Author: Justin Lambert

The $100K cost embedded in a broken physician onboarding process 

Posted on January 13, 2026 by - Employee Experience (EX)

Every day a physician is not seeing patients creates about $8,000 in unrealized revenue. Traditional 20-week onboarding cycles delay roughly $100,000+ in revenue per provider before first patient appointments begin. For systems onboarding multiple physicians annually, those delays translate into millions in unrealized revenue. The impact extends to patient access, staff burnout, and recruiting outcomes. 

Healthcare HR teams operate across 30+ disconnected tools, managing manual credentialing processes, fragmented communications, and limited end-to-end status visibility. Physicians face redundant information requests, unclear timelines, and inconsistent requests with unclear ownership from the start. 

A clear separation exists between health systems that modernize onboarding and those that run it manually. Some organizations reach 8- to 12-week onboarding cycles by connecting workflows and shared intake data. Others run 20+ week cycles built on manual processes and providers left waiting on unclear steps and handoffs. 

The hidden costs of a slow physician onboarding process 

Revenue delay gets attention. Operational friction compounds across teams. 

Compliance risk increases when credentialing documentation sits across disconnected tools and inboxes. One missed license verification creates audit risk across every facility and department that touches the record. 

Physician dissatisfaction can start before the first day on site. Candidates comparing offers evaluate onboarding as evidence of how the organization runs day to day. Poor first impressions weaken recruiting credibility and slow future acceptance decisions. 

Operations capacity gets consumed in the background. Administrative burden can exceed 40 hours per hire for capacity-constrained operations teams. Credentialing specialists track down missing documents and confirmations. IT teams work through urgent, last-minute provisioning requests. Practice managers answer recurring status questions. 

Delayed starts extend patient wait times and shift additional load to current clinicians. Every week a physician remains on an unfinished onboarding path, current clinicians absorb additional patient volume, increasing burnout risk and turnover pressure. 

Recruiting pressure rises as faster-moving health systems capture top talent. Your process adds avoidable handoffs and waiting time. 

FAQs: Hidden costs and risk 

Why does the physician onboarding process take so long? 

Physician onboarding often slows due to manual credentialing, disconnected systems, unclear ownership, and sequential handoffs across HR, IT, and clinical teams. Each delay compounds when work waits for approvals, documents move through email, and progress remains hard to track. 

How does slow physician onboarding affect patient care? 

Delays in the physician onboarding process extend patient wait times and increase demand on existing clinicians. When new physicians start late, current providers absorb additional workload, which strains capacity and raises burnout risk across care teams. 

The four structural changes that improve the physician onboarding process 

Healthcare organizations that reach 8- to 12-week onboarding cycles while strengthening compliance controls and provider experience tend to make four structural changes. These shifts clarify where execution changes first, and they scale because they reduce friction and improve decision flow rather than adding more effort. 

1) Connected workflow coordination reduces system sprawl 

The typical physician gets routed across 30+ disconnected systems during the physician onboarding process. Handoffs add waiting time, and duplicate entry increases rework and dissatisfaction. Manual coordination across HR, credentialing, IT, and practice management teams adds rework and slows decisions. 

Replace fragmented coordination with shared workflow steps and clear ownership. Connect intake, credentialing, and provisioning through shared workflow steps and clear ownership. Reduce or remove duplicate entry by aligning how data moves across systems. 

What changes: Teams run key steps in parallel instead of waiting on serial handoffs. HR completes I-9 verification as credentialing verifies licenses and IT provisions access. The result shows up as shorter cycle time, fewer delays caused by missing information, and less manual coordination. 

FAQs: Workflow coordination 

What is the best way to streamline the physician onboarding process? 

The most effective way to streamline physician onboarding is to connect intake, credentialing, and IT provisioning through shared workflows with clear ownership. Running steps in parallel instead of sequentially reduces waiting time, limits rework, and shortens overall cycle time. 

2) Credentialing workflow design reduces the credentialing bottleneck 

Manual verification of licenses, certifications, and references often adds weeks to the cycle. Documents and confirmations spread across email threads and shared drives. Expiration dates slip when ownership and alerts stay unclear. Physicians do not have a clear view of what remains or where things stand. Credentialing teams lose time that should go to exception handling and quality checks. 

Build credentialing steps that verify, route, and track work consistently. Design credentialing so the record stays complete, tasks move to the right owner, and exceptions surface early. Provide a secure intake experience so physicians submit credentials once, then track progress with current status and clear next steps. 

What changes: Work that used to require back-and-forth often completes in days rather than weeks. Credentialing teams spend more time on quality and exceptions and less time on chasing updates. 

FAQs: Credentialing 

What causes delays in physician credentialing? 

Credentialing delays typically come from manual verification, missing documents, unclear ownership, and lack of visibility into status. When expiration dates, references, and approvals are tracked across emails or spreadsheets, cycle time increases and risk grows. 

How long should the physician credentialing process take? 

Well-structured physician onboarding programs often complete credentialing in weeks rather than months. Cycle time depends on specialty and payer requirements, but consistent workflows and early exception handling significantly reduce unnecessary delays. 

3) Up-to-date visibility enables proactive management 

Many healthcare organizations do not have an easy way to answer key operational questions about their physician onboarding process: 

  • What is the average credentialing cycle time by specialty? 
  • Which handoffs and approvals add the most waiting time? 
  • How does onboarding cycle time correlate with first-year retention? 
  • Where do providers most commonly get stuck? 

Create shared visibility into each provider’s status, ownership, and next required step. Visibility works best when it shows who owns the next action, what remains blocked, and what deadline is at risk. Trend views expose repeated delays in document submission, access provisioning, and start-date readiness. 

What changes: Visibility turns delays into solvable, owned work. Teams spot bottlenecks early enough to rebalance work and clear approvals. Performance trends guide resource allocation. Continuous improvement becomes practical because leaders can see where execution actually slows down. 

FAQs: Visibility and management 

Why is visibility important in the physician onboarding process? 

Visibility allows leaders to see ownership, blockers, and deadlines across onboarding steps. When status and trends remain visible, teams can intervene early, rebalance work, and prevent small delays from turning into missed start dates. 

4) A provider-first onboarding experience reduces friction 

Physicians joining your organization often balance clinical transitions, relocation, and personal logistics. Requirements can stay unclear, forms repeat, status visibility drops, and start dates feel uncertain. That friction becomes part of the physician experience before the first day. 

Design an onboarding experience that shows owners, deadlines, and next steps in one place. Provide contextual help that explains why documents matter and where to find them. Use simple progress indicators. Push updates so physicians receive timely notifications without constant checking. Add guided help and searchable answers for common questions. 

What changes: Clearer onboarding sets expectations for how work gets done. Unclear, inconsistent onboarding creates doubt about day-to-day execution. A provider-first experience reduces administrative inquiries, improves confidence, and supports stronger recruiting outcomes. 

FAQs: Physician experience 

How does onboarding experience affect physician retention? 

The physician onboarding experience shapes early confidence and trust. Clear timelines, fewer administrative burdens, and visible progress reduce frustration and signal how the organization supports clinicians, which influences first-year engagement and retention. 

The business case shows up beyond revenue 

Organizations onboarding 10+ physicians annually often see payback in a matter of weeks through faster time to first patient appointments and recovered operations capacity. 

The impact shows up in revenue, capacity, and retention. 

Revenue impact: Every week you shorten onboarding brings forward about $40,000 in revenue per provider. Cut a 20-week cycle to 10 weeks, and you bring forward about $400,000 in annual revenue per 10 physician starts. 

Operations capacity: Forty hours recovered per physician at $50 per hour equals $2,000 in recovered capacity value per hire. For organizations onboarding 50 physicians annually, that equals $100,000 in recovered operations capacity. 

Retention risk: First-year physician turnover can exceed $500,000 per departure in recruiting, onboarding, and lost productivity. Onboarding experience influences early confidence and commitment, especially when it sets expectations for how the organization supports clinical practice. 

Recruiting advantage: Organizations that share eight-week onboarding timelines in recruiting conversations often see stronger acceptance rates. That advantage compounds in tight talent markets. 

FAQs: Business impact 

How much does a delayed physician onboarding process cost? 

Each week of delay in the physician onboarding process can represent tens of thousands of dollars in unrealized revenue per provider. Delays also increase administrative effort, strain clinician capacity, and raise turnover risk, compounding the financial impact. 

Strategic advantages that scale across hiring waves 

Physician onboarding process speed creates long-term leverage when it improves execution, compliance, and provider confidence. These advantages scale because they reduce rework and clarify ownership across every hire. 

Scale without proportional cost 

Standardized workflows support higher onboarding volume without adding manual coordination. Many teams increase throughput because the process relies less on last-minute triage and more on consistent steps, clear owners, and shared visibility. 

Audit-ready compliance 

Verifications and documents stay tracked with a complete audit trail. Compliance controls strengthen when records remain complete and approvals remain visible. Routine audits become faster to support because the documentation stays connected to the workflow. 

Expand across roles 

A strong onboarding motion scales across advanced practice providers and other clinical roles because the same principles apply. Clear ownership, fewer handoffs, and consistent documentation reduce exceptions and improve predictability. 

Improve using data 

Up-to-date visibility into each provider’s status and next step helps leaders identify recurring delays and redesign the process where it slows down. Investment decisions tied to outcomes become easier because teams can see which changes reduce cycle time and rework. 

What healthcare organizations achieve when they modernize the physician onboarding process 

Healthcare organizations that modernize onboarding see consistent patterns in outcomes. 

  • 30–50% reduction in onboarding cycle time across clinical roles 
  • 50% reduction in credentialing cycle time from submission to approval 
  • 35% reduction in onboarding time for advanced practice providers 
  • Fewer tools needed to complete onboarding steps 
  • Up-to-date visibility into each provider’s status and next step 
  • Audit trails that strengthen compliance controls 
  • Improved provider satisfaction scores tied to clearer onboarding steps 

These results reflect patterns reported by health systems that turned onboarding into a faster, more reliable start process. 

Your path forward 

Over the last five years, our teams have helped healthcare organizations modernize their physician onboarding process. That work clarified where programs stall, what reduces cycle time without increasing risk, and what it takes to sustain adoption across hiring waves. 

Webinar: “The business case for better onboarding” 

See how the shifts work together to reduce delays and accelerate time to first patient appointments. The session connects the four shifts to a practical operating approach and an ROI model based on hiring volume. 

What you will learn 

  • Workflow patterns that reduce credentialing and provisioning delays 
  • How ownership and handoffs affect cycle time across HR, credentialing, and IT 
  • How teams sequence changes to produce early wins and sustain adoption 
  • An ROI model based on hiring volume and current cycle time 

Watch the webinar 

Physician onboarding assessment workshop 

Explore what a faster onboarding program looks like for your organization. The assessment workshop focuses on cycle time, ownership, and adoption so you can decide on next steps with confidence. 

What the assessment workshop covers 

  • Benchmark view: Understand how your cycle time compares across roles and specialties 
  • Process review: Identify the largest sources of waiting time and rework 
  • Systems review: Understand where data and handoffs break across HR, credentialing, and IT 
  • Implementation outline: Define phases, owners, and success measures 
  • A clear next-step plan 

Schedule the assessment workshop 

Delays keep compounding 

Every week of delay creates meaningful productivity and capacity impact. It also gives competitors a head start in attracting top talent. Health systems that streamline the physician onboarding process to eight-week cycles strengthen recruiting credibility, expand clinical capacity sooner, and improve provider confidence early. 

Earlier action brings earlier capacity and a repeatable onboarding motion. Watch the webinar to see how the shifts work together. Use the assessment workshop to identify where your cycle time stalls and what to change first. 

Crafting the modern organization: it’s all about fit, not a fixed formula 

Posted on January 6, 2026 by - Organizational Change & Culture

Some organizations navigate change with speed and control, while others stall. The difference often comes down to operating model design, the blueprint for how work flows across people, process, technology, and governance. In an AI-saturated world, operating models perform best when they fit the organization’s context, strategic intent, and real business outcomes. 

This article outlines how modern organizations approach operating model design. It focuses on teaming structures and AI-enabled ways of working, drawing on frameworks such as Elabor8’s Teaming Primes of Organizational Design. The central point stays constant: operating models succeed when they match your context and trade-offs are made deliberately. 

Why deliberate operating model design matters in the age of AI 

An operating model is the engine that turns strategy into execution. It defines how people, processes, technology, and culture work together to deliver value. In a fast-changing environment, deliberate operating model design drives outcomes such as: 

  • AI-first competitive advantage: applying AI where it improves speed, quality, and decision-making 
  • Staying on track: aligning teams and decisions to enterprise priorities, supported by AI-enabled performance signals and real-time progress visibility. 
  • Working smarter: optimizing how you deploy people and resources, streamlining workflows, and improving productivity by shifting routine tasks to AI-assisted automation and agents. 
  • Adapting with speed: responding to disruption and capturing opportunity through scenario planning, forecasting, and AI-enabled market sensing. 
  • Designing around the customer: building operating choices that improve experience, consistency, and trust. 
  • Embedding AI capabilities: placing intelligence into core workflows and defining how humans and AI collaborate in decisions and execution. 
  • Managing risk: designing governance that monitors compliance, bias, security, and model drift across AI-enabled decisioning. 
  • Engaging your teams: clarifying roles, strengthening collaboration, and reinforcing autonomy with accountability. 

The Teaming Primes: a practical lens for organizing the enterprise 

The Teaming Primes provide a structured way to design how an organization delivers value. They describe fundamental patterns for organizing work, including shifts towards customer and product alignment and the operating implications of AI-enabled execution. These shifts span a spectrum. 

On one end are traditional structures: departments organized around projects, technical components (such as a specific IT system), or business functions. These designs prioritize efficiency within established boundaries. In today’s environment, AI often shows up here as automation and optimization inside the function (for example, using AIOps to stabilize IT operations). The result typically improves internal efficiency and reliability. 

On the other end are customer- or product-aligned approaches: structures designed around how value flows to the customer. Organizations may align around customer journeys, products and services, or end-to-end value streams. In these models, AI is designed into the flow of work to improve speed, quality, and decision-making across the system. 

A key takeaway from the Teaming Primes is that many organizations recognize misalignment and struggle to correct it. The framework positions the organization as an adaptive system that can continually refocus on value delivery as the business, competitors, and customers change. 

Teaming structures: how work gets done 

Within any operating model, teaming structures determine how people collaborate and how decisions move. Many organizations are shifting towards flexible, empowered, cross-functional teams that accelerate delivery and improve customer alignment. Common teaming patterns include: 

Functional teams: grouped by specialized skills (for example, marketing or engineering). 

  • Good for: deep expertise, clear roles, operational efficiency. 
  • Watch out for: siloed thinking, slow cross-functional communication, and limited visibility into the end-to-end customer experience. 

Divisional teams: grouped by product line, geography, or customer segment. 

  • Good for: focus on specific markets or products, faster decision-making within the division. 
  • Watch out for: duplicated effort, reduced cross-division collaboration, and fragmentation across “mini silos”. 

Matrix teams: where people report to more than one leader, such as a functional manager and a project manager. 

  • Good for: shared expertise across projects, flexibility in resource allocation. 
  • Watch out for: role ambiguity, competing priorities, and increased coordination overhead. 

Cross-functional product teams: small teams with diverse skills that own a product or customer journey end-to-end. 

  • Good for: rapid iteration, strong customer alignment, higher autonomy, and improved engagement. 
  • Watch out for: significant cultural change requirements, challenges to traditional management practices, and scaling complexity. 

Process- or value stream-aligned teams: organized around an end-to-end value stream (for example, order to cash). 

  • Good for: optimizing value delivery across multiple functions, reducing hand-offs. 
  • Watch out for: complex coordination across functions, difficult governance. 

Networked/distributed teams: rely on flexible connections and collaboration across geographies and, in some cases, external partners. 

  • Good for: access to global talent, flexible resourcing, collaboration with external experts. 
  • Watch out for: requires strong communication practices and tooling, and introduces cultural and time zone coordination challenges. 

Taken together, these patterns raise an important question: how is work organized in your own business today, and how well is that serving you? Are you seeing the benefits these structures promise, and are the trade-offs showing up in familiar ways? Understanding where your current model helps or hinders execution sets the foundation for choosing what comes next. 

Why context drives operating model choices 

The effectiveness of an operating model depends on organizational context. Selecting the right design requires clarity across: 

Goals and vision: what outcomes matter most across the short, medium, and long term? Examples include growth, market expansion, innovation, cost leadership, and experience leadership. Innovation-led strategies often benefit from empowered product teams. Efficiency-led strategies often benefit from more standardized, process-driven designs. 

Starting point and capabilities: assess strengths and constraints across people, process, technology, and culture. Identify legacy systems and entrenched behaviors that slow change. Clarify current skills and the capability build required to reach your target state. 

Industry and market dynamics: how quickly is the market changing, and what do customers and competitors signal? Fast-moving environments typically demand adaptable structures and shorter decision cycles. 

Target outcomes: define the measurable results the new operating model must produce, such as faster product launches, improved customer experience, lower cost-to-serve, higher engagement, and stronger innovation throughput. 

Culture and leadership: assess readiness for empowerment, experimentation, and distributed decision-making. Strong operating models depend on leaders who reinforce new behaviors and teams who feel safe to learn, iterate, and improve. 

Making change stick through people 

Operating model design often focuses on structure, process, and technology. Implementation succeeds through people. The model delivers value when teams understand the intent, adopt the behaviors, and change how work gets done. 

People resist change when the purpose feels unclear or the shift feels unmanageable. The COM-B model for behavior change is a useful lens. For someone to adopt a new behavior, they need: 

  1. Capability (C): the skills and knowledge to do the behavior. 
  2. Opportunity (O): the right environment, resources, and support. 
  3. Motivation (M): the desire and reason to change. 

Using COM-B, focus areas for successful rollout include: 

Explain the purpose and benefits (motivation): clearly communicate why the change matters and how it improves outcomes for teams and the enterprise. Connect the operating model to strategy, measurable results, and better day-to-day execution. When teams see the value and understand the direction, motivation rises. 

Equip teams with skills (capability): new operating models demand new behaviors and, increasingly, AI-enabled ways of working. Invest in training that covers collaboration rituals, agile delivery practices, data fluency, AI literacy (ethical use of generative AI), and AI oversight (how leaders validate and govern agent outputs). Reinforce the human skills that make cross-functional delivery work, such as feedback and active listening. 

Set up the environment for success (opportunity): skills scale when the environment reinforces them. That includes: 

  • New processes: redesign workflows to fit the new structure, including hand-offs, decision rights, and where AI agents support decisions. 
  • Supportive technology: provide the tools people need to collaborate, work transparently, and access the right data. 
  • Clear roles and responsibilities: define who owns what so teams can act with confidence. 
  • Remove friction: address physical and social barriers that block adoption by updating policies, aligning incentives, and replacing outdated habits. 
  • Sustain motivation: after launch, reinforce commitment through empowerment, leadership attention, and visible support mechanisms. 
  • Lead by example: leaders model the behaviors the operating model requires. 
  • Safe space to try: create a culture that supports experimentation, learning, and constructive feedback without fear. 
  • Recognize and reward: celebrate progress and reward teams for adopting new ways of working. 
  • Listen and adapt: gather feedback on what works, identify friction, and use what you learn to refine the model. 

Designing with purpose and strategic intent 

Designing and implementing a modern operating model is an iterative process: 

  1. Assess the current state: understand where you are today. 
  1. Set guiding principles: define the design rules anchored to strategy and outcomes. Use them to steer every operating model decision. 
  1. Test and learn: run smaller-scale pilots for new structures and ways of working, then iterate based on evidence. 
  1. Improve continuously: review and refine the operating model as conditions change across the enterprise and the market. 

With a deliberate, iterative approach and frameworks such as Elabor8’s Teaming Primes, organizations can design operating models that fit their context and accelerate progress towards strategic goals. 

The goal is clarity on who you are, where you are headed, and how you organize to deliver outcomes on that path. People make the model real through daily decisions and execution. 

The $2.3 Million Problem Hiding in Your Operations 

Posted on December 26, 2025 by - Strategic Portfolio Management (SPM)

We analyzed over 200 mid-market companies and uncovered a consistent pattern: operational friction drains an average of $2.3 million annually from each organization. That loss directly constrains growth investment while faster-moving competitors compound their advantage. 

Mid-market leaders face a clear decision: how long can millions continue leaking from daily operations? 

Where operational inefficiency drains value every day 

Large portions of workforce capacity disappear into automatable tasks. Manual processes inflate operational costs and limit operational efficiency by slowing routine requests far beyond acceptable thresholds. 

Finance teams lose entire workdays each week to low-value administration. Operations leaders wait weeks to approve routine purchases that should move in hours. Procurement absorbs significant effort in administrative work while savings opportunities slip away unnoticed. 

When employees enjoy frictionless consumer experiences but face layered approvals and disconnected systems at work, retention erodes quickly. Replacement costs compound beyond salary alone, while innovation capacity exits with every departure. 

What is operational inefficiency in mid-market organizations?

Operational inefficiency refers to the hidden friction that accumulates across everyday processes, approvals, and handoffs. In mid-market organizations, this often shows up as manual work, disconnected systems, and slow decision-making that quietly increases cost while reducing execution speed and capacity.

Why does operational inefficiency become expensive over time?

Small delays and manual steps compound as organizations grow. Over time, inefficiency spreads across finance, procurement, HR, and operations, creating higher costs, slower execution, and reduced capacity for strategic work.

How mid-market organizations rewire operations with ServiceNow SPM 

Leading organizations rewire how work flows through their operations by adopting ServiceNow Strategic Portfolio Management (SPM) as a foundation for sustained performance gains. 

Across HR, Finance, Procurement, and Facilities, these organizations apply strategic portfolio management to create faster service resolution, higher self-service adoption, and shorter procurement cycles that return time and focus to strategic work. These results create competitive separation that compounds quarter after quarter. 

What is ServiceNow Strategic Portfolio Management (SPM)?

ServiceNow Strategic Portfolio Management (SPM) helps organizations prioritize, fund, and execute the work that delivers the most business value. It connects strategy, investment decisions, and execution so leaders can adjust priorities in real time and reduce waste caused by misaligned initiatives.

How does ServiceNow SPM help reduce operational inefficiency?

ServiceNow SPM improves visibility into demand, capacity, and investment decisions across the enterprise. By aligning work to strategic priorities and streamlining decision flows, organizations reduce manual effort, shorten approval cycles, and eliminate work that does not contribute to measurable outcomes.

Why mid-market ServiceNow transformations accelerate faster than enterprise programs 

Mid-market organizations operate at a unique intersection of scale and agility. Complexity exists, but decision-making remains fast. This combination allows transformation programs to gain momentum quickly when guided by a structured, outcome-driven approach. 

A disciplined ServiceNow consulting approach builds early traction by identifying high-impact friction, redesigning cross-functional workflows, accelerating adoption, and sustaining continuous optimization across the enterprise. 

Early phases establish a unified employee experience foundation. Subsequent stages expand automation, adoption, and cross-functional impact across the organization. 

Why is ServiceNow SPM especially relevant for mid-market organizations?

Mid-market organizations often operate with enterprise-level complexity while retaining faster decision-making and execution speed. ServiceNow SPM brings structure to prioritization and funding without slowing momentum, enabling faster transformation and clearer accountability than traditional approaches.

How is ServiceNow SPM different from traditional portfolio management tools?

Traditional portfolio management tools focus on tracking projects after decisions are made. ServiceNow SPM connects planning, funding, and execution in a single system, enabling continuous prioritization, real-time visibility, and tighter alignment between strategy and operational delivery as conditions change.

See the full path from operational friction to scalable mid-market advantage 

Operational inefficiency rarely announces itself clearly. It hides inside everyday delays, fragmented systems, and manual work that quietly compounds cost. 

Our comprehensive ebook, Rewire Your Business: The Mid-Market Guide to AI-Driven Transformation, explains how mid-market leaders work with ServiceNow SPM implementation partners to convert operational friction into sustained advantage. 

Inside the ebook, you’ll explore how ServiceNow SPM drives impact: 

  • How leading organizations compress procurement cycles and reclaim capacity through a proven ServiceNow SPM transformation framework. 
  • How unified employee experiences reduce attrition risk and improve service performance across HR, IT, Finance, and Procurement. 
  • Where hidden operational costs accumulate and how intelligent automation redirects savings toward growth. 
  • What a structured, milestone-driven transformation journey looks like when execution and value move together. 

Momentum is building across the mid-market with ServiceNow SPM 

Organizations across the mid-market are reshaping operations through ServiceNow SPM partnerships that prioritize flow, speed, and measurable outcomes. As automation expands and expectations rise, operational friction becomes harder to tolerate and more expensive to ignore. 

The complete transformation playbook for reclaiming lost value is waiting. 

Reskilling vs. upskilling: choosing the right strategy for AI-first readiness

Posted on December 22, 2025 by - AI Transformation, Learning

AI is reshaping how teams work, how decisions get made, and how value gets delivered. Many organizations now face the same urgent question:

How do we prepare our people to perform in what’s next?

Some build training programs. Others redesign the organization and restructure roles.

Speed creates a common failure mode. Teams blur the most critical distinction.

Learning strategies solve different workforce problems, and the differences decide ROI.

Leaders build an AI-first workforce by aligning learning to the workforce shift in motion. That alignment equips teams to integrate intelligent systems and improve business performance.

That requires a clear distinction between two strategies: reskilling and upskilling.

Understanding the talent pressure behind AI-driven transformation

Today’s workforce faces role evolution alongside the skill gap.

The World Economic Forum’s Future of Jobs Report 2025 finds that nearly 40% of core skills will change by 2030, reflecting broad transformation pressures on skill requirements. IBM’s Institute for Business Value research shows that 40% of the global workforce, a proxy for how deeply AI is reshaping job responsibilities worldwide. 

For enterprise leaders, this creates immediate operating-model pressure:

  • How do we ensure teams use new tools and systems effectively?
  • How do we redesign roles AI is fundamentally altering?
  • How do we do it while protecting time, budget, and talent?

Two predictable traps emerge.

  1. Blanket upskilling pushes training to everyone before leaders define which roles must evolve.
  2. Reactive reskilling waits for role obsolescence before retraining or redeploying talent.

Both approaches waste investment and slow performance.

Leaders need a targeted strategy that matches learning investment to the talent shift underway.

Reskilling vs. upskilling: a strategic comparison

Leaders can operationalize the difference between upskilling and reskilling with a simple framing.

Upskilling addresses capability gaps in existing roles. Teams stay in role while adopting AI-augmented skills, increasing agility and performance in current workflows. AI-first tactics include contextual learning nudges and task-aware recommendations.

Reskilling addresses role displacement or redesign. Employees move into redefined roles as AI reshapes work, enabling workforce redeployment into strategic growth areas. AI-first tactics include capability mapping and role-based learning pathways.

In practice, upskilling builds deeper capability in the current role. Reskilling prepares talent to succeed in a new, value-aligned role.

Both strategies strengthen an AI-first workforce when they align to the transformation underway.

What can go wrong: three hidden risks to avoid

Even well-intentioned strategies backfire when leaders misread the workforce shift underway.

Three risks show up repeatedly.

1. The upskill-only trap

Organizations default to upskilling because it feels politically safe, deploys quickly, and creates the appearance of momentum. In many cases, AI is already phasing out those roles or restructuring them radically.

One enterprise trained hundreds of employees on AI tools. Six months later, those tools had replaced half the workflow the teams were supporting.

The training reinforced an outdated structure and diluted productivity gains.

2. The role collapse effect

AI reshapes jobs by merging, compressing, or splitting responsibilities in unpredictable ways. When one role expands from three responsibilities to seven and spans two teams, people feel overworked and underprepared.

In several digital product organizations, roles such as business analyst, project manager, and scrum master are converging. AI automates status tracking and reporting. Humans manage risk, interpret system-level dependencies, and guide value delivery.

Job titles stay stable while the work changes dramatically.

3. The ghost gap

The most important capabilities in an AI-first organization, such as judgment, orchestration, prompt fluency, and signal interpretation, rarely appear in job frameworks or learning catalogs.

When teams fail to name these capabilities, training never targets them. The result is predictable blind spots.

Hybrid AI-human systems amplify the risk. A misinterpreted AI suggestion. A poorly written prompt. A pattern not noticed early.

These failures reflect capability gaps.

Why this distinction matters more than ever

In AI-first teams, roles are evolving fast.

A customer support rep manages AI agents, flags anomalies, and optimizes system-level feedback loops alongside ticket resolution.

A product manager orchestrates predictive tools, interprets real-time user behavior, and coordinates across value streams.

If leaders treat these changes as minor shifts, the real transformation disappears.

These changes redefine roles. Preparing for them requires role-aware capability development.

That focus explains why organizations serious about intelligent transformation move beyond generic learning programs and build role-specific, signal-driven capability systems.

A proven framework for capability transformation

Many organizations operationalize reskilling and upskilling through a three-phase framework that balances insight, speed, and scalability.

1. Audit

Teams begin with real signal detection.

They examine what is actually happening in the work and where frictions, blockers, and behavior gaps surface across delivery tools, communication patterns, and decision cycles.

This approach functions as a capability pulse check rather than a static skills inventory.

In one healthcare technology organization, over 40 percent of team delays traced back to decision misalignment rather than technical skill gaps. Capability mapping addressed the issue more effectively than tool-focused training.

2. Architect

Once the gaps are clear, teams design for the future.

They define future-state roles and responsibilities, identify the capabilities those roles require beyond tasks or tools, and build learning journeys tied to real business objectives.

This work often surfaces capabilities such as AI orchestration, decision accountability in multi-agent systems, and feedback loop ownership. These capabilities span roles and frequently lack clear ownership until leaders deliberately define them.

3. Activate

Organizations then build enablement systems that bring those capabilities to life.

These systems include in-flow learning nudges, role-specific workshops, embedded coaching, and micro-retros based on team performance signals.

Because progress is measured by behavior change rather than course completion, teams can track how these capabilities improve decision-making, velocity, and delivery resilience over time.

How to choose the right strategy

If your team is using new tools in the same roles, upskill to improve fluency, speed, and alignment.

If your team is shifting into new workflows or structures, reskill into redefined roles with new responsibilities.

If you are leading a transformation, apply both strategies with clear orchestration and capability tracking.

Still unsure? Ask whether teams are retraining to do the same job better or preparing to do a different job well. Ask whether capacity supports what exists today or what comes next.

The future belongs to capability-driven organizations

Reskilling and upskilling remain foundational workforce strategies. Their design and delivery must evolve as intelligent transformation collapses feedback loops, merges human and AI workflows, and blurs role boundaries.

The future of work centers on activating the right capabilities at the right time and within the right roles. This capability focus defines high-performing AI-first organizations. This approach develops the kind of talent AI-first teams require to thrive.

How CIOs and CHROs cut developer ramp time with unified HR–IT orchestration 

Posted on November 21, 2025 by - AI Transformation, Employee Experience (EX)

Every enterprise feels the drag of manual onboarding, but nowhere is the impact sharper than in IT and software development. Engineering teams depend on a dense ecosystem of tools, repositories, environments, and security layers that must be ready on day one. When those systems stay disconnected, onboarding slows, productivity stalls, and visibility disappears across HR, IT, and engineering. 

This installment in our series builds on the foundational narrative introduced in the HRSM overview. It focuses on how intelligent orchestration accelerates onboarding for technical teams and strengthens the connection between HR and IT. 

Technical onboarding requires deeper integration across developer ecosystems. Access must align with engineering roles such as SRE, backend engineer, or platform engineer. Compliance requirements introduce additional complexity. The opportunity is clear: turn onboarding from a manual sequence into an intelligent flow that prepares engineers to build, test, and ship faster. 

Why technical onboarding breaks down 

Technical onboarding introduces challenges that ripple across HR, IT, and engineering, creating friction before work even begins. 

A dense ecosystem of developer tools 

Engineering onboarding involves far more than account activation. Developers need immediate access to repositories, CI/CD pipelines, cloud environments, secrets managers, monitoring tools, and container registries. Each system carries unique permission models and compliance requirements. Manual provisioning turns this into a web of dependencies, repeat requests, and approval bottlenecks. 

The amplified visibility gap 

HR triggers onboarding. IT provisions tools. Engineering managers define role requirements. Yet none of these stakeholders can see onboarding progress in real time. That gap slows sprint planning, blocks code commits, and adds friction to the earliest days of a developer’s experience. 

Productivity friction unique to IT and software 

When a new engineer waits for access, they lose more than time. They lose context. They lose momentum. They lose the confidence that they can contribute quickly. This friction extends across teams as code reviews stall, dependencies wait, and project timelines stretch. 

The fragmentation problem 

Fragmentation across tools, teams, and processes slows engineering productivity and weakens operational flow. 

Fragmentation across the software delivery lifecycle 

A typical engineering environment spans version control, build pipelines, infrastructure provisioning, observability, and deployment systems. Onboarding touches every one of these systems. Without unified orchestration, each step becomes a separate request, a separate approval, and a separate delay. 

Fragmented ownership 

HR manages identity. IT manages provisioning. Engineering manages tool-level permissions. Security manages compliance. Without orchestration linking these responsibilities, onboarding expands from a workflow into a maze. 

Manual work that scales poorly 

Manual provisioning introduces repeated steps: environment setup, key registration, permission alignment, testing access, and more. As organizations scale, these steps multiply. Automation becomes essential. 

The ideal state: orchestrated onboarding across HR, IT, and engineering 

An orchestrated model replaces disconnected tasks with an intelligent, connected flow that accelerates readiness. 

  • One workflow spanning three functions – Orchestrated onboarding connects HRIS, JSM, and developer tools into one intelligent workflow that activates at the moment of hire. 
  • Context-aware, role-based provisioning – Role templates define everything a developer needs based on engineering function. When HR updates a role in the HRIS, JSM immediately orchestrates the provisioning sequence. 
  • Real-time visibility for every stakeholder – HR sees onboarding progress. IT sees provisioning status. Engineering sees when tools are ready so new hires can join sprint work on time. 

Cprime’s solution: intelligent orchestration with JSM for engineering workflows 

Cprime leverages your Atlassian tool stack to redesign onboarding as an integrated, automated workflow that aligns HR, IT, and engineering from the start. 

Unifying HRIS, JSM, and developer tools 

Cprime architects a connected onboarding flow that integrates with systems such as GitHub, GitLab, Jenkins, and cloud IAM. These integrations turn access provisioning into a predictable, automated sequence. 

Turning JSM into the provisioning command center 

JSM becomes the orchestration hub: approvals, provisioning, compliance checks, and communication all move through a single, intelligent workflow. Automation eliminates handoffs and reduces rework. 

Accelerating engineering proficiency 

Atlassian reports that organizations implementing connected, automated onboarding through JSM see engineers reach full proficiency 34% faster. This acceleration comes from fewer delays, cleaner access patterns, and earlier engagement in active development. 

Strengthening security and compliance 

Standardized, role-based provisioning ensures engineers receive the appropriate level of access from the start. Every action is logged, auditable, and aligned with internal controls. 

Business outcomes for CIOs and CHROs 

A unified onboarding model drives measurable impact across productivity, efficiency, and experience. 

  • Faster time-to-productivity – Connected workflows eliminate multi-day waits and allow developers to contribute to active code and infrastructure work much sooner. 
  • Greater operational efficiency – IT handles fewer manual tickets. HR avoids status-tracking overhead. Engineering gains immediate clarity on environment readiness. 
  • Improved developer experience and retention – Developers start contributing earlier and avoid the frustration of stalled onboarding. 
  • Stronger governance – Orchestrated provisioning ensures every access decision is captured, monitored, and aligned with enterprise security standards. 

Why Cprime is uniquely positioned to deliver this 

Cprime brings the expertise, accelerators, and alignment needed to rewire onboarding into an intelligent engineering workflow. 

Cprime brings two decades of Atlassian expertise, proven accelerators for engineering-centric workflows, and a co-design approach that aligns HR, IT, and engineering around one unified experience. As organizations begin transitioning from digital-native to AI-first operations, orchestrated onboarding serves as a strategic foundation for more intelligent, adaptive engineering workflows. 

TBMC 2025 recap: a new era for TBM, value, and the modern operating model 

Posted on November 19, 2025 by - Financial Optimization / TBM / FinOps

TBMC 2025 signaled a turning point for enterprise leaders working to align strategy, spend, and execution. The energy in Miami made one thing unmistakable: TBM is accelerating beyond its roots, and the enterprises driving the most meaningful impact are the ones evolving their operating models around value flow, AI-first execution, and connected financial governance. 

This year’s conference surfaced a unified message across hundreds of conversations, sessions, and forums. Enterprises are moving faster. The pressure for financial clarity is rising. And leaders want a more adaptive, outcome-oriented model for decisions that affect every corner of the business. TBM sits at the center of that shift, and TBM Framework 5.01 emerged as the catalyst. 

Below is a clear view of the themes that shaped TBMC 2025 and what they mean for organizations building toward the next generation of operating discipline. 

TBM Framework 5.01: the most significant update in years 

The headline announcement reverberated across the entire event: TBM Framework 5.01 launches January 1. This update introduces a more modern, accessible, and value-focused version of TBM and reflects the changing demands of enterprise operations. 

The shift is more than cosmetic. Leaders responded strongly to the new emphasis on value drivers rather than data mechanics, a notable realignment that gives organizations a clearer starting point and a faster path to meaningful outcomes. 

Key elements of TBM 5.01 that stood out to attendees 
  • Streamlined certification, replacing outdated programs with a more modern, practical approach. 
  • Practical adoption guidance, including how to start implementing the framework in 30 days—something practitioners have requested for years. 
  • More digestible onboarding materials for new adopters, reducing the learning curve historically required. 
  • Published knowledge artifacts, replacing the tacit, community-driven guidance that previously filled the gaps in documentation. 

These changes significantly lower the barrier for adoption while presenting new opportunities for teams ready to mature into a more sophisticated TBM practice. 

Yet the excitement came with a dose of realism. Many attendees said the pace of change feels fast. As organizations prepare to adopt the new materials, interest in structured learning, advisory support, and TBM-aligned assessments surged. Leaders understand the value of the new framework; now they want a clear roadmap for approaching it with confidence. 

AI-first transformation, enterprise architecture, and value orchestration dominated the event 

While TBM 5.01 was the biggest announcement, the broader conversation revealed deeper shifts in how organizations plan to run. 

AI-first expectations drove the transformation dialogue 

Across sessions and informal discussions, enterprise leaders focused on the same goal: use AI to close execution gaps and bring decision velocity closer to real time. Many described AI as the missing mechanism that links TBM insights to operational change. 

The appetite was strong, but grounded. Leaders want AI applied with purpose, not novelty. They want to accelerate forecasting, strengthen investment choices, and automate the mechanics that slow down strategic execution. 

Enterprise architecture returned to center stage 

EA sessions drew sustained interest across the conference. Leaders are recognizing that enterprise architecture—when paired with TBM and modern portfolio practices—becomes a driver of value flow instead of a gatekeeper. Conversations centered around: 

  • Breaking architecture into clear delivery streams 
  • Integrating architecture earlier in decision cycles 
  • Using EA to modernize platforms and prepare for AI-first operations 

The message landed well: EA is no longer about standards alone. It is part of the machinery of value creation. 

See this in action with the story of a leading international airline that gained the visibility needed to accelerate decisions, reduce friction, and operate with greater strategic confidence. 

Read the case study

Tool-agnostic TBM gained meaningful momentum 

In hallway conversations and booth discussions, a clear trend emerged. Many organizations evaluating TBM practices are moving toward tool-agnostic approaches. Some attendees were not using Apptio at all. Others were exploring more flexible or cost-efficient platforms. The industry is shifting toward TBM as a discipline, not a product. Leaders want adaptability without sacrificing structure. 

Executive alignment remains both critical and challenging 

CFOs and CIOs increasingly share ownership of investment governance, value realization, and performance visibility. Yet many leaders acknowledged ongoing uncertainty about how to activate that partnership inside their operating model. Interest in clearer governance patterns and dynamic funding mechanisms was strong throughout the event. 

The call for real examples was louder than ever 

One theme rose consistently above all others: leaders want to learn from real journeys, not just frameworks. 

Case-based sessions filled quickly. Attendees gravitated toward stories that revealed patterns, mistakes, and practical ways to navigate complexity. 

The appetite for clarity was unmistakable. Organizations want guidance they can act on, rooted in real enterprise behavior, not only theoretical best practices. This reinforces the increasing demand for advisory interaction, working sessions, and diagnostic assessments. 

Cprime’s presence and perspective made an impact 

Cprime’s voice resonated strongly throughout TBMC 2025, and the response validated the central role of the Enterprise Operating Model in powering TBM effectiveness. 

High engagement across our ten sessions 

Our sessions on TBM, enterprise finance, portfolio performance, architecture, and operating model design consistently drew strong attendance. Leaders gravitated toward the intersection of TBM and the operating model: how decisions accelerate when financial governance, architecture, strategy, and delivery operate as a connected system. 

Industry recognition: Apptio’s Americas SPM Partner of the Year 

Cprime received Apptio’s Americas SPM Partner of the Year award, underscoring the measurable value organizations are achieving through our TBM and SPM engagements. The award reflects the momentum behind outcome-based operating model transformation and reinforces our role in shaping the next chapter of this discipline. 

Strong alignment with market needs 

Every conversation we had at TBMC pointed toward the same conclusion: enterprises are ready for a more connected approach to TBM that integrates strategy, financial governance, architecture, portfolio decisions, and AI-first execution into a cohesive operating model. 

What 5.01 means for enterprises moving forward 

TBM 5.01 marks a shift toward simplicity, clarity, and value. The implications for enterprise leaders are significant. 

For organizations beginning their TBM journey: the new guidance accelerates early adoption by providing clearer starting points and structured materials. Teams can apply TBM with greater consistency, confidence, and alignment from day one. 

For maturing practitioners: 5.01 encourages a deeper look at value drivers, costing models, and governance practices. Assessments and maturity analysis will become essential as teams refine how TBM shapes financial and operational decisions. 

For enterprises building AI-first operating models: TBM becomes the economic intelligence layer that powers real-time planning, prioritization, and value flow. AI-first execution demands a clear understanding of how capital, capacity, and outcomes move across the enterprise. TBM strengthens that foundation. 

How Cprime is helping leaders navigate the 5.01 era 

As TBM evolves, enterprises benefit from clear guidance and a structured path that helps them apply the new framework with confidence. We’re helping organizations accelerate momentum through: 

TBM 5.01 learning and enablement: Training and education designed to make the new framework accessible, actionable, and tied directly to enterprise priorities. 

Certification preparedness: Support for teams navigating the modernized certification process and building foundational fluency in TBM 5.01. 

TBM assessments: Diagnostic assessments to evaluate maturity, identify gaps, and highlight the highest-impact areas for improvement, aligned to both 5.01 and Enterprise Operating Model practices. 

Integration into the Enterprise Operating Model: TBM strengthens the connective tissue of the EOM, linking strategy, investment decisions, architecture, and execution. Our operating model solutions help leaders build systems where TBM becomes part of the enterprise’s ongoing rhythm. 

Navigating the next wave of organizational change: insights from the front line 

Posted on November 14, 2025 by - Learning, Organizational Change & Culture

Every organization is feeling the pressure to adapt faster than ever. Successful transformation demands clarity, commitment, and the right tools, far beyond a simple acknowledgement that change is necessary.  

We brought together a panel of industry leaders for a frank discussion on the challenges and successes of modernizing organizational practices. The conversation spanned topics from shifting mindsets to integrating new technology and revealed key insights for any company preparing for its next stage of evolution. 

The potholes on the road to agility 

A common pain point emerged immediately: the pace of change itself. Many established organizations move too slowly, weighted down by traditional practices and rigid annual cycles. This adherence to old ways often leads to weak prioritization and delayed value, especially when it comes to deciding what work truly matters.  

The real risk lies in prioritizing opinion over economic value. A traditional project-based mentality encourages big, front-loaded expectations with a fixed scope, leaving little room for learning or incremental delivery. This pattern erodes return on investment because much of the potential value surfaces only at the very end of a long project. 

Key challenges highlighted 

The funding shift: Moving from project cost accounting to a product-based operating model is a major financial and cultural hurdle. It requires senior leadership to fund autonomous value streams with an eye toward continuous delivery instead of a single, fixed outcome. 

Mindset and cultural entropy: Getting long-tenured employees to abandon deeply ingrained workflows is challenging. Leaders need to actively support and reinforce the new way of working to prevent teams from reverting to comfortable but ineffective old habits. 

Initial expectations vs. reality: While the promise of Agile is often speed, the immediate gain is usually increased transparency and earlier feedback. Adopting a new framework enables you to deliver valuable increments sooner and surface issues earlier, even when the underlying work remains complex. 

The strategic path to product-centricity 

For organizations committed to making the leap, one team’s transition from an older tool (Planview) to Targetprocess provided a powerful case study.  

A key to their success was acknowledging early that they could not do it alone. Bringing in external partners and coaches added a “new voice in the conversation” and helped accelerate adoption of a structured scaling framework such as SAFe (Scaled Agile Framework).  

A pivotal decision was their shift in focus: they used the migration as an opportunity to re-evaluate their core processes. Instead of configuring a new tool around broken, outdated processes, they first defined how they wanted to work and then configured the platform to support that modern, product-focused methodology.  

The result was rapid deployment of the new system, immediate visibility into data quality issues that had been hidden for years, and, most importantly, the ability to challenge existing norms based on rich, objective data. This new transparency provided a clear line of sight from strategy to execution, helping to eliminate noise and focus capacity on the most valuable work. 

Looking ahead: the AI-powered portfolio 

Looking ahead, AI dominated the conversation. For many organizations, AI investment is a given; the real question is how to manage the corresponding surge in new, complex initiatives. Technology leaders must treat AI as a critical area for portfolio investment and move beyond viewing it as just another cost line. That shift requires leaders to: 

Quantify business value: Accurately measure the financial impact of AI initiatives, whether through cost savings, new revenue streams, or risk reduction. 

Manage the portfolio for innovation: Use the enterprise portfolio tool to track AI investments alongside core product development and ensure alignment with top-level organizational goals. 

Harness AI for the portfolio itself: Use new AI capabilities to analyze portfolio data, predict outcomes, and flag potential bottlenecks so prioritization becomes an informed, data-driven activity rather than a political one. 

Final takeaway: start simple, be tenacious 

For any organization on this journey, the advice from the panel was clear: anchor every decision in a specific business outcome. Be clear on the result you are trying to achieve, invest in the right expertise and tooling with confidence, and approach the work as a marathon that rewards sustained commitment.  

The incremental gains of true agility, transparency, and data-driven decision-making become the foundation for sustainable success. 

How AI-first teams can learn and adapt without waiting

Posted on November 12, 2025 by - Agile & DevOps, AI-Ready Foundations

Agile teams struggle less with reflection itself than with timing. They reflect too late. 

In today’s market, the cost of delayed learning is real: missed deadlines, rising defect rates, burned-out teams, and customer churn hiding in plain sight. 

Most organizations still rely on weekly or biweekly retrospectives as their primary source of insight. That approach is like trying to steer a race car by checking the rearview mirror every few miles. 

But in an AI-native delivery environment, learning shifts from a scheduled event to a continuous system behavior. 

It’s embedded. Continuous. Contextual. 

It is happening right now, whether your team feels ready or not. 

From reflection to orchestration: the shift that changes everything 

We call this shift the “Retrospective Collapse.” It compresses feedback loops from discrete meetings into a state of always-on insight, orchestrated by AI. 

Retrospective Collapse supplements human reflection with real-time, always-available feedback signals that help teams adapt before problems snowball. 

It’s a capability shift powered by Flow-Aware Learning Systems. These are AI-native environments that continuously surface, interpret, and act on delivery intelligence, without waiting for a meeting, a survey, or a formal review. 

What AI-Native Learning Looks Like in Practice 

AI-native teams already deploy AI to accelerate learning across three dimensions: 

1. Blocker Detection Before the Block 
  • LLMs parse communication channels (such as Slack and Jira comments) to detect hesitation, confusion, or duplicate questions. 
  • AI agents flags stories that bounce between swimlanes more than twice and trigger a “pattern of churn” alert. 
  • Sprint dashboards evolve from static charts into real-time friction maps. 
     
2. Real-Time Team Health Signals 
  • Sentiment analysis identifies early signs of burnout or disengagement and feeds insights to Scrum Masters or Agile Coaches automatically. 
  • AI highlights individuals who haven’t contributed to discussions or PRs in several sprints, creating an early flag for morale or bandwidth issues. 
     
3. AI-Augmented Continuous Improvement 
  • Instead of retro notes disappearing into Confluence pages, LLMs convert them into prioritized backlog refinement suggestions. 
  • Delivery metrics combine with NLP-driven qualitative feedback to create coachable moments embedded in the workflow. 
     

These capabilities work within existing tooling by integrating agentic AI into the platforms teams already use. 

Why This Matters Now 

Across AI-native delivery environments, results include: 

  • 18% faster cycle times when AI surfaces blockers mid-sprint 
  • 22% less rework when teams act on continuous insight vs. biweekly feedback 
  • Stronger team satisfaction and velocity scores when improvement opportunities are shared across teams rather than trapped in silos 

When you shorten the time between signal and response, you ship faster, and build smarter, more sustainable teams. 

Your retrospective still matters, but it no longer stands alone. 

The agile ceremony remains essential, and in AI-first teams its role is evolving. 

Retrospectives become moments of synthesis that build on ongoing discovery.  

Learning happens throughout the sprint, not just at the end. 

The next generation of high-performing teams will stand out by how quickly they adapt, because their systems learn with them. 

The real question for every leader: what are your teams still not seeing in time to act? 

From Listening to Leading: Our Journey to TBMC 2025 

Posted on October 29, 2025 by - Enterprise Operating Model, Financial Optimization / TBM / FinOps

Five years ago, we joined TBMC to listen, learn, and share our early perspective. We were a small group of practitioners driven to make technology spending strategic, connecting investments, people, and outcomes through smarter flow. 

Today, at TBMC 2025, that story comes full circle. What started as a few conversations in breakout rooms has evolved into a movement shaped by our clients, our frameworks, and the growth of TBM itself. This year, we return as Platinum Sponsors, hosting 10 sessions and a keynote featuring transformation stories from Southwest Airlines, CNO Financial, and The Standard. 

Shaping the enterprise operating model of the future 

Every one of our sessions this year revolves around a single idea: the modern enterprise operating model, a connected system that aligns strategy, finance, and delivery into a continuous flow of value. From AI-integrated operations and real-time CapEx orchestration to evidence-based management and enterprise finance transformation, we’re showing how TBM is evolving from a cost framework into the performance engine of the modern operating model. 

Whether it’s: 

  • Southwest Airlines reimagining enterprise architecture as the core of agility 
  • BNY Mellon driving evidence-based investment decisions 
  • CNO Financial proving that small starts can scale into enterprise transformation 

Each session and story showcases what’s possible when TBM moves from governance to orchestration. 

From frameworks to living systems 

Our journey mirrors that of our clients, evolving from frameworks and tools into living systems of performance that connect strategy, funding, and delivery into measurable flow. TBM now forms the foundation for adaptive enterprises that learn, evolve, and compete with confidence. 

As we take the stage this year, we celebrate the community that made this evolution real, the clients who transformed TBM theory into enterprise performance, and the shared vision for what comes next: a future where TBM powers every strategic decision across the enterprise. 

Join us at #TBMC25 and see how the next generation of the enterprise operating model is being built, one decision, one connection, and one transformation at a time.  

AI in L&D: enhancing experiences, personalizing training, and improving accessibility 

Posted on October 27, 2025 by - AI-Ready Foundations, Learning

AI brings learning into a new light, reshaping how people learn, grow, and develop across organizations. Learning and development (L&D) is shifting fast, and artificial intelligence (AI) is driving the change. AI now reshapes how people learn, grow, and develop across organizations. From hyper-personalized learning paths to immersive “choose-your-own-adventure” simulations, AI equips L&D teams to build a skilled, engaged, future-ready workforce. 

Rapid technology shifts and changing roles raise the premium on learning and adaptability. Traditional one-size-fits-all training misses the diverse needs of today’s workforce. AI delivers targeted solutions that bring new clarity to how learning connects people and progress, making learning more effective, engaging, and accessible. 

Enhancing the learning experience: beyond the digital textbook 

AI elevates corporate training beyond static decks and lengthy documents. Here’s how: 

AI-powered content creation and curation: 

Generative AI tools can rapidly create a variety of learning materials, from interactive simulations and quizzes to realistic video scenarios. AI also curates up-to-date resources for each learner by scanning large content libraries, saving L&D teams significant time. 

Virtual tutors and AI coaches: 

Always-available virtual tutors support learners 24/7. AI-powered chatbots and mentors provide instant support, answer questions, and guide in the flow of work. These AI companions simulate real-world conversations, deliver performance feedback, and adapt to each learner’s pace. 

Gamification and immersive learning: 

AI adds competition and play to drive engagement. Adaptive challenges, leaderboards, and branching narratives in AI-driven gamification boost engagement and retention. Combined with virtual and augmented reality (VR/AR), AI enables realistic, immersive environments for hands-on training in safe, controlled settings. 

The power of personalization: one size fits one 

L&D aims to deliver truly individualized learning. AI now makes that ambition practical at scale. 

Adaptive learning paths: 

AI-enabled learning management systems (LMS) and learning experience platforms (LXP) analyze data on skills, roles, aspirations, and preferences. AI then constructs unique learning paths and recommends relevant courses, articles, and activities to advance each learner’s goals. 

Identifying and closing knowledge gaps: 

AI excels at identifying subtle patterns and gaps in a learner’s understanding. Intelligent assessments and continuous monitoring pinpoint where an employee needs support and deliver targeted micro-learning in real time. This proactive approach keeps learning relevant and impactful. 

“Choose-your-own-adventure” learning: 

AI-powered branching scenarios and interactive storytelling put learners in the driver’s seat. In these modules, the narrative adapts to each decision, creating engaging, memorable experiences. This approach develops critical thinking, problem solving, and decision-making. 

Accessibility for all: removing barriers to learning 

Real-time translation and transcription: 

For global organizations, AI translates learning content into multiple languages instantly, removing communication barriers. Real-time captioning and transcription in video-based learning improve access for people who are deaf or hard of hearing. 

Text-to-speech and speech-to-text: 

AI-powered text-to-speech converts written content to audio to support learners with visual impairments or reading disabilities. Speech-to-text lets learners dictate responses and interact with platforms by voice. 

Support for neurodiversity: 

AI can be tailored to support neurodiverse learners. For example, it can offer alternative content formats for those with dyslexia and break information into smaller, timed chunks with reminders for learners with ADHD. 

The AI-enabled L&D function: a glimpse into the future 

Integrating AI into learning and training management systems (LMS/TMS) modernizes L&D administration. AI automates course scheduling, learner enrollment, and progress tracking, freeing L&D teams to focus on strategic initiatives. AI-powered analytics reveal program effectiveness and enable data-driven decisions and continuous improvement. 

L&D’s future tracks with the evolution of AI. Expect more sophisticated applications: hyper-personalized learning that adapts in the moment, AI-driven predictive analytics that surface future skills gaps, and seamless integration of learning into daily workflows. 

With AI, L&D teams will evolve from content providers into architects of dynamic, personalized learning ecosystems, illuminating new paths for growth and shining a clearer light on human potential. The goal is to empower employees with the knowledge and skills to thrive in a constantly changing world. The journey has just begun, and the possibilities are limitless.