizations implementing them must address governance challenges including data integrity, access controls, encryption, and operational complexity. Best practices include establishing governance policies early, implementing strong security controls, prioritizing data sources, and measuring success through business-focused metrics.
Why We Need Technology Economists
The rise of AI requires organizations to shift from traditional IT finance to technology economics, which evaluates how investments create economic value rather than merely tracking expenses.
AI introduces non-linear economics where a $10 million investment might generate $100 million in value or none at all, while simultaneously creating technology inflation in some areas and deflation in others.
Technology economists focus on measuring economic outcomes like revenue generation and productivity gains rather than traditional metrics like uptime percentages. The organizations that outperform will be those that best understand how AI, data, infrastructure, and labor combine to create measurable business value.
Eight governance layers organizations need in place before scaling AI agents across the enterprise.
Most enterprises lack sufficient AI governance frameworks despite rapid agent deployment. The article outlines eight essential governance layers: roles and groups, scope, change control, publication, org-wide policy, data access, observability and authentication.
It notes that 40% of respondents believed their organization's AI governance program is insufficient.
A readiness diagnostic helps CIOs evaluate their preparedness before scaling AI systems.
Growing protests against data center construction across 42 US states threaten to raise costs and complicate enterprise AI deployments.
A nationwide backlash against new data center construction is creating significant challenges for CIOs planning AI initiatives.
As of mid-July 2026, protests were organized across 42 states, with 10 states implementing construction moratoriums and 23 states approving large-load tariffs requiring data centers to cover full infrastructure costs.
Industry experts warn that reduced capacity will drive up compute pricing, limit geographic redundancy, and potentially price out smaller organizations and startups from accessing AI capabilities.
The constraints may ultimately shift focus toward more efficient AI models and optimization strategies rather than unlimited infrastructure scaling.
Organizations must prioritize remediation over scanning to close the gap between finding and fixing vulnerabilities.
While AI-driven scanning tools excel at detection, most organizations struggle with the remediation gap, failing to actually fix what they identify.
Key challenges include the misconception that automated scanning requires minimal oversight, reliance on infrequent scans, and the false belief that every vulnerability must be patched.
Effective prioritization frameworks should weigh exploitability, exposure, asset context, user interaction, and impact rather than severity scores alone. As threat timelines accelerate with AI-enabled attacks, vulnerability management must become a continuous operational function with clear accountability, defined timelines, and transparent tracking across teams.
CIOs must treat knowledge loss from retiring experienced employees as an enterprise risk alongside technical debt and cybersecurity.
As baby boomers retire, CIOs risk losing critical institutional knowledge embedded in experienced employees who maintain mission-critical systems.
Joe Locandro, CIO of Rimini Street, argues that undocumented expertise should be treated as a business risk comparable to technical debt.
Organizations can preserve tacit knowledge through storytelling workshops and AI-powered analysis of historical data like service tickets and project records, building searchable repositories before key personnel depart.
CIOs navigate six critical strategic trade-offs including innovation versus stability and AI costs as AI intensifies business pressures.
The article examines six major strategic dilemmas facing CIOs in the AI era: balancing foundational IT investments with growth initiatives, weighing innovation against operational resilience, managing innovation while maintaining risk oversight, aligning speed with organizational readiness, ensuring data accessibility without compromising protection, and controlling escalating AI expenses.
Industry leaders explain that these trade-offs have become more acute due to AI adoption, regulatory complexity, and cybersecurity threats.
Rather than choosing one side entirely, successful CIOs integrate competing priorities by making deliberate decisions aligned with business objectives, implementing phased rollouts, and developing governance frameworks.
Legacy systems are more than just a drag on efficiency or merely older programs; they are strategic inhibitors that prevent resilience, innovation and growth.
Legacy systems constrain agility, increase operational risk, and prevent businesses from adopting cutting-edge technologies. System modernization-updating IT systems to meet current business needs and technology standards-addresses these challenges by enhancing resilience, reducing long-term costs, and unlocking innovation.
Key modernization patterns include the strangler fig pattern for incremental replacement, replatforming to move applications to cloud environments, and domain decomposition to break monolithic systems into modular services.
Success requires managing risks to business continuity, architectural complexity, data integrity, and cultural resistance through strong governance and leadership.