Absolutely. Below is a first research map we can use as input for the white paper. I would treat this as the evidence layer around the IEEE article you uploaded, which focused mainly on competences and learning.

Research map: future of computing in the AI era

The topic can be organized into five pillars:

AI infrastructure
Responsible AI
Software engineering standards
AI regulation
Workforce transformation

Together, they show that the AI era is not just about models. It is about compute, energy, governance, engineering discipline, regulation, and people.

⸻

AI infrastructure

Core finding

AI is pushing computing infrastructure from general-purpose cloud computing toward accelerated, power-dense, specialized, and geographically constrained infrastructure.

The International Energy Agency notes that AI is accelerating the deployment of high-performance accelerated servers, increasing power density in data centers and making accelerator adoption a key driver of future electricity demand.

This matters because AI progress is now limited not only by algorithms, but also by:

GPU/TPU/NPU availability;
memory bandwidth;
interconnect capacity;
data-center power;
cooling;
energy sourcing;
cloud concentration;
edge deployment constraints;
inference cost.

Important trend: AI compute is becoming strategic infrastructure

Large AI models need massive training and inference infrastructure. This is why hyperscalers, chip vendors, sovereign governments, and private capital are investing heavily in AI data centers.

Deloitte reported that eight hyperscalers expected a 44% year-over-year increase in 2025 capital expenditure for AI data centers and computing resources, reaching approximately US$371 billion.

Recent market activity also confirms this direction. Google and Blackstone announced an AI cloud venture targeting large-scale TPU-based compute-as-a-service, with Blackstone initially contributing US$5 billion to develop 500 MW of data-center capacity by 2027.

White-paper insight

The future of computing is moving from software-defined everything to compute-constrained intelligence.

A useful line for your paper:

In the AI era, infrastructure is no longer a background utility. Compute, memory, power, and cooling become strategic constraints that shape what systems can be built, where they can run, and who can afford to operate them.

Gaps to discuss

AI infrastructure raises several open questions:

Will AI compute concentrate power in a few hyperscalers?
Can edge AI reduce latency and privacy risks, or will it create fragmented deployment complexity?
Will energy availability become the new bottleneck for AI adoption?
Can model optimization reduce infrastructure pressure?
Will custom silicon reduce dependency on GPUs?

⸻

Responsible AI

Core finding

Responsible AI is moving from broad ethical language toward operational risk management.

The NIST AI Risk Management Framework was developed to help manage AI risks to individuals, organizations, and society. The practical value of NIST AI RMF is that it gives organizations a structured way to govern, map, measure, and manage AI risks rather than treating ethics as an abstract principle.

Key responsible AI themes

For the white paper, responsible AI should include:

Area Meaning Reliability Does the system behave consistently under expected conditions? Safety Can the system cause harm if it fails or is misused? Security Can it resist attacks, prompt injection, data poisoning, and model abuse? Privacy Does it expose personal, sensitive, or confidential information? Fairness Does it create discriminatory or biased outcomes? Transparency Can users and auditors understand how it is used and governed? Accountability Who owns the decision, the deployment, and the failure? Human oversight Where must humans approve, review, or intervene?

ISO/IEC 42001

ISO/IEC 42001 is important because it moves responsible AI into a management-system model. In practical terms, it helps organizations define policies, roles, controls, monitoring, improvement cycles, and governance practices for AI systems.

A useful white-paper framing:

NIST AI RMF helps organizations think about AI risk. ISO/IEC 42001 helps them institutionalize AI governance.

White-paper insight

Responsible AI is not a legal checkbox or a public-relations layer. It is an engineering and operating discipline.

Suggested wording:

Responsible AI must be embedded into the AI lifecycle: dataset selection, model choice, prompting, evaluation, deployment, monitoring, incident response, and retirement. Treating responsibility as a final review step is too late.

⸻

Software engineering standards

Core finding

The AI era does not make software engineering standards obsolete. It makes them more important.

The IEEE article you uploaded makes the same argument indirectly: AI-generated development without lifecycle discipline can create chaotic, undocumented, difficult-to-maintain systems.

The SWEBOK Guide defines generally accepted software engineering knowledge and is used as a foundation for education, certification, and shared professional understanding. SFIA also maps SWEBOK v4 to a competency model based on SFIA v9, showing the connection between bodies of knowledge and workforce skills.

Key standards and bodies of knowledge to include

Standard / Framework Relevance SWEBOK v4 Software engineering knowledge foundation ISO/IEC/IEEE 12207 Software lifecycle processes ISO/IEC/IEEE 15288 System lifecycle processes ISO/IEC 25010 Software/system quality model ISO/IEC 27001 Information security management ISO/IEC 42001 AI management system NIST AI RMF AI risk management SFIA v9 Skills and competency framework

ISO/IEC/IEEE 12207 provides a common process framework for software lifecycle engineering. This is relevant because AI-generated software still needs requirements, architecture, verification, validation, configuration management, deployment, operation, maintenance, and retirement.

White-paper insight

The central message should be:

AI can accelerate software production, but it cannot remove the need for software engineering discipline.

AI tools may generate code, tests, documentation, and design proposals, but engineering standards remain necessary to control:

requirements traceability;
architecture decisions;
quality attributes;
configuration management;
safety and security evidence;
release governance;
operational support;
maintainability;
auditability.

Important angle

AI-assisted development creates a new standards question:

How do we certify or validate systems where parts of the design, code, tests, documentation, or decision logic were produced by AI?

This is a strong white-paper discussion point.

⸻

AI regulation

Core finding

AI governance is shifting from voluntary principles to enforceable regulation.

The EU AI Act is the most important regulatory reference for your white paper. The European Commission describes it as a risk-based AI regulatory framework. Its phased application has already started: prohibited AI practices and AI literacy obligations applied from 2 February 2025, governance rules and obligations for general-purpose AI models became applicable from 2 August 2025, and certain high-risk AI systems embedded into regulated products have an extended transition period until 2 August 2028.

Why this matters

AI regulation changes engineering practice. It forces organizations to document, classify, control, and monitor AI systems.

For high-risk systems, the practical implications include:

risk management;
data governance;
technical documentation;
logging;
transparency;
human oversight;
accuracy, robustness, and cybersecurity;
post-market monitoring;
incident reporting.

The European Commission has also opened consultation on draft guidelines for classifying high-risk AI systems, which shows that practical interpretation is still evolving.

White-paper insight

Regulation turns AI from an experimental capability into a governed product discipline.

Suggested wording:

The AI era will reward organizations that can convert regulatory requirements into engineering controls. Compliance will not be separate from architecture; it will become part of architecture.

Important warning

For your paper, avoid saying “AI regulation will stop innovation.” A better balanced position:

Regulation may slow careless deployment, but it can also create trust, clarify accountability, and make enterprise adoption easier in regulated sectors.

This is especially relevant for finance, healthcare, telecom, public services, automotive, and critical infrastructure.

⸻

Workforce transformation

Core finding

AI is not only changing jobs. It is changing the structure of work.

The World Economic Forum’s Future of Jobs Report 2025 is based on input from more than 1,000 employers representing over 14 million workers across 22 industries and 55 economies. It examines how technology, the green transition, and other macrotrends will affect jobs and skills between 2025 and 2030.

The OECD notes that AI brings opportunities but also risks, including automation, loss of agency, bias, privacy breaches, and lack of transparency.

Major workforce shifts

The research points toward several changes:

Old work model AI-era work model Static job descriptions Dynamic roles Human-only teams Human-agent teams Task execution Task orchestration Formal training cycles Continuous learning Certificates Demonstrated capability Routine entry-level work More complex entry-level expectations Department silos Cross-functional AI-enabled teams

Microsoft’s 2025 Work Trend Index describes AI skilling and digital labor as top workforce strategies, with leaders exploring human-agent teams. Microsoft also reported that 78% of leaders were considering hiring for new AI roles, while 83% said AI would let employees take on more complex and strategic work earlier in their careers.

Important workforce tension

AI creates a paradox:

It can make workers more productive, but it can also make entry-level learning harder.

If AI removes routine junior tasks, new engineers may lose the traditional path where they learned by doing simpler work first. This is a serious point for your white paper.

Organizations need to redesign learning pathways so junior staff still develop foundations, judgment, debugging ability, and system understanding.

White-paper insight

The future workforce is not simply “AI-skilled.” It is AI-literate, domain-aware, system-oriented, and accountable.

⸻

Cross-cutting synthesis

The five research areas connect strongly.

Pillar Main pressure White-paper implication AI infrastructure Compute, energy, cost, concentration AI capability depends on physical and economic constraints Responsible AI Risk, trust, accountability AI must be governed across its lifecycle SE standards Lifecycle discipline AI-generated software still requires engineering control AI regulation Enforceable obligations Compliance becomes part of architecture Workforce transformation Role redesign Engineers become orchestrators, validators, and accountable decision-makers

The strongest overall thesis is:

The future of computing in the AI era is not defined by AI models alone. It is defined by the interaction between intelligent software, specialized infrastructure, responsible governance, disciplined engineering, regulation, and a workforce capable of supervising increasingly autonomous systems.

⸻

Suggested white paper sections

I would structure the paper like this:

Executive Summary
From Software Systems to Intelligent Systems
AI Infrastructure: Compute Becomes Strategic
Responsible AI: From Ethics to Operational Risk Management
Software Engineering Discipline in the Age of Generated Code
AI Regulation: Compliance as an Architectural Constraint
Workforce Transformation: From Coders to AI Orchestrators
The New Competence Stack
Risks, Blind Spots, and Open Questions
Recommendations for Leaders, Engineers, Universities, and Policymakers
Conclusion: Beyond Code, Beyond Models

⸻

Strong thesis for the white paper

You can use this as the anchor:

The AI era does not replace computing discipline. It raises the level at which discipline must operate. Code generation becomes cheaper, but infrastructure, governance, validation, security, regulation, and human judgment become more valuable. The future of computing belongs to organizations and professionals that can combine accelerated infrastructure, responsible AI governance, strong software engineering practices, regulatory awareness, and continuous workforce learning.

This gives us a serious, balanced foundation: optimistic about AI, but not naïve.