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Governing the future: A strategic framework for federal HR IT modernization

The federal government is preparing to undertake one of the most ambitious IT transformations in decades: Modernizing and unifying human resources information technology across agencies. The technology itself is not the greatest challenge. Instead, success will hinge on the governmentโ€™s ability to establish an effective, authoritative and disciplined governance structure capable of making informed, timely and sometimes difficult decisions.

The central tension is clear: Agencies legitimately need flexibility to execute mission-specific processes, yet the government must reduce fragmentation, redundancy and cost by standardizing and adopting commercial best practices. Historically, each agency has evolved idiosyncratic HR processes โ€” even for identical functions โ€” resulting in one of the most complex HR ecosystems in the world.

We need a governance framework that can break this cycle. It has to be a structured requirements-evaluation process, a systematic approach to modernizing outdated statutory constraints, and a rigorous mechanism to prevent โ€œcorner casesโ€ from derailing modernization. The framework is based on a three-tiered governance structure to enable accountability, enforce standards, manage risk and accelerate decision making.

The governance imperative in HR IT modernization

Modernizing HR IT across the federal government requires rethinking more than just systems โ€” it requires rethinking decision making. Technology will only succeed if governance promotes standardization, manages statutory and regulatory constraints intelligently, and prevents scope creep driven by individual agency preferences.

Absent strong governance, modernization will devolve into a high-cost, multi-point, agency-to-vendor negotiation where each agency advocates for its โ€œuniqueโ€ variations. Commercial vendors, who find arguing with or disappointing their customers to be fruitless and counterproductive, will ultimately optimize toward additional scope, higher complexity and extended timelines โ€” that is, unless the government owns the decision framework.

Why governance is the central challenge

The root causes of this central challenge are structural. Agencies with different missions evolved different HR processes โ€” even for identical tasks such as onboarding, payroll events or personnel actions. Many โ€œrequirementsโ€ cited today are actually legacy practices, outdated rules or agency preferences. And statutes and regulations are often more flexible than assumed, but in order to avoid any risk of perceived noncompliance or litigation.

Without centralized authority, modernization will replicate fragmentation in a new system rather than reduce it. Governance must therefore act as the strategic filter that determines what is truly required, what can be standardized and what needs legislative or policy reform.

A two-dimensional requirements evaluation framework

Regardless of the rigor associated with the requirements outlined at the outset of the program, implementers will encounter seemingly unique or unaccounted for โ€œrequirementsโ€ that appear to be critical to agencies as they begin seriously planning for implementation. Any federal HR modernization effort must implement a consistent, transparent and rigorous method for evaluating these new or additional requirements. The framework should classify every proposed โ€œneedโ€ across two dimensions:

  • Applicability (breadth): Is this need specific to a single agency, a cluster of agencies, or the whole of government?
  • Codification (rigidity): Is the need explicitly required by law/regulation, or is it merely a policy preference or tradition?

This line of thinking leads to a decision matrix of sorts. For instance, identified needs that are found to be universal and well-codified are likely legitimate requirements and solid candidates for productization on the part of the HR IT vendor. For requirements that apply to a group of agencies or a single agency, or that are really based on practice or tradition, there may be a range of outcomes worth considering.

Prior to an engineering discussion, the applicable governance body must ask of any new requirement: Can this objective be achieved by conforming to a recognized commercial best practice? If the answer is yes, the governance process should strongly favor moving in that direction.

This disciplined approach is crucial to keeping modernization aligned with cost savings, simplification and future scalability.

Breaking the statutory chains: A modern exception and reform model

A common pitfall in federal IT is the tendency to view outdated laws and regulations as immutable engineering constraints. There are in fact many government โ€œrequirementsโ€ โ€” often at a very granular and prescriptive level โ€” embedded in written laws and regulations, that are either out-of-date or that simply do not make sense when viewed in a larger context of how HR gets done. The tendency is to look at these cases and say, โ€œThis is in the rule books, so we must build the software this way.โ€

But this is the wrong answer, for several reasons. And reform typically lags years behind technology. Changing laws or regulations is an arduous and lengthy process, but the government cannot afford to encode obsolete statutes into modern software. Treating every rule as a software requirement guarantees technical debt before launch.

The proposed mechanism: The business case exception

The Office of Management and Budget and the Office of Personnel Management have demonstrated the ability to manage simple, business-case-driven exception processes. This capability should be operationalized as a core component of HR IT modernization governance:

  • Immediate flexibility: OMB and OPM should grant agencies waivers to bypass outdated procedural requirements if adopting the standard best practice reduces administrative burden and cost.
  • Batch legislative updates: Rather than waiting for laws to change before modernizing, OPM and OMB can โ€œbatch upโ€ these approved exceptions. On a periodic basis, these proven efficiencies through standard processes to modify laws and regulations to match the new, modernized reality.

This approach flips the traditional model. Instead of software lagging behind policy, the modernization effort drives policy evolution.

Avoiding the โ€œcorner caseโ€ trap: ROI-driven decision-making

In large-scale HR modernization, โ€œcorner casesโ€ can become the silent destroyer of budgets and timelines. Every agency can cite dozens of rare events โ€” special pay authorities, unusual personnel actions or unique workforce segments โ€” that occur only infrequently.

The risk is that building system logic for rare events is extraordinarily expensive. These edge cases disproportionately consume design and engineering time. And any customization or productization can increase testing complexity and long-term maintenance cost.

Governance should enforce a strict return-on-investment rule: If a unique scenario occurs infrequently and costs more to automate than to handle manually, it should not be engineered into the system.

For instance, if a unique process occurs only 50 times a year across a 2-million-person workforce, it is cheaper to handle it manually outside the system than to spend millions customizing the software. If the government does not manage this evaluation itself, it will devolve into a โ€œping-pongโ€ negotiation with vendors, leading to scope creep and vulnerability. The government must hold the reins, deciding what gets built based on value, not just request.

Recommended governance structure

To operationalize the ideas above, the government should implement a three-tiered governance structure designed to separate strategy from technical execution.

  1. The executive steering committee (ESC)
  • Composition: Senior leadership from OMB, OPM and select agency chief human capital officers and chief information officers (CHCOs/CIOs).
  • Role: Defines the โ€œNorth Star.โ€ They hold the authority to approve the โ€œbatch exceptionsโ€ for policy and regulation. They handle the highest-level escalations where an agency claims a mission-critical need to deviate from the standard.

The ESC establishes the foundation for policy, ensures accountability, and provides air cover for standardization decisions that may challenge entrenched agency preferences.

  1. The functional control board (FCB)
  • Composition: Functional experts (HR practitioners) and business analysts.
  • Role: The โ€œgatekeepers.โ€ They utilize the two-dimensional framework to triage requirements. Their primary mandate is to protect the standard commercial best practice. They determine if a request is a true โ€œneedโ€ or just a preference.

The FCB prevents the โ€œpaving cow pathsโ€ phenomenon by rigorously protecting the standard process baseline.

  1. The architecture review board (ARB)
  • Composition: Technical architects and security experts.
  • Role: Ensures that even approved variations do not break the data model or introduce technical debt. They enforce the return on investment (ROI) rule on corner cases โ€” if the technical cost of a request exceeds its business value, they reject it.

The ARB enforces discipline on engineering choices and protects the system from fragmentation.

Federal HR IT modernization presents a rare opportunity to reshape not just systems, but the business of human capital management across government. The technology exists. The challenge โ€” and the opportunity โ€” lies in governance.

The path to modernization will not be defined by the software implemented, but by the discipline, authority, and insight of the governance structure that guides it.

Steve Krauss is a principal with SLK Executive Advisory. He spent the last decade working for GSA and OPM, including as the Senior Executive Service (SES) director of the HR Quality Service Management Office (QSMO).

The post Governing the future: A strategic framework for federal HR IT modernization first appeared on Federal News Network.

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Australiaโ€™s New Aged Care Act Raises Expectations for Workforce Data

16 January 2026 at 02:37

Australiaโ€™s new Aged Care Act raises the bar for workforce visibility. Hereโ€™s why HR data and connected systems are now essential for aged care providers.

The post Australiaโ€™s New Aged Care Act Raises Expectations for Workforce Data appeared first on TechRepublic.

Australiaโ€™s New Aged Care Act Raises Expectations for Workforce Data

16 January 2026 at 02:37

Australiaโ€™s new Aged Care Act raises the bar for workforce visibility. Hereโ€™s why HR data and connected systems are now essential for aged care providers.

The post Australiaโ€™s New Aged Care Act Raises Expectations for Workforce Data appeared first on TechRepublic.

The workforce shift โ€” why CIOs and people leaders must partner harder than ever

15 January 2026 at 07:20

AI wonโ€™t replace people. But leaders who ignore workforce redesign will begin to fail and be replaced by leaders who adapt and quickly.

For the last decade or so, digital transformation has been framed as a technology challenge. New platforms. Cloud migrations. Data lakes. APIs. Automation. Security layered on top. It was complex, often messy and rarely finished โ€” but the underlying assumption stayed the same: Humans remained at the center of work, with technology enabling them.

AI breaks that assumption.

Not because it is magical or sentient โ€” it isnโ€™t โ€” but because it behaves in ways that feel human. It writes, reasons, summarizes, analyzes and decides at speeds that humans simply cannot match. That creates a very different emotional and organizational response to any technology that has come before it.

I was recently at a breakfast session with HR leaders where the topic was simple enough on paper: AI and how to implement it in organizations. In reality, the conversation quickly moved away from tools and vendors and landed squarely on people โ€” fear, confusion, opportunity, resistance and fatigue. That is where the real challenge sits.

AI feels human and that changes everything

AI is just technology. But it feels human because it has been designed to interact with us in human ways. Large language models combined with domain data create the illusion that AI can do anything. Maybe one day it will. Right now, what it can do is expose how unprepared most organizations are for the scale and pace of change it brings.

We are all chasing competitive advantages โ€” revenue growth, margin improvement, improving resilience โ€” and AI is being positioned as the shortcut. But unlike previous waves of automation, this one does not sit neatly inside a single function.

Earlier this year I made what I thought was an obvious statement on a panel: โ€œAI is not your colleague. AI is not your friend. It is just technology.โ€ After the session, someone told me โ€” completely seriously โ€” that AI was their colleague. It was listed on their Teams org chart. It was an agent with tasks allocated to it.

That blurring of boundaries should make leaders pause.

Perception becomes reality very quickly inside organizations. If people believe AI is a colleague, what does that mean for accountability, trust and decision-making? Who owns outcomes when work is split between humans and machines? These are not abstract questions โ€” they show up in performance, morale and risk.

When I spoke to younger employees outside that HR audience, the picture was even more stark. They understood what AI was. They were already using it. But many believed it would reduce the number of jobs available to their generation. Nearly half saw AI as a net negative force. None saw it as purely positive.

That sentiment matters. Because engagement is not driven by strategy decks โ€” it is driven by how people feel about their future.

Roles, skills and org design are already out of date

One of the biggest problems organizations face is that work is changing faster than their structures can keep up.

As Zoe Johnson, HR director at 1st Central, put it: โ€œThe biggest mismatch is in how fast the technology is evolving and how possible it is to redesign systems, processes and people impacts to keep pace with how fast work is changing. We are seeing fast progress in our customer-facing areas, where efficiencies can clearly be made.โ€

Job frameworks, skills models and career paths are struggling to keep up with reality. This mirrors what we are now seeing publicly, with BBC reporting that many large organizations expect HR and IT responsibilities to converge as AI reshapes how work actually flows through the enterprise.

AI does not neatly replace a role โ€” it reshapes tasks across multiple roles simultaneously. That shift is already forcing leadership teams to rethink whether work should be organized by function at all or instead designed endโ€‘toโ€‘end around outcomes. That makes traditional workforce planning dangerously slow.

Organizations are also hitting change saturation. We have spent years telling ourselves that โ€œthe only constant is change,โ€ but AI feels relentless. It lands on top of digital transformation, cloud, cyber, regulation and cost pressure.

Johnson is clear-eyed about this tension: โ€œThis is a constant battle, to keep on top of technology development but also ensure performance is consistent and doesnโ€™t dip. Iโ€™m not sure anyone has all the answers, but focusing change resource on where the biggest impact can be made has been a key focus area for us.โ€

That focus is critical. Because indiscriminate AI adoption does not create advantages โ€” it creates noise.

This is no longer an IT problem

For years, organizations have layered technology on top of broken processes. Sometimes that was a conscious trade-off to move faster. Sometimes it was avoidance. Either way, humans could usually compensate.

AI does not compensate. It amplifies. This is the same dynamic highlighted recently in the Wall Street Journal, where CIOs describe AI agents accelerating both productivity and structural weakness when layered onto poorly designed processes.

Put AI on top of a poor process and you get faster failure. Put it on top of bad data and you scale mistakes at speed. This is not something a CIO can โ€œfixโ€ alone โ€” and it never really was.

The value chain โ€” how people, process, systems and data interact to create outcomes โ€” is the invisible thread most organizations barely understand. AI pulls on that thread hard.

That is why the relationship between CIOs and people leaders has moved from important to existential.

Johnson describes what effective partnership actually looks like in practice: โ€œConstant communication and connection is key. We have an AI governance forum and an AI working group where we regularly discuss how AI interventions are being developed in the business.โ€

That shared ownership matters. Not governance theatre, but real, ongoing collaboration where trade-offs are explicit and consequences understood.

Culture plays a decisive role here. As Johnson notes, โ€œCulture and trust is at the heart of keeping colleagues engaged during technological change. Open and honest communication is key and finding more interesting and value-adding work for colleagues.โ€

AI changes what work is. People leaders are the ones who understand how that lands emotionally.

The CEO view: Speed, restraint and cultural expectations

From the CEO seat, AI is both opportunity and risk. Hayley Roberts, CEO of Distology, is pragmatic about how leadership teams get this wrong.

โ€œAll new tech developments should be seen as an opportunity,โ€ she said. โ€œLeadership is misaligned when the needs of each department are not congruent with the businessโ€™s overall strategy. With AI it has to be bought in by the whole organization, with clear understanding of the benefits and ethical use.โ€

Some teams want to move fast. Others hesitate โ€” because of regulation, fear or lack of confidence. Knowing when to accelerate and when to hold back is a leadership skill.

โ€œWe love new tech at Distology,โ€ Roberts explains, โ€œbut that doesnโ€™t mean it is all going to have a business benefit. We use AI in different teams but it is not yet a business strategy. It will become part of our roadmap, but we are using what makes sense โ€” not what we think we should be using.โ€

That restraint is often missing. AI is not a race to deploy tools โ€” it is a race to build sustainable advantage.

Roberts is also clear that organizations must reset cultural expectations: โ€œBusinesses are still very much people, not machines. Comprehensive internal assessment helps allay fear of job losses and assists in retaining positive culture.โ€

There is no finished AI product. Just constant evolution. And that places a new burden on leadership coherence.

โ€œI trust what we are doing with our AI awareness and strategy,โ€ Roberts says. โ€œThere is no silver bullet. Making rash decisions would be catastrophic. I am excited about what AI might do for us as a growing business over time.โ€

Accountability doesnโ€™t disappear โ€” it concentrates

One uncomfortable truth sits underneath all of this: AI does not remove accountability. It concentrates it. Recent coverage in The HR Director on AIโ€‘driven restructuring, role redesign and burnout reinforces that outcomes are shaped less by the technology itself and more by the leadership choices made around design, data and pace of change.

When decisions are automated or augmented, the responsibility still sits with humans โ€” across the entire C-suite. You cannot outsource judgement to an algorithm and then blame IT when it goes wrong.

This is why workforce redesign is not optional. Skills, org design and leadership behaviors must evolve together. CIOs bring the technical understanding. CPOs and HRDs bring insight into capability, culture and trust. CEOs set the tone and pace.

Ignore that partnership and AI will magnify every weakness you already have.

Get it right and it becomes a powerful force for growth, resilience and better work.

The workforce shift is already underway. The question is whether leaders are redesigning for it โ€” or reacting too late.

This article is published as part of the Foundry Expert Contributor Network.
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โ€œ์—์ด์ „ํ‹ฑ AI ์ „๋žต์˜ ๋น ์ง„ ๊ณ ๋ฆฌโ€ IT์™€ HR์˜ ํ˜‘๋ ฅ

12 January 2026 at 01:00

IT ์—…๊ณ„์˜ ๋ถ„์œ„๊ธฐ๋งŒ ๋ณด๋ฉด, AI ์—์ด์ „ํŠธ๊ฐ€ ์ „์ฒด ๋น„์ฆˆ๋‹ˆ์Šค ํ”„๋กœ์„ธ์Šค๋ฅผ ์ž๋™ํ™”ํ•ด ์ „ ์„ธ๊ณ„ ๊ธฐ์—…์„ ๋ฐ”๊ฟ”๋†“์„ ๊ฒƒ ๊ฐ™๋‹ค. ํ•˜์ง€๋งŒ ํ˜„์‹ค์€ ์ „ํ˜€ ๋‹ค๋ฅด๋‹ค.

์™€ํŠผ ์Šค์ฟจ๊ณผ ์ง€๋น„์ผ€์ด ์ปฌ๋ ‰ํ‹ฐ๋ธŒ(GBK Collective)๊ฐ€ ๋ฐœํ‘œํ•œ AI ๋„์ž… ์กฐ์‚ฌ์— ๋”ฐ๋ฅด๋ฉด, ๊ธฐ์—… IT ์˜์‚ฌ๊ฒฐ์ •๊ถŒ์ž 58%๊ฐ€ ์กฐ์ง ๋‚ด์—์„œ AI ์—์ด์ „ํŠธ๋ฅผ ์‹œ๋ฒ” ์šด์˜ํ•˜๊ณ  ์žˆ๋‹ค๊ณ  ๋‹ตํ–ˆ์œผ๋ฉฐ, ๋Œ€๋‹ค์ˆ˜๋Š” ํ”„๋กœ์„ธ์Šค ์ž๋™ํ™”, ์›Œํฌํ”Œ๋กœ์šฐ ํšจ์œจํ™”, ๊ณ ๊ฐ ์„œ๋น„์Šค ๋“ฑ์˜ ์‚ฌ์šฉ๋ก€์— ์ ์šฉํ•  ๊ณ„ํš์ด๋ผ๊ณ  ๋ฐํ˜”๋‹ค.

ํ•˜์ง€๋งŒ ์–ด๋””๊นŒ์ง€๋‚˜ ํŒŒ์ผ๋Ÿฟ ๋‹จ๊ณ„์ด๋ฉฐ, ํ”„๋กœ๋•์…˜ ํ™˜๊ฒฝ์— ์ ์šฉ๋œ ๊ฒƒ์€ ์•„๋‹ˆ๋‹ค. ์™„์„ฑ๋œ ์ธ๊ฐ„-AI ์—์ด์ „ํŠธ ํ˜‘์—… ์›Œํฌํ”Œ๋กœ์šฐ๋ฅผ ์œ„ํ•œ ๊ต๋ณธ ๊ฐ™์€ ๊ฒƒ์€ ์•„์ง ์—†๋‹ค.

IT ๋ถ€์„œ๊ฐ€ AI๋ฅผ ์‚ฌ์šฉํ•ด ์šด์˜์„ ์ž๋™ํ™”ํ•  ์ตœ์„ ์˜ ๊ธธ์„ ๋‘๊ณ  ๊ณ ๋ฏผํ•˜๋Š” ๊ณผ์ •์—์„œ HR๊ณผ์˜ ๊ธด๋ฐ€ํ•œ ํ˜‘๋ ฅ์ด ํ•„์ˆ˜์ ์ธ ์š”์†Œ๋กœ ๋– ์˜ฌ๋ž๋‹ค. IT์™€ HR์˜ ํ˜‘๋ ฅ์€ ํ˜ผ๋ž€์„ ์ตœ์†Œํ™”ํ•˜๊ณ  ์ธ๊ฐ„๊ณผ AI๊ฐ€ ๋™๋ฃŒ๋กœ์„œ ๊ฐ™์ด ๊ทผ๋ฌดํ•˜๋Š” ํ™˜๊ฒฝ์ด ํ˜„์‹คํ™”๋  ๋•Œ ๋“ฑ์žฅํ•  ์ƒˆ ์—ญํ• , ํ”„๋กœ์„ธ์Šค, ํŒ€ ๊ตฌ์กฐ์— ๊ธฐ์—…์ด ๋น ๋ฅด๊ฒŒ ์ ์‘ํ•  ์ˆ˜ ์žˆ๋„๋ก ์ง€์›ํ•œ๋‹ค.

AI ์—์ด์ „ํŠธ๋ฅผ ์กฐ์ง ์•ˆ์œผ๋กœ ๋“ค์ด๋Š” ๋ฐฉ๋ฒ•

์‚ฌ์ด๋ฒ„ ๋ณด์•ˆ ํšŒ์‚ฌ ์†Œํฌ์Šค(Sophos)์˜ CIO ํ† ๋‹ˆ ์˜์€ ์ฑ…์ž„ ์žˆ๋Š” AI ๋ฐฐํฌ๋ฅผ ์œ„ํ•œ ๋ณ€ํ™”๊ด€๋ฆฌ์—์„œ IT์™€ HR์˜ ๋ฐ€์ฐฉ์ด ์ค‘์š”ํ•˜๋‹ค๊ณ  ๊ฐ•์กฐํ–ˆ๋‹ค. ์˜์€ โ€œ์˜ฌ๋ฐ”๋ฅธ ์ ‘๊ทผ์€ HR ์ „๋ฌธ๊ฐ€๋“ค๊ณผ ํ•จ๊ป˜ํ•˜๊ณ , ์–ด๋–ป๊ฒŒ ๊ตฌ์„ฑ์›์„ ๋ณ€ํ™”์— ๋™์ฐธ์‹œํ‚ฌ์ง€ ์ดํ•ดํ•˜๋Š” ๊ฒƒโ€์ด๋ผ๋ฉฐ HR๊ณผ์˜ ๋™ํ–‰์„ ํ•ต์‹ฌ์œผ๋กœ ์งš์—ˆ๋‹ค.

์˜์ด ๊ทธ๋ฆฌ๋Š” ๋ฏธ๋ž˜ ์กฐ์ง์—๋Š” ์ž๋™ํ™” ์ „๋ฌธ๊ฐ€๋ฟ ์•„๋‹ˆ๋ผ, ์ฝ˜ํ…์ธ ๋ฅผ ํ๋ ˆ์ด์…˜ํ•˜๊ณ  ๋ฐ์ดํ„ฐ๋ฅผ ๋‹ค๋ค„ ์—์ด์ „ํ‹ฑ AI ์ „ํ™˜์„ ๋งค๋„๋Ÿฝ๊ฒŒ ๋งŒ๋“œ๋Š” ์ธ๋ ฅ๋„ ๋Š˜์–ด๋‚œ๋‹ค. HR์€ ์ด๋Ÿฐ โ€˜์ƒˆ๋กœ ์ƒ๊ฒจ๋‚˜๋Š” ์ „๋ฌธ ์ธ๋ ฅโ€™์„ ํŒ€์— ์ž์—ฐ์Šค๋Ÿฝ๊ฒŒ ์„ž์–ด ๋ฐฐ์น˜ํ•˜๋Š” ์—ญํ• ์„ ๋งก๋Š”๋‹ค.

AI ์ „ํ™˜๊ธฐ์—๋Š” ์ง์›์˜ ๋ถˆ์•ˆ์„ ์™„ํ™”ํ•  โ€˜์ž‘์€ ์žฅ์น˜โ€™๋„ ์œ ์šฉํ•˜๋‹ค. ์†Œํฌ์Šค์˜ ๋งˆ์ผ€ํŒ… ๋ถ€์„œ๋Š” AI ์—์ด์ „ํŠธ๋ฅผ ์กฐ์ง๋„์— ํŒ€ ๊ตฌ์„ฑ์›์œผ๋กœ ํฌํ•จํ–ˆ๊ณ , ์ƒˆ ์—์ด์ „ํŠธ๊ฐ€ ํˆฌ์ž…๋˜๋ฉด ์‚ฌ๋žŒ๊ณผ ๋งˆ์ฐฌ๊ฐ€์ง€๋กœ โ€˜์‹ ๊ทœ ํŒ€์› ์†Œ๊ฐœโ€™ ๊ณต์ง€๋ฅผ ๋‚ธ๋‹ค. IT ์„œ๋น„์Šค ๋ฐ์Šคํฌ์—๋Š” ์‚ฌ๋žŒ ์ง์›์ด ๋””์ง€ํ„ธ ๋™๋ฃŒ์™€ ์„ฑ๊ณผ๋ฅผ ๋น„๊ตํ•  ์ˆ˜ ์žˆ๋Š” ๋ฆฌ๋”๋ณด๋“œ๋„ ๋งˆ๋ จํ–ˆ๋‹ค. ๋‹ค๋งŒ ์‚ฌ๋žŒ ์ง์›์ด AI ์—์ด์ „ํŠธ์˜ ๊ฒฐ๊ณผ๋ฅผ ๊ฒ€์ฆํ•˜๊ณ  ๊ฐ์‹œํ•˜๋Š” ๊ตฌ์กฐ๋ฅผ ์œ ์ง€ํ•ด ํœด๋จผ ์ธ ๋” ๋ฃจํ”„(human-in-the-loop) ๋ฒ ์ŠคํŠธ ํ”„๋ž™ํ‹ฐ์Šค์— ๋งž์ถ˜ ์šด์˜์„ ์ด์–ด๊ฐ„๋‹ค๋Š” ์„ค๋ช…์ด๋‹ค.

์—ญ๋Ÿ‰ ๊ธฐ์ค€๋„ ๋‹ฌ๋ผ์ง„๋‹ค. ์˜์€ โ€œLLM์„ ํ™œ์šฉํ•˜๋Š” ๋ฒ•์ด๋‚˜ ์—์ด์ „ํŠธ๋ฅผ ๋งŒ๋“œ๋Š” ๋ฒ•์€ ์—‘์…€์„ ๋‹ค๋ฃจ๋Š” ๊ฒƒ๊ณผ ๋น„์Šทํ•˜๋‹คโ€๋ผ๋ฉฐ, โ€œ๋ชจ๋‘๊ฐ€ ๊ฐ–์ถฐ์•ผ ํ•˜๋Š” ์ƒˆ๋กœ์šด ๊ธฐ๋ณธ ์—ญ๋Ÿ‰์ด ๋๋‹คโ€๋ผ๊ณ  ํ‘œํ˜„ํ–ˆ๋‹ค. ์ด๋ฅผ ์œ„ํ•ด CIO๊ฐ€ HR ๋ฆฌ๋”์™€ ํ•จ๊ป˜ AI ๊ต์œก ์˜์ œ๋ฅผ ์ •ํ•˜๊ณ , ์ƒ์„ฑํ˜• AI ๊ด€๋ จ ์ž๊ฒฉยท๊ต์œก๊ณผ์ •๊นŒ์ง€ ํฌํ•จํ•œ โ€˜์ „์‚ฌ ํ•™์Šต ๋กœ๋“œ๋งตโ€™์„ ๋งˆ๋ จํ•ด์•ผ ํ•œ๋‹ค๋Š” ์ฃผ์žฅ์ด๋‹ค.

์ˆ˜๋ฐฑ ์ˆ˜์ฒœ์˜ ๋ด‡์ด ์ผํ•˜๋Š” ํšŒ์‚ฌ์˜ ์šด์˜๋ชจ๋ธ

์—์ด์ „ํ‹ฑ AI ํ™˜๊ฒฝ์„ ์™„์„ฑํ•œ ๊ธฐ์—…์€ ์–ด๋–ค ๋ชจ์Šต์ผ๊นŒ. ์ˆ˜๋ฐฑ ์ˆ˜์ฒœ์˜ ์ž์œจ โ€˜๋ด‡โ€™์ด ์—”๋“œ ํˆฌ ์—”๋“œ ์—…๋ฌด ํ”„๋กœ์„ธ์Šค๋ฅผ ์ˆ˜ํ–‰ํ•˜๊ณ , ์ด๋“ค ๋ด‡์„ ๊ด€๋ฆฌํ•˜๋ฉฐ ์ž‘์—… ์ดํƒˆ์„ ๋ง‰๋Š” โ€˜๋ณด์Šค ๋ด‡โ€™์ด ์ƒ๋‹จ์— ์žˆ๋Š” ๊ตฌ์กฐ๊ฐ€ ๊ฑฐ๋ก ๋œ๋‹ค. ์ธ๊ฐ„ ์กฐ์ง์ด ์ง€์‹ ๋…ธ๋™์„ ํ•ด์˜จ ๋ฐฉ์‹๊ณผ ๋Œ€์นญ์ ์ด์ง€๋งŒ, ์‹ค์ œ๋กœ๋Š” ์—์ด์ „ํŠธ์™€ ํ•จ๊ป˜ ์ผํ•˜๊ธฐ ์œ„ํ•œ ์ƒˆ๋กœ์šด ์šด์˜๋ชจ๋ธ์ด ํ•„์š”ํ•˜๋‹ค๋Š” ๊ฒƒ์ด ํ•ต์‹ฌ์ด๋‹ค.

์•ž์œผ๋กœ IT ๋ถ€์„œ๋Š” ์—์ด์ „ํŠธ์˜ ์˜์‚ฌ๊ฒฐ์ • ์ฒด๊ณ„์™€ ๊ทธ์— ๋”ฐ๋ฅธ ์›Œํฌํ”Œ๋กœ์šฐ๋ฅผ ์„ค๊ณ„ยท๋ฐฐํฌยท์šด์˜ํ•˜๋Š” ์ฑ…์ž„์ด ์ปค์งˆ ๊ฒƒ์ด๋‹ค. ์›Œํฌํ”Œ๋กœ์šฐ๋Š” ๋ถ€์„œ๋งˆ๋‹ค ๋‹ค๋ฅด๊ฒŒ ์„ค๊ณ„๋ผ์•ผ ํ•œ๋‹ค. ์˜ˆ๋ฅผ ๋“ค์–ด ์ฝœ์„ผํ„ฐ ์—…๋ฌด๋ฅผ AI๋กœ ์ž๋™ํ™”ํ•˜๋ ค๋ฉด, ์‚ฌ๋žŒ์ด ์—์ด์ „ํŠธ๋ฅผ ๋ชจ๋‹ˆํ„ฐ๋งํ•˜๋Š” ๋ฒ•์„ ๋ฐฐ์›Œ์•ผ ํ•œ๋‹ค. ์ด๋Š” ๊ธฐ์กด ์ƒ๋‹ด ์ธ๋ ฅ์˜ ์—…๋ฌด ๋„๊ตฌ๋ฅผ ๋„˜์–ด์„œ๋Š” ๊ด€๋ฆฌยท๊ธฐ์ˆ  ์—ญ๋Ÿ‰์„ ์š”๊ตฌํ•œ๋‹ค.

๊ธฐ์—… ๋งฅํ‚จ์ง€์˜ ์ˆ˜์„ ํŒŒํŠธ๋„ˆ ํด๋ ˆ๋ฉ˜์Šค ์•ผ๋ฅดํƒ€๋ฅด๋Š” โ€œํ†ตํ™” ์˜๋„๋ฅผ ์ดํ•ดํ•˜๊ณ  ๊ฒฝ๊ณ„๋ฅผ ์„ค์ •ํ•˜๋Š” ๋“ฑ ์ƒˆ๋กœ์šด ์—ญ๋Ÿ‰์ด ํ•„์š”ํ•˜๋‹คโ€๋ผ๊ณ  ์„ค๋ช…ํ–ˆ๋‹ค. ๊ธฐ์กด์˜ โ€˜์‚ฌ๋žŒ ์ค‘์‹ฌโ€™ ์šด์˜ ๋ฐฉ์‹์— ์ต์ˆ™ํ•œ ์กฐ์ง์ผ์ˆ˜๋ก ํ”„๋กœ์„ธ์Šค ๊ด€๋ฆฌ ์—ญ๋Ÿ‰์„ ์ƒˆ๋กœ ๊ธธ๋Ÿฌ์•ผ ํ•œ๋‹ค๋Š” ์ง€์ ์ด๋‹ค.

์„ธ์ผ์ฆˆยท๋งˆ์ผ€ํŒ…์— AI ์—์ด์ „ํŠธ๋ฅผ ์ ‘๋ชฉํ•  ๋•Œ๋Š” CRM ๋“ฑ ๊ณ ๊ฐ ์ ‘์  ์‹œ์Šคํ…œ ์ „๋ฐ˜์˜ ์›Œํฌํ”Œ๋กœ์šฐ ์žฌ์„ค๊ณ„๊ฐ€ ๊ณผ์ œ๋กœ ๋– ์˜ค๋ฅธ๋‹ค. ์šด์˜ ์กฐ์ง ๋“ฑ ๋‹ค๋ฅธ ๊ธฐ๋Šฅ๋„ ๋งˆ์ฐฌ๊ฐ€์ง€๋‹ค. ์–ด๋–ค ์—…๋ฌด ํ๋ฆ„์ด๋“  HR์€ ๋ช…ํ™•ํ•˜๊ณ  ์ผ๊ด€๋œ ์ปค๋ฎค๋‹ˆ์ผ€์ด์…˜์œผ๋กœ ์ง์›๋“ค์ด ๋ฐ›์„ ์ถฉ๊ฒฉ์„ ์ค„์ด๊ณ , IT์™€ ๊ฐ ๋ถ€์„œ๊ฐ€ ์žฌ๊ต์œกยท๋ฆฌ์Šคํ‚ฌ๋ง ์ „๋žต์„ ์‹คํ–‰ํ•˜๋„๋ก ๋•๋Š” ์—ญํ• ์„ ๋งก์„ ์ˆ˜ ์žˆ๋‹ค.

๋งˆ์ดํฌ๋กœ์†Œํ”„ํŠธ๋Š” IT์™€ HR์ด ์ธ๊ฐ„ยท๋””์ง€ํ„ธ ์ธ๋ ฅ์„ ํ•จ๊ป˜ ์กฐ์œจํ•˜๋Š” CRO(Chief Resource Officer)โ€™ ๊ฐ™์€ ์ƒˆ ์—ญํ• ์„ ๋งŒ๋“ค ์ˆ˜ ์žˆ๋‹ค๊ณ  ์ „๋งํ–ˆ๊ณ , ์ผ๋ถ€ ์กฐ์ง์—๋Š” โ€˜์—์ด์ „ํŠธ ๋ณด์Šค(agent boss)โ€™๊ฐ€ ๋“ฑ์žฅํ•  ๊ฐ€๋Šฅ์„ฑ๋„ ์ œ๊ธฐํ–ˆ๋‹ค. ๋งฅํ‚จ์ง€๋Š” AI ์œค๋ฆฌยท์ฑ…์ž„ ์‚ฌ์šฉ, AI ํ’ˆ์งˆ ๋ณด์ฆ ์ฑ…์ž„, ์—์ด์ „ํŠธ ์ฝ”์น˜ ๋“ฑ ์‹ ๊ทœ ์ง๋ฌด๊ฐ€ ์ƒ๊ฒจ๋‚  ๊ฒƒ์œผ๋กœ ๋‚ด๋‹ค๋ดค๋‹ค.

๋ฐ์ดํ„ฐ ์‹ ๋ขฐ์„ฑ์€ ์ž์œจ์„ฑ์˜ ์ƒ๋ช…์ค„

๊ณผ์ œ๊ฐ€ ์–ด๋ ต์ง€๋งŒ, ๋„˜์ง€ ๋ชปํ•  ์ˆ˜์ค€์€ ์•„๋‹ˆ๋ผ๋Š” ํ‰๊ฐ€๋„ ๋‚˜์˜จ๋‹ค. ๋‹ค๋งŒ ์—์ด์ „ํ‹ฑ AI ์•„ํ‚คํ…์ฒ˜์— ์ง€๋‚˜์นœ ์˜์‚ฌ๊ฒฐ์ • ๊ถŒํ•œ์„ ๋ถ€์—ฌํ•˜๋Š” ๊ฒƒ์€ ์œ„ํ—˜ํ•˜๋‹ค๋Š” ์ง€์ ์ด ์ ์ง€ ์•Š๋‹ค.

ํ•€์˜ต์Šค ํ”Œ๋žซํผ ์„œ๋น„์Šค ์—…์ฒด ๋‘์ž‡(DoiT)์˜ ํ•„๋“œ CTO ์•„๋ฏธํŠธ ํ‚จํ•˜๋Š” ์„œ๋กœ ๋‹ค๋ฅธ ํ”Œ๋žซํผ ์ „๋ฐ˜์— ๊ฑธ์นœ ๊ธฐ์ˆ ์  ๋‚œ์ œ์™€ โ€˜์•”๋ฌต์ง€(tribal knowledge)โ€™์˜ ๊ณต๋ฐฑ์„ ๋ฆฌ์Šคํฌ๋กœ ์ง€๋ชฉํ–ˆ๋‹ค. ์˜ˆ๋ฅผ ๋“ค์–ด, ์ €์—ฐ์ฐจ ๊ฐœ๋ฐœ์ž์—๊ฒŒ ์—…๋ฌด๋ฅผ ์ฃผ๋ฉด ํ•„์š”ํ•  ๋•Œ ์„ ์ž„์—๊ฒŒ ๋„์›€์„ ๊ตฌํ•  ์ˆ˜ ์žˆ์ง€๋งŒ, ํ˜„์žฌ AI ์—์ด์ „ํŠธ๋Š” ๊ทธ๋Ÿฐ ์ง€์‹ ์ ‘๊ทผ ๋ฉ”์ปค๋‹ˆ์ฆ˜์ด ๋ถ€์กฑํ•˜๋‹ค๋Š” ์ง€์ ์ด๋‹ค. ํ‚จํ•˜๋Š” โ€œ์ง„์‹ค์˜ ์ถœ์ฒ˜(source of truth)๊ฐ€ ์–ด๋””์ธ์ง€๊ฐ€ ์ค‘์š”ํ•˜๋‹คโ€๋ฉฐ โ€œ๊ทธ ๊ธฐ๋ฐ˜์ด ์œ ํšจํ•˜์ง€ ์•Š์œผ๋ฉด ์˜์‚ฌ๊ฒฐ์ • ํŠธ๋ฆฌ ์ „์ฒด๊ฐ€ ๋ฌดํšจ๊ฐ€ ๋œ๋‹คโ€๋ผ๊ณ  ์šฐ๋ ค๋ฅผ ๋“œ๋Ÿฌ๋ƒˆ๋‹ค.

์—์ด์ „ํŠธ ์‹คํ–‰์˜ ํŒŒ๊ธ‰๋ ฅ๋„ ํฌ๋‹ค. ์˜ˆ์ปจ๋Œ€ 15๊ฐœ ์‹œ์Šคํ…œ์— ๊ฑธ์ณ ์—…๋ฐ์ดํŠธํ•  ์ˆ˜ ์žˆ๋Š” ๋ฉ€ํ‹ฐ ์—์ด์ „ํŠธ ์‹œ์Šคํ…œ์€ ํ•˜์œ„ ๋‹จ๊ณ„์— ์—ฐ์‡„์ ์ธ ์˜ํ–ฅ์„ ์ฃผ๋ฉฐ ์ˆ˜์ต์„ฑ์— ์‹ค์งˆ์  ํƒ€๊ฒฉ์„ ์ž…ํž ์ˆ˜ ์žˆ๋‹ค. ์ด๋Ÿฐ ๋ฆฌ์Šคํฌ๋ฅผ ์ค„์ด๋ ค๋ฉด ๊ฑฐ๋ฒ„๋„Œ์Šค ์ฐจ์›์—์„œ โ€˜์ฒดํฌํฌ์ธํŠธโ€™๋ฅผ ๋‘๋Š” ๋ฐฉ์‹์ด ๋Œ€์•ˆ์œผ๋กœ ๊ผฝํžŒ๋‹ค. ์–ด๋–ค ์—์ด์ „ํŠธ๋Š” ๋‹จ๋… ๊ฒฐ์ •์„ ํ—ˆ์šฉํ•˜๋˜, ๋‹ค๋ฅธ ์—์ด์ „ํŠธ๋Š” ์‚ฌ๋žŒ ์Šน์ธ ์—†์ด๋Š” ์‹คํ–‰ํ•˜์ง€ ๋ชปํ•˜๊ฒŒ ํ•˜๋Š” ์‹์ด๋‹ค.

ํ‚จํ•˜๋Š” โ€œ์™„์„ฑํ•˜๊ธฐ ๊ฐ€์žฅ ์–ด๋ ค์šด ๋ถ€๋ถ„์ด ์˜์‚ฌ๊ฒฐ์ • ์ž์œจ์„ฑโ€์ด๋ผ๋ฉฐ, ์ž์œจ์„ฑ์ด ๋„ˆ๋ฌด ๋‚ฎ์œผ๋ฉด ์‚ฌ๋žŒ์—๊ฒŒ ๋งค๋ฒˆ ํ™•์ธํ•˜๋А๋ผ ์ž๋™ํ™”๊ฐ€ ๋ฉˆ์ถ”๊ณ , ๋„ˆ๋ฌด ๋†’์œผ๋ฉด ์น˜๋ช…์  ์‹ค์ˆ˜๊ฐ€ ๋ฐœ์ƒํ•  ์ˆ˜ ์žˆ๋‹ค๊ณ  ์„ค๋ช…ํ–ˆ๋‹ค. ๋˜, ๋ชฉํ‘œ์™€ ์˜๋„๋ฅผ ๋ช…ํ™•ํžˆ ํ•˜๊ณ , ๋ฐ์ดํ„ฐ ์œ„์ƒ์„ ํ™•๊ณ ํžˆ ํ•ด์•ผ ํ•œ๋‹ค๊ณ  ๋ง๋ถ™์˜€๋‹ค.

๋ฐ์ง€๋งŒ ์˜ˆ์ธกํ•  ์ˆ˜ ์—†๋Š” ๋ฏธ๋ž˜

๊ธฐ์ˆ ๊ณผ ํ”„๋กœ์„ธ์Šค์˜ ํผ์ฆ์ด ๋งž์ถฐ์ง€๊ธฐ ์‹œ์ž‘ํ•˜๋ฉด, ์‚ฌ๋žŒ์—์„œ โ€˜์‚ฌ๋žŒ+๊ธฐ๊ณ„โ€™๋กœ ์ผํ•˜๋Š” ๋ฐฉ์‹์œผ๋กœ ๋„˜์–ด๊ฐ€๋Š” ์ „ํ™˜์„ ๋•๋Š” ์ถ•์€ ๊ฒฐ๊ตญ IT์™€ HR์˜ ํŒŒํŠธ๋„ˆ์‹ญ์ด ๋  ๊ฐ€๋Šฅ์„ฑ์ด ํฌ๋‹ค. AI ์—์ด์ „ํŠธ๋ฅผ ๋„์ž…ํ•˜๋Š” ๋ชจ๋“  ๊ธฐ์—…์€ ํ”„๋กœ์„ธ์Šค ์‹คํ–‰ ๋ฐฉ์‹๊ณผ ์„ฑ๊ณผ ์ธก์ • ์ฒด๊ณ„๋ฅผ ๋” ์˜๋„์ ์œผ๋กœ ์„ค๊ณ„ํ•ด์•ผ ํ•œ๋‹ค.

์•ผ๋ฅดํƒ€๋ฅด๋Š” โ€œ๊ฐ ๊ธฐ๋Šฅ ์˜์—ญ์˜ ๊ตฌ์„ฑ์› ๋ชจ๋‘๊ฐ€ ์˜๋„ ์„ค์ •, ๊ฒฝ๊ณ„ ์„ค์ •, ์ธก์ • ์—ญ๋Ÿ‰์„ ๋Œ์–ด์˜ฌ๋ ค์•ผ ํ•œ๋‹คโ€๋ผ๋ฉฐ โ€œ๊ทธ ๊ณผ์ •์—๋Š” ์—ฌ๋Ÿฌ ํ•ด๊ฐ€ ๊ฑธ๋ฆด ๊ฒƒโ€์ด๋ผ๊ณ  ๋‚ด๋‹ค๋ดค๋‹ค. ์˜๋„ โ€œ๊ธฐ์—…๋งˆ๋‹ค ์†๋„๊ฐ€ ๋‹ค๋ฅผ ๊ฒƒโ€์ด๋ผ๋ฉฐ, ์ž๋™ํ™”๋ฅผ ๊ฐ•ํ•˜๊ฒŒ ๋ฐ€์–ด๋ถ™์ด๋Š” ์กฐ์ง๊ณผ ๊ทธ๋ ‡์ง€ ์•Š์€ ์กฐ์ง ์‚ฌ์ด์— ์ƒˆ๋กœ์šด โ€˜๊ฒฉ์ฐจโ€™๊ฐ€ ์ƒ๊ธธ ์ˆ˜ ์žˆ๋‹ค๊ณ  ๋ง๋ถ™์˜€๋‹ค.

ํ•œ ๊ฐ€์ง€, ์ง์žฅ์—์„œ ์ธ๊ฐ„๊ณผ ๊ธฐ๊ณ„๊ฐ€ ํ•จ๊ป˜ ์ผํ•˜๋Š” ํ™˜๊ฒฝ๊ณผ ๊ด€๋ จ๋œ ๋„์ „์€ ์ด์ œ ๋ง‰ ์‹œ์ž‘๋๋‹ค๋Š” ์ ์€ ๋ถ„๋ช…ํ•˜๋‹ค.
dl-ciokorea@foundryco.com

Operational Readiness and Resiliency Index: A new model to assess talent, performance

You just left a high-level meeting with agency leadership. You and your colleagues have been informed that Congress passed new legislation, and your agency is expected to implement the new law with your existing budget and staff. The lead program office replied, โ€œWe can make this work.โ€ The agency head is pleased to hear this, but has reservations. How?

Another situation: The president just announced a new priority and has assigned it to your agency. Again, there is no new funding for the effort. Your agency head assigns the priority to your program with the expectation for success. How do you proceed?

Today, given the recent reductions in force (RIFs), people voluntarily leaving government, and structural reorganizations that have taken place and will likely continue, answering the question โ€œHow to proceed?โ€ is even more difficult. There is a real need to โ€œknowโ€ with a level of certainty whether there are sufficient resources to effectively deliver and sustain new programs or in some cases even the larger agency mission.

Members of the Management Advisory Group โ€” a voluntary group of former appointees under Presidents George W. Bush and Donald Trump โ€” and I believe the answer to these and other questions around an organizationโ€™s capabilities and capacity to perform can be found by employing the Operational Readiness and Resiliency Index (ORRI). ORRI is a domestic equivalent of the military readiness model. It is structured into four categories:

  • Workforce
  • Performance
  • Culture
  • Health

Composed of approximately 50 data elements and populated by existing systems of record, including payroll, learning management systems, finance, budget and programmatic/functional work systems, ORRI links capabilities/capacity with performance, informed by culture and health to provide agency heads and executives with an objective assessment of their organizationโ€™s current and future performance.

In the past, dynamic budgeting and incrementalism meant that risk was low and performance at some levels predictable. We have all managed some increases or cuts to budgets. Those days are gone. Government is changing now at a speed and degree of transformation that has not been witnessed before. Relying on traditional budgeting methods and employee surveys cannot provide insights needed to assess whether current resources provide the capabilities or capacity for future performance of an agency โ€” at any level.

So how does it work?

As is evident with the illustration above, ORRI pulls mainly from existing systems of record. Many of these systems are outside of traditional human resources (HR) departments to include budget, finance and work-systems. Traditionally, HR departments relied on personnel data alone. These systems told you what staff were paid to do, not what they could do. It is focused on classification and pay, not skills, capacity or performance.

Over the years, we have made many efforts to better measure performance. The Government Performance and Results Act (GPRA) as amended, the Performance Assessment Rating Tool (PART), category management and other efforts have attempted to better account for performance. These tools โ€” along with improvements in budgeting to include zero-based budgeting, planning, programming and budgeting systems, and enterprise risk management โ€” have continued to advance our thinking along systems lines. These past efforts, however, failed to produce an integrated model that runs in near real-time or sets objective performance targets using best-in-class benchmarks. Linking capabilities/capacity to performance provides the ability to ask new questions and conduct comparative performance assessments. ORRI can answer such questions as:

  • Are our staffing plans ready for the next mission priority? Can we adapt? Are we resilient?
  • Do we have the right numbers with the right skills assigned to our top priorities?
  • Are we over-staffed in uncritical areas?
  • Given related functions, where are the performance outliers โ€” good and bad?
  • Given our skill shortages, where do I have those skills that are at the right level available now? Should we recruit, train or reassign to make sure we have the right skills? What is in the best interest of the agency/taxpayer?
  • Is our performance comparable โ€” in named activity, to the best โ€” regardless of sector?
  • What does our data/evidence tell us about our culture? Do we represent excellence in whatever we do? Compared to whom?
  • Where are we excelling and why?
  • Where can we invest to demonstrate impact faster?

Focusing on workforce and performance are critical. However, if you believe that culture eats strategy every time, workforce and performance needs to be informed by culture. Hence ORRI includes culture as a category. Culture in this model concentrates on having a team of executives that drive and sustain the culture, evidenced by cycles of learning, change management success and employee engagement. Health is also a key driver for sustained higher performance. In this regard, ORRI tracks both positive and negative indicators of health, as is evident in the illustration. Again, targets are set and measured to drive performance and increase organizational health. Targets are set by industry best in class standards and strategic performance targets necessary for mission achievement.

Governmentwide, ORRI can provide the Office of Management and Budget with real-time comparative performance around key legislative and presidential priorities and cross-agency thematic initiatives. For the Office of Personnel Management, it can provide strategic intelligence on talent, such as enterprise risk management based on an objective assessment: data driven, on critical skills, numbers, competitive environment and performance.

ORRI represents the first phase of an expanded notion of talent assessment. It concentrates on human talent: federal employees.

Phase two of this model will expand the notion of operating capabilities to include AI agents and robotics. As the AI revolution gains speed and acceptance, we can see that agencies will move toward increased use of these tools to increase productivity and reduce transactional cost of government services. Government customer service and adjudication processes will be assigned to AI agents. Like Amazon, more and more warehouse functions will be assigned to physical robots. Talent will need to include machine capabilities, and the total capabilities/capacity reflect the new performance curve โ€” optimizing talent from various sources. This new reality will require a reset in the way government plans, budgets, deploys talent, and assesses overall performance. Phase three will encompass the governmentโ€™s formalized external supply chains which represent the non-governmental delivery systems โ€” essentially government by other means. For example, the rise of public/private partnerships is fundamentally changing the nature of federated government; think of NASA and its dependence on Space X, Boeing, Lockheed Martin and others. ORRI will need to expand to accurately capture these alternative delivery systems to overall government performance. As the role of the federal government continues to evolve, so too do our models for planning, managing talent and assessing performance. ORRI provides that framework.

John Mullins served on the Trump 45 Transition Team and later as the senior advisor to the director at OPM. Most recently Mullins served as strategy and business development executive for IBM supporting NASA, the General Services Administration and OPM.

Mark Formanย was the first administrator for E-Government and Information Technology (Federal CIO). He most recently served as chief strategy officer at Amida Technology Solutions.

The post Operational Readiness and Resiliency Index: A new model to assess talent, performance first appeared on Federal News Network.

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Businessman hold circle of network structure HR - Human resources. Business leadership concept. Management and recruitment. Social network. Different people.

Your agentic AI strategyโ€™s missing link: Human resources

9 January 2026 at 05:01

Tech industry sentiment suggests that AI agents will automate entire business processes, potentially transforming companies worldwide.

Todayโ€™s reality is starkly different.

Fifty-eight percent of enterprise IT decision-makers say their organizations are piloting AI agents, with the majority targeting process automation, workflow efficiencies, or customer service, among other use cases, according to AI adoption research published by Wharton and the GBK Collective.

Again, these are pilots โ€” not production implementations. There isnโ€™t yet a playbook for fully baked human-AI agent workflows.

Still, as IT departments wrestle with the best path forward for using AI to automate operations, close partnership with human resources departments will be essential to minimize disruption and ensure the organization is primed to capitalize on the new roles, processes, and team structures that will arise as true human-AI coworking arrives.

Bringing AI agents into the fold

Tight interaction between IT and HR is crucial for the change management required for responsible AI deployment, says Sophos CIO Tony Young, who is spearheading the deployment of AI at the MDR vendor, including Microsoft Copilot. โ€œThe right approach is engaging with your HR pros and understanding how we bring the workforce along,โ€ Young says.

For example, Young envisions more companies will employ automation experts, along with those who understand how to curate content and work with data to smooth the transition to agentic AI. HR can help blend the budding array of specialists.

Moreover, a little anthropomorphization can go a long way toward easing the transition to digital colleagues, Young adds.

The marketing organization at Sophos now includes AI agents in org charts as part of its teams, working alongside humans. New agents get new team member announcements โ€” just like humans, says Young.

And Sophosโ€™ IT service desk function now features a leaderboard that allows humans to see how they stack up against their digital coworkers. Human staffers monitor the AI agents to validate their work, consistent with human-in-the-loop best practices.

โ€œUnderstanding how to use an LLM, or how to create an agent is like mastering Excel,โ€ Young says. โ€œThatโ€™s a new baseline skill that we all need to have.โ€

To get there, CIOs need to partner with HR leaders to help set the workforce AI training agenda, which could include emerging gen AI certifications as well as coursework for driving AI change.

What the agent-infused organization of the future will look like

What will fully agentic businesses look like in the future? Picture hundreds or thousands of autonomous โ€œbotsโ€ working together to facilitate the execution of business processes end-to-end. These worker bots will likely be managed by a โ€œbossโ€ bot that ensures they stay on task.

If this sounds familiar itโ€™s because itโ€™s a symmetrical analogy for how humans have long performed knowledge work.

Yet organizations require a new operating model for working with agents. It will be incumbent on IT departments to stage and manage agent decision trees and the resulting workflows. These workflows will vary by function.

For instance, organizations that choose to automate call center operations with AI will need to train humans to monitor agents โ€” a managerial and technical skill that goes beyond most call center associatesโ€™ current toolboxes.

โ€œIt requires a new skillset, including understanding the intent of calls and setting boundaries,โ€ says Klemens Hjartar, senior partner at McKinsey. This requires new process management muscles for organizations accustomed to working a certain, human-centric way.

The introduction of AI agents to sales and marketing processes presents different challenges involving various workflows for CRM and other systems of engagement. The same can be said for operations teams and other functions likely to be impacted by agentic AI.

Whatever the workflow, HR can help soften the impact on teams through clear, consistent communication, as well as messaging around how IT and other departments can reskill their teams for the new era.

Microsoft predicted that IT and HR teams will forge new roles such as chief resource officers to help balance human and digital workers, while some organizations may install โ€œagent bosses.โ€ McKinsey envisions new roles for AI ethics and responsible usage, AI quality assurance leads, and agent coaches.

The hurdles are huge but not insurmountable

In short, wholesale changes to organizational dynamics are on the horizon, with IT and HR serving on the front lines of these transformations โ€” mostly in tandem.

While these changes are a ways away, most organizations arenโ€™t ready for it โ€” but need to keep this future in mind as they plan their way forward.

One challenge is the fact that allocating too much decision-making authority to agentic AI architectures poses significant risks, due to technical challenges across disparate platforms and implicit knowledge gaps, says Amit Kinha, field CTO of FinOps platform provider DoiT.

For example, if you give a junior programmer some tasks to accomplish, they can turn to more experienced engineers when they need help. Today there isnโ€™t a mechanism for AI agents to access the same tribal knowledge, Kinha says.

โ€œWhere is the source of truth coming from?โ€ Kinha wonders. โ€œBecause if itโ€™s not valid the whole decision tree will be invalid as well.โ€ย 

The ramifications of agentic actions loom large. A multi-agent system with the power to update across 15 systems could have significant impacts downstream that materially impact the bottom line, Kinha says.

One approach may include instituting checkpoints as part of organizational governance strategies. For instance, while some AI agents may be authorized to make individual decisions, others may have to seek approval from a human.

โ€œThe hardest part to master is decision autonomy,โ€ Kinha says. Agents with too little autonomy will regularly check with humans, stunting automation. Those with too much will make mistakes that could be catastrophic. In addition to being explicit with goals and intents, organizations must make sure their data hygiene is sound, Kinha says.

The future looks bright(ish) โ€” but unpredictable

When the technical and process challenges are reconciled, HR and IT partnership will be essential in assisting the transition from humans to human-plus-machine work. Every company introducing AI agents to their organizations must become more intentional about how they execute their business processes and measure outcomes.

โ€œAll of us in different functional domains need to up our game in intent-setting, boundary-setting, and measurement,โ€ Hjartar says. โ€œThatโ€™s going to take many years for us.โ€

Young says that every company will proceed at their own pace, which will create new categories of haves and have nots โ€” just like preceding paradigm shifts involving emerging technology. โ€œSome will push hard to automate; others wonโ€™t.โ€

Whatโ€™s clear is that the challenges of human-machine commingling in the workplace are just beginning.

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