惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

月光博客
月光博客
Apple Machine Learning Research
Apple Machine Learning Research
IT之家
IT之家
阮一峰的网络日志
阮一峰的网络日志
雷峰网
雷峰网
S
SegmentFault 最新的问题
量子位
有赞技术团队
有赞技术团队
V
V2EX
宝玉的分享
宝玉的分享
Hugging Face - Blog
Hugging Face - Blog
B
Blog
H
Hackread – Cybersecurity News, Data Breaches, AI and More
Jina AI
Jina AI
C
Check Point Blog
G
Google Developers Blog
博客园 - 叶小钗
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
博客园_首页
T
Tailwind CSS Blog
B
Blog RSS Feed
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
酷 壳 – CoolShell
酷 壳 – CoolShell
U
Unit 42

informationweek

2026 tech company layoffs How Sedgwick scaled AI in legacy claims workflows InformationWeek Podcast: CTOs on using AI in regulated spaces How top CIOs are measuring the real ROI of IT automation What AI must learn from Roosevelt, conservation and 1929 Experian's chief innovation officer gleans AI gains with startup collab ETS CIO on competing with AI startups 'running with scissors' Before the next VMware: How CIOs prepare for vendor shocks The strategic alignment powering cyber-resilient organizations The AI infrastructure bottleneck is becoming a CIO problem InformationWeek Podcast: CTOs on reining in rogue AI agents Workplace equity in the age of AI Why and how to implement an AI asset rationalization strategy Why companies are shifting toward private AI models AI agents in automation: When to build, when to buy Navan CTO AI on trial: The Workday case that CIOs can The AI infrastructure boom is coming for enterprise budgets How CIOs can manage LLM costs: A practical guide What CIOs miss when buying vertical SaaS software InformationWeek Podcast: How CTOs balance AI and their teams Whirlpool, Duke Energy, Cleveland Clinic CIOs on scaling AI Where CIOs get stuck rebuilding the enterprise: What 'Rewired' reveals As AI makes projects harder to track, will CIOs need new controls? Why disaster recovery plans fail in geopolitical crises A silent erosion of enterprise AI by data poisoning Priceline CTO prioritizes engineers able to 'hold a room and a roadmap' InformationWeek Podcast: When CTOs need to restart IT projects Wayfair CTO maps agentic path across digital and brick-and-mortar commerce The AI contract gaps the Google-Pentagon deal just made visible
How CIOs run and rebuild the business at the same time in...
2026-04-07 · via informationweek
Myles Suer,

CIO Analyst and Tech Journalist

April 7, 2026

6 Min Read

In his 1993 book "Managing with Dual Strategies," Derek Abell made a bold argument for his time: running the business and changing the business are not sequential activities -- they need to happen in parallel. He wrote that changing the business requires a clear vision of the future and a strategy for how the organization must evolve to meet it.

That's difficult in the age of AI, where there are no cookie-cutter roadmaps forward. And it's especially hard for CIOs who -- now expected to run and change the business at the same time -- still lack in many cases the deep business partnerships needed to do both well. 

The gap often starts with HR. In a recent interview, Jonathan Feldman, CIO for Wake County, N.C., said, "IT is fundamentally a people business -- and that without a strong partnership with HR, CIOs risk falling short." 

In the AI era, this partnership is critical, not optional. As partners, CIOs and HR leaders can define and shape the future of work for their companies, in close alignment with their CEOs. The risk of inaction is significant. 

Related:From Data to Doing: Agentic AI Will Revolutionize the Enterprise

Organizations that fail to adapt will face higher costs than competitors, struggle to build the workforce they need, and lack the speed required to compete in an increasingly AI-driven world. 

This article focuses on three areas CIOs must address in parallel to compete:

  • How work is changing across roles and functions.

  • How systems must evolve to support AI-augmented work. 

  • What skills workers will need to remain relevant. 

Job disruption and workforce shifts

Last November, a 2025 Stanford University study led by Dr. Erik Brynjolfsson using data from millions of ADP payroll records found that AI is already driving labor market shifts. Early-career workers in AI-exposed occupations are experiencing a 16% decline in employment, while employment for experienced workers remains stable so far. To be clear, employment changes are concentrated in occupations where AI automates rather than augments labor. 

Without question, AI will affect tasks, occupations and industries in different ways, replacing work in some, augmenting others, and transforming still others. Professions already affected at the hiring level include software developers and customer service representatives. More experienced workers have not been disrupted at the same rate, despite being less likely to embrace AI to augment their work. Generative AI tools like Claude and Open AI models are already demonstrating gains in personal productivity. 

The question CIOs and their HR partners need to answer is: which business processes and tasks within the enterprise will be automated, augmented, or changed, and -- over time -- what work will look like if agents handle execution. In the longer-term agentic world, humans will be responsible for architecting work, putting together governance structures (guidelines, guardrails, and standards), and managing how agents do their execution. In this phase, Ian Beacraft said in his South by Southwest talk a few weeks ago, we move to agentic organizations. 

Related:7 behaviors of the AI-Savvy CIO

Which work gets automated, augmented, rebuilt 

To navigate the workforce shift, CIOs should task their enterprise architects to take their maps of business capabilities and business processes and determine which will be automated, augmented, or changed. In practice, this makes enterprise architecture the mechanism for redesigning work. In many cases, this should be done using future-state maps that reflect how AI can transform operating models and create new value propositions. 

With these in hand, CIOs, along with their HR partners and AI-skilled architects, should evaluate job skills, determine which can be automated or augmented with AI, and align them to job descriptions. 

CIOs and IT leaders share perspectives on managing AI and the business

Role of enterprise architects 

Enterprise architects can help by relating maps of business capabilities, skills, and job descriptions. To be clear, EAs are in an assist role; they should not own the workforce or job redesign directly. 

Related:CIO role in unlocking strategic value: How to determine and implement AI use cases

Instead, EAs should connect the dots across business capabilities, processes, systems, data, the operating model and governance, helping to inform role and skills changes alongside HR and the business.

This collective effort should result in two things: first, identifying highly automated jobs; second, defining new job classifications that will combine job tasks from partially automated jobs or for roles that will manage agent-driven work and performance. This may be the most important job that enterprise architects ever perform -- a rebuilding of the entire enterprise.

Systems must support AI-augmented work 

With this completed, the next logical question to consider is how systems should be designed to better support augmented jobs. 

For these positions, the question that CIOs, CHROs, EAs and CEOs need to consider is what systems must be able to do to support augmented work -- and where they fall short today.

These are big questions that must be answered collaboratively. Once again, enterprise architects need to take center stage.

12 skills CIOs say workers need to stay relevant 

Lastly, I asked CIOs about the skills that workers should develop to be relevant in an AI-driven future. Their answers were synthesized into 12 skill recommendations.

  1. AI fluency. Understand how AI models work -- how they ingest, process and validate data -- and where their limitations lie.

  2. Human judgment. Apply critical thinking to assess AI outputs, especially when something feels off or incomplete.

  3. Problem-solving. Ability to frame the right questions and use AI to accelerate better, more informed decisions.

  4. Ethical responsibility and AI safety awareness. Understand how AI is used responsibly, with attention to bias, risk, accountability and governance.

  5. Adaptability. Ability to continuously adjust to rapidly evolving tools, workflows and business expectations.

  6. Continuous learning mindset. Commitment to ongoing skill development as AI reshapes roles and required capabilities.

  7. Business acumen. Understand core business goals, processes and value drivers to ensure AI delivers meaningful outcomes.

  8. Process and systems thinking. Ability to reimagine workflows end-to-end -- moving from isolated tasks to integrated, AI-enabled outcomes.

  9. Creativity and innovation. Identify new data sets, use cases and ways AI can unlock value -- not just optimize existing work.

  10. Communication and translation skills. Bridge technical and business worlds by explaining AI concepts in clear, actionable terms.

  11. Cross-functional collaboration. Work effectively across IT, HR and business units as AI becomes embedded in every function.

  12. Outcome orientation. Focus on building systems that deliver predictive insights and measurable business impact.

This is a strong list. Clearly, the degree of fluency and process and systems thinking will be different for IT and business workers. But all workers need to be AI savvy to a degree. 

What CIOs can't afford to get wrong 

This article argues that this is a moment for CIOs to step up and partner deeply across the organization. It also highlights a critical opportunity for enterprise architects to help define the path forward. Delivering on this opportunity will require discipline and strong collaboration. Success will depend on building the right mix of skills. The winners in the AI era won't be defined by technical depth alone, but by their ability to combine human capabilities -- judgment, creativity, ethics -- with AI as a partner to drive business outcomes.

About the Author

Myles Suer

CIO Analyst and Tech Journalist

Myles Suer is a CIO analyst and tech journalist. Recognized by Leadtail as a top CIO influencer, he is the former leader of #CIOChat, a global community connecting CIOs and senior technology leaders. His insights have been featured in publications such as CMSWire, CIO.com, VKTR, and Cutter Business Technology Journal. Suer frequently reviews books on AI, technology, and business strategy from leading publishers, including Harvard Business Review Press, MIT Press, and Columbia University Press. Additionally, he serves as research director at Dresner Advisory Services.