Real World Leadership

Leadership One Day at a Time

  • The Hidden Price of Cloud Services: What CDOs and CFOs Must Know About Cloud Data Costs

    The Hidden Price of Cloud Services: What CDOs and CFOs Must Know About Cloud Data Costs

    We sold the board on agility and scale. We convinced the business that cloud would let teams experiment fast, spin up analytics, and iterate toward better decisions. And for the most part, that promise has been real.

    But there’s a quieter truth that doesn’t get as many slide deck minutes: cloud economics are variable, and in a world awash with data, that variability becomes the thing that keeps finance and data leaders awake at night. As both a CDO and CFO across multiple cloud migrations, I’ve seen the pattern too often: data gets created and uploaded cheaply; the expensive part is what we do with it afterward, how often we touch it, how we compute over it, and how and where we move it.

    Below I’ll walk through the behavioral and technical drivers of variable cloud cost, show the critical difference between creating/uploading data and consuming it, point to market data and reporting where possible, describe documented financial impact cases, and close with practical guardrails you can apply now to reconcile speed with fiscal discipline.

    Variable cost is the new normal

    Historically, IT costs were largely fixed: you bought servers, depreciated them, and budgeted for refresh cycles. Cloud flips that script. Storage, compute, and, critically, network transfers are metered. The bill arrives as a sum of thousands of operational decisions: how many clusters ran overnight, which queries scanned terabytes instead of gigabytes, which business intelligence dashboards refresh by default every five minutes.

    This pattern matters because many of those decisions are made by people who think in analytics and velocity, not dollars-per-GB. Engineers and data scientists treat compute as elastic, and they should, for innovation, but the elasticity becomes costly without governance. Recent industry reporting confirms that unexpected usage and egress fees are a leading cause of budget overruns. [1]

    Upload vs. download: the crucial distinction

    Cloud pricing is purposefully asymmetric. Ingress, uploading data into the cloud, is typically free or very cheap. Providers want your data on their platform. Egress, moving data out of the cloud, between regions, or to downstream consumers, is where the economics bite. That’s why uploading billions of log lines feels inexpensive, but serving those logs to users, copying datasets between regions, or exporting terabytes for partner analytics can produce bills that scale in minutes.

    For example: major cloud providers publish tiered network and storage pricing where ingress is minimal and egress ranges by region and destination. Amazon’s S3 pricing pages and general data transfer documentation show free or near-free ingress alongside non-trivial outbound transfer rates that vary by region and tier. [2]
    [3]

    Put differently: storing a terabyte for a month costs one thing; repeatedly reading, copying, or exporting that terabyte is another. A platform that charges separately for compute time (for queries and pipelines), storage, and network transfer will make consumption the dominant lever in your monthly bill. For example, some analytic platforms separate compute + storage + egress explicitly. [4] [5]

    Where consumption surprises come from (and why they compound)

    Consumption overruns aren’t a single root cause, they’re a system. A few common patterns show up repeatedly:

    • Unfettered experimentation. Teams spin up large clusters, train big models, or run broad scans ‘for a test.’ A single heavy job run at full scale can spike costs for the month.
    • Chatty pipelines and duplication. Every copy, transform, and intermediate table multiplies storage and compute. When teams don’t centralize or catalogue datasets, duplicates proliferate and get processed again and again, increasing cost with each duplication.
    • Always-on analytics and reports. Hundreds of dashboards (and linked on demand reports) refreshing by default, real-time streams with high retention, and cron jobs without review all turn predictable activity into persistent cost.
    • Cross-region and multi-cloud traffic. Moving data between regions or providers often carries egress or inter-region fees. That cost is small per GB but large in aggregate, and it’s often invisible until it’s not.
    • AI and ML compute consumption. Training and inference on large models use GPU/accelerator time, which is expensive and scales super-linearly with workload size. [6]

    Industry surveys back this up: finance leaders consistently say a lack of visibility into technical drivers is a main contributor to runaway spending. [7]

    What the market tells us about scale and trajectory

    Two useful frames help here: (1) total cloud spending trends and (2) raw data growth.

    Analyst forecasts show cloud spending continues to accelerate. According to Gartner’s 2025 public-cloud forecast, worldwide end-user spending on public cloud services is projected to exceed US $720 billion, a strong year-over-year jump that underscores how much budget is flowing into cloud platforms. [8]

    On the data side, Fortune Business Insights [9] series quantified the explosion of the global datasphere: past forecasts put the global datasphere in the hundreds of zettabytes by the mid-2020s.  The scale is staggering, tens to hundreds of zettabytes of created, captured, copied, and consumed data, with continuous growth driven by IoT, media, and especially AI workloads that train on massive datasets. Those macro trends mean the base unit (how much data is available to touch) is rising fast which, left unmanaged, makes consumption costs an ever-larger line on the P&L.

    Documented cases of financial impact due to cloud consumption and egress costs

    Several documented cases highlight the financial impact of cloud consumption and egress costs:

    • A large large insurance company that generates over 200,000 customer statements a month are spending over $10,000,000 yearly just on customer statement generation as they pay the server side compute and data egress costs.
    • Data Canopy’s $20,000 monthly egress fees: Data Canopy, a provider of managed co-location and cloud services, was paying $20,000 monthly in egress fees by using VPN tunnelling to connect clients to AWS. VPN routes often introduce latency, lack scalability, and result in unpredictable costs due to fluctuating data-transfer volumes.
    • A startup’s $450,000 Google Cloud bill: A startup reported on the OpenMetal blog received a $450K Google Cloud bill after compromised API keys triggered massive unauthorized transfers in 45 days.
    • $120,000 AWS bill from a stress test: An engineering team set up infrastructure for a product stress test that copied large files from S3 to an EC2 instance. The setup led to a $120,000 AWS bill over the weekend due to data-transfer and compute costs.

    These cases underscore the importance of understanding and managing cloud consumption and egress costs to avoid unexpected financial burdens.

    Hard numbers and egress examples

    Exact per-GB egress numbers vary by provider, region, and tier, and providers publish detailed tiered pricing tables. A representative comparison often quoted shows outbound transfer rates commonly between US $0.05’$0.12 per GB in many regions, with variation for cross-region or inter-cloud transfers. 

    For platform-specific color: some analytic platforms break billing into distinct components (storage, compute, data transfer) so a scan-heavy workload that reads lots of compressed data can run up compute credits far faster than storage alone would suggest. [4]

    Forecast: growth + consumption = more financial focus

    Two simple forces are converging: raw data volumes continue to expand (zettabytes of data in the global datasphere), and enterprises are running more compute-heavy workloads (AI, real-time analytics, large-scale ETL). The combination means consumption bills will grow faster than storage bills. Cloud-spending forecasts (hundreds of billions annually) and rapid AI adoption make this inevitable unless governance catches up. In practice, expect your cloud‐consumption line to be one of the fastest-growing operational expenses over the next 3’5 years unless you adopt stronger cost visibility and control. [8]

    Practical Guardrails for Leaders Who Want Both Speed and Control

    Innovation does not stop because you start measuring costs. But you can innovate more safely. Below are detailed guardrails based on industry feedback:

    1. Real-Time Cost Telemetry + Visibility

    Treat cloud cost as you treat service downtime metrics. Engineers should see cost, usage, and performance side-by-side. For example, when a data scientist launches a heavy job, they should know in real time the incremental cost in dollars, not just cluster hours. Create dashboards that show compute usage, egress GBs, and storage growth with mapped cost. Set alarms for unexpected surges.

    2. Workload Ownership with Showback/Chargeback

    Every dataset, every pipeline, every compute environment needs a ‘budget owner.’ That person or team receives monthly cost summaries, cost variances, and the ability to act. If a team treats the cloud like a sandbox with no accountability, costs balloon. Use tagging and cost-center attribution so every resource is traceable. Monthly cost reviews should include business teams, not just engineering.

    3. Automated Lifecycle & Data Tiering Policies

    Treat data like the asset it is: ephemeral unless activated. Implement rules: dev/test clusters auto-shutdown after inactivity; datasets not accessed for 90 days shift to cold storage or archive; raw ingestion copies truncated or summarized. Remove or archive intermediate copies automatically. Set retention policies aligned to usage and cost thresholds. The fewer idle TBs sitting and refreshing, the smaller the ‘always-on’ burden.

    4. Right-size Compute & Leverage Auto-Scaling / Spot Instances

    Large, fixed clusters are easy but wasteful. Use auto-scaling or spot/pre-emptible instances where appropriate, particularly for non-mission-critical workloads. Enforce policies: cluster size ceiling, job timeout limits, query concurrency limits. Review usage logs monthly to optimize resource sizing and avoid ‘large cluster for test’ scenarios. Encourage cost awareness in engineering planning.

    5. Eliminate Duplication, Enforce Data Catalogue & Reuse

    Multiple copies of the same dataset, processed in isolation across teams, drive duplicate storage and compute. Create a central data catalogue, promote reuse of datasets, and mark copies only when necessary. Standardize ingestion patterns so that processes don’t proliferate ad-hoc pipelines. Encouraging teams to search existing assets before creating new ones reduces waste and cost.

    6. Tagging, Attribution & Forecasting

    Resources without tags are cost-blind. Ensure every cluster, dataset, job has tags for business unit, project, owner, environment (dev/test/prod). Use this to attribute cost, forecast spend based on usage trends, and model scenarios. Don’t treat cloud invoices as ‘job done’ at month’s end, use them as input to forecasting, cost optimization, and decision-making. Run ‘what-if’ modelling: what happens if ingestion doubles? What if egress increases by 50%?

    7. AI/ML Spend Discipline

    Training large models and real-time inference pipelines are expensive. Require clear business use-case and cost estimates before spinning large GPU/cluster jobs. Use smaller batch trials in cheaper environments, then scale only for production. Monitor overarching GPU-hour consumption and set thresholds. Make AI spend visible and subject to the same ownership and budget discipline as ETL or BI pipelines.

    8. Negotiate Committed Use / Savings Plans Where Appropriate

    If you can forecast a baseline level of consumption, negotiate committed-use discounts or savings plans with your cloud provider. Treat that baseline separately from the variable tail. The tail, experimental work, ad-hoc data movement, new analytics, stays uncommitted so you retain agility while limiting surprise.

    9. Capacity Building + Cost Literacy in Data Teams

    Last but not least: make cost behavior part of your data culture. Engineers, architects, analysts should all understand that ‘every query is a financial decision.’ Include cost implications in your onboarding, training, and architecture reviews. Celebrate teams that reduce cost while delivering performance. Make cost reduction visible, not just cost growth.

    Final Word: Treat Consumption as an Operational Discipline, Not a Surprise

    Cloud gives us extraordinary capabilities. But capabilities without constraints create risk. Consumption is a behavioral and architectural problem as much as a pricing problem. The data is growing exponentially; so must our financial stewardship.

    If you are a CDO, your role now includes translating technical choices into economic outcomes. If you are a CFO, your role now includes translating invoices into operational levers that engineers can act on. When those two disciplines converge, when finance and data speak the same language and operate with the same telemetry, cloud becomes less of a gamble and more of a controlled advantage.

    The cloud will continue to win for those who learn to measure not just bytes at rest, but the dollars behind every byte moved and every CPU-second consumed.

    References (summarized)

    1. CIO Dive ‘ Cloud data storage woes drive cost overruns, business delays, Feb 26 2025. https://www.ciodive.com/news/cloud-storage-overspend-wasabi/740940/  
    2. Amazon Web Services ‘ Amazon S3 Pricing. https://aws.amazon.com/s3/pricing
    3. Amazon Web Services ‘ AWS Products and Services Pricing. https://aws.amazon.com/pricing
    4. Snowflake Documentation ‘ Understanding overall cost. https://docs.snowflake.com/en/user-guide/cost-understanding-overall
    5. Microsoft Azure ‘ Azure Databricks Pricing. https://azure.microsoft.com/en-us/pricing/details/databricks
    6. CIO Dive ‘ What Wipro’s global CIO learned about AI cost overruns, Oct 6, 2025. https://www.ciodive.com/news/wipro-global-cio-generative-ai-agents-cost-deployment/801943
    7. CIO Dive ‘ Runaway cloud spending frustrates finance execs: Vertice, Sept 26, 2023. https://www.cfodive.com/news/runaway-cloud-spending-frustrates-finance-execs-vertice/694706
    8. CIO Dive ‘ Global cloud spend to surpass $700B in 2025 as hybrid adoption spreads: Gartner Nov 19, 2024 .
      https://www.ciodive.com/news/cloud-spend-growth-forecast-2025-gartner/733401
    9. Fortune Business Insights ‘ Data Storage Size, Share, Forecast, Oct 6, 2025. https://www.fortunebusinessinsights.com/data-storage-market-102991
    10. HelpNetSecurity ‘ Cloud security gains overshadowed by soaring storage fees, Mar 7 2025. https://www.helpnetsecurity.com/2025/03/07/cloud-storage-fees/  
    11. ComputerWeekly ‘ Unexpected costs hit many as they move to cloud storage, Mar 5 2024. https://www.computerweekly.com/news/366572292/Unexpected-costs-hit-many-as-they-move-to-cloud-storage  
    12. Academic paper ‘ Skyplane: Optimizing Transfer Cost and Throughput Using Cloud-Aware Overlays, Oct 2022. https://arxiv.org/abs/2210.07259  
    13. Gartner – Tame Data Egress Charges in the Public Cloud, Sept 2023. https://www.gartner.com/en/documents/4786031
    14. IDC ‘Future-Proofing Storage, Mar 2021. https://www.seagate.com/promos/future-proofing-storage-whitepaper/_shared/masters/future-proofing-storage-wp.pdf

     

    Eric Ford

  • AI Strategy: Developing a Technology Roadmap Aligned with Business Needs

    AI Strategy: Developing a Technology Roadmap Aligned with Business Needs

    In the orchestration of modern business, artificial intelligence and advanced analytics strike thrilling chords. They are no longer just fascinating novelties but indispensable instruments in the orchestra of strategic excellence. Yet, many organizations grapple with a dissonance: their tech endeavors often play solo, detached from the harmonious flow of overarching business goals.

    This misalignment leads to predictable outcomes: expensive AI projects that fail to deliver meaningful ROI, data science teams building impressive models that never reach production, and frustration from business leaders who don’t see the promised transformation.

    Below is a structured outline of an approach to developing an AI, analytics, and technology roadmap that truly aligns with your organization’s strategic priorities and financial realities.

    Understanding Your Business Foundations

    Before diving into technology decisions, it’s essential to establish a comprehensive understanding of what your business is trying to accomplish. Similar to how a conductor needs to grasp the essence of a symphony before leading an orchestra, understanding your company’s strategic goals is crucial. Only then can the AI and technology initiatives harmonize perfectly with the business’s ambitions.

    Start with strategy: Revisit your organization’s strategic plan, annual reports, and leadership communications. What are the 3-5 key objectives driving the business forward? Are you focused on cost reduction, market expansion, customer experience enhancement, preparing your company for sale, or operational excellence? Your AI roadmap should directly support these priorities.

    Map existing challenges: Where are the current pain points across your organization? Often, the most valuable AI applications address specific, well-defined problems rather than implementing technology for its own sake. Speak with frontline employees who understand operational challenges intimately. Be sure to look at cross department challenges not just challenges focused on individual departments or silos.

    Stakeholder interviews: Conduct structured interviews with leaders across departments, not just in IT. Ask questions like:
    • “What metrics are you accountable for improving?”
    • “Where do you spend most of your time?”
    • “What decisions would be easier with better information?”
    • “What would meaningfully change your ability to meet objectives?”
    • “What are cross department processes or value streams that your company would like to improve?”

    Define financial success: Work with finance teams to understand how technology investments will be evaluated. Establish clear metrics that resonate with business leaders—whether that’s revenue growth, cost reduction, improved margins, enhanced customer lifetime value, or reduced churn.

    Assess Current Technological Capabilities

    With a clear understanding of business needs, the next step is to honestly evaluate your organization’s technological readiness.

    Data infrastructure audit: Many AI failures stem from fundamental data issues. Assess your data architecture, storage solutions, integration capabilities, and data governance practices. Is your data accessible, accurate, complete, and timely? Without quality data foundations, advanced AI applications will struggle.

    Technology maturity assessment: Different AI approaches require different levels of technological sophistication. Be honest about where your organization stands on the maturity curve—from basic reporting to advanced machine learning. This will help set realistic expectations about what’s immediately achievable versus what needs foundational investment.

    Skills inventory: Catalog the data and technical skills currently available in your organization. Look beyond official job titles to identify hidden talents and potential champions. Where are the gaps between your current capabilities and what you’ll need to execute your strategy?

    Workflow analysis: Document how decisions are currently made across key business processes. Where do employees spend their time? Which processes still rely on manual intervention or tribal knowledge? These areas often represent prime opportunities for AI-driven automation or augmentation.

    Opportunity Identification and Prioritization

    With business needs and technological capabilities assessed, you can now identify specific AI opportunities worth pursuing.

    Opportunity framework: Develop a consistent framework for evaluating potential projects. Include considerations like:
    • Strategic alignment with business priorities
    • Potential financial impact (both revenue and cost)
    • Technical feasibility with current capabilities
    • Data requirements and availability
    • Organizational readiness and change management needs
    • Timeline to value realization

    Impact vs. effort matrix: Plot identified opportunities on a simple 2×2 matrix. The horizontal axis represents implementation difficulty, while the vertical axis represents potential business impact. This visualization helps identify “quick wins” (high impact, low effort) that can build momentum, as well as transformational projects that may require more significant investment.

    Project portfolio mix: Create a balanced portfolio of initiatives that includes:
    • Quick wins (3-6 months) to demonstrate value
    • Medium-term projects (6-12 months) building on initial successes
    • Strategic, transformational initiatives (12+ months) that may redefine business capabilities

    Value chain prioritization: When considering where to apply AI, prioritize core value chain activities over support functions. Improvements in product development, operations, or customer experience typically yield higher returns than back-office optimizations.

    Developing the Roadmap

    With prioritized opportunities in hand, it’s time to structure them into a coherent roadmap that accounts for dependencies and resource constraints.

    Phased implementation: Break larger initiatives into smaller, achievable phases with clear milestones. This approach allows for course correction and helps manage risk. For each phase, define specific deliverables and success criteria.

    Resource allocation: Be realistic about available resources—both human and financial. Avoid the common trap of trying to pursue too many initiatives simultaneously, which typically results in nothing being done well. Sequence projects to maximize resource utilization.

    Flexibility by design: Technology evolves rapidly, as do business priorities. Build flexibility into your roadmap by establishing regular review points (quarterly is often appropriate) where initiatives can be reprioritized based on changing conditions.

    Governance structure: Establish clear accountability for roadmap execution. Consider creating a cross-functional steering committee that includes both business and technology leaders to guide implementation and resolve conflicts as they arise.

    Driving Adoption and Managing Change

    Even the most technically sophisticated AI solutions fail without proper adoption. Your roadmap should explicitly address the human elements of implementation.

    Cross-functional teams: For each major initiative, create teams that include both technical experts and business domain specialists. This collaboration ensures solutions address real needs and helps build organizational buy-in.

    Skills development: Identify training needs across the organization—not just for technical teams. Business users will need education on how to effectively leverage new capabilities, while leaders may need guidance on data-driven decision-making.

    Success metrics: Establish clear KPIs for each initiative that relate directly to business outcomes. Avoid vanity metrics that don’t translate to business value (like model accuracy in isolation). Instead, focus on metrics that matter to stakeholders (like reduced processing time or improved customer satisfaction).

    Feedback mechanisms: Create structured processes for gathering user feedback throughout implementation. Use this input to refine solutions and address pain points quickly. Early adopters can become powerful advocates if their input is visibly incorporated.

    Financial Considerations

    AI investments need to demonstrate value to maintain organizational support. Your roadmap should include clear financial frameworks.

    Business case development: For major initiatives, develop comprehensive business cases that consider both quantitative benefits (cost savings, revenue increases) and qualitative improvements (better decisions, enhanced customer experience). Be conservative in your estimates to build credibility.

    ROI model adaptation: Traditional ROI models often struggle with AI initiatives where benefits may be probabilistic or emerge over time. Work with finance teams to develop appropriate evaluation frameworks that account for the unique characteristics of AI investments.

    Funding strategy: Consider alternative funding approaches beyond traditional annual budgeting. Options might include:
    • Innovation funds allocated specifically for experimentation
    • Shared funding models where multiple departments contribute
    • Value-based funding where initial successes fund future phases
    • External partnerships to share development costs

    Budget defense: Prepare clear, compelling narratives that connect technology investments to business outcomes. Frame AI initiatives as business transformation projects, not technology deployments.

    Leveraging AI and Technology Advisors

    Even with internal expertise, developing a comprehensive AI roadmap can benefit significantly from an external perspective. An experienced AI and Technology advisor can provide valuable input throughout the process.

    Objective assessment: External advisors bring an unbiased view of your current capabilities and realistic assessment of what’s achievable. They can help identify blind spots that internal teams may miss due to organizational politics or legacy thinking.

    Industry benchmarking: Quality advisors have visibility across multiple organizations and industries, allowing them to share relevant case studies, common pitfalls, and realistic timelines. This perspective helps set appropriate expectations and avoid reinventing the wheel.

    Technology guidance: The AI landscape evolves rapidly, with new tools and approaches emerging constantly. Advisors who specialize in this space can help navigate options, identifying which technologies are production-ready versus those that may still be experimental.

    Implementation acceleration: Experienced advisors can bring proven methodologies, templates, and frameworks that accelerate roadmap development. This structure helps organizations avoid common implementation pitfalls and compress time-to-value.

    Change management expertise: Many advisors specialize in the human aspects of technology transformation. They can help design effective change management approaches that increase adoption and minimize resistance.

    When selecting an advisor, look for:
    • Demonstrated experience in both technology implementation and business strategy
    • Specific technology expertise relevant to your industry
    • A collaborative approach that transfers knowledge to your team
    • Willingness to challenge assumptions constructively
    • Track record of successful implementations with referenceable outcomes
    The right advisor relationship functions as a true partnership, complementing your team’s strengths rather than replacing internal capabilities or dictating solutions without context.

    To Sum it Up

    Creating an effective AI and technology roadmap isn’t a purely technical exercise, it’s a strategic business planning process that requires thoughtful alignment between business objectives, technological capabilities, and organizational readiness.

    By following the approach outlined in this article, organizations can avoid the common pitfall of pursuing technology for its own sake. Instead, they can develop focused roadmaps that directly address business priorities and deliver measurable value.

    Remember that a roadmap is a living document, not a static plan. The most successful organizations maintain regular review cycles, adjusting course as business needs evolve and as implementation reveals new insights.

    The organizations that thrive in the AI era won’t necessarily be those with the most advanced technology or the largest data science teams. Rather, success will come to those who most effectively align their technological capabilities with clear business priorities—and execute with discipline against a well-structured roadmap.

    Eric Ford

  • More Than Résumés: Building an Effective Team by Getting the Right People in the Right Places

    More Than Résumés: Building an Effective Team by Getting the Right People in the Right Places

    More Than Résumés: Building an Effective Team by Getting the Right People in the Right Places

    You’ve got a growing responsibility: not just hiring talented individuals, but orchestrating a high-performing team.

    Organizational success isn’t driven by isolated stars—it’s driven by how well your team works together to solve problems, innovate, and adapt. The real work lies not in simply filling seats—but in making sure every person is in a seat where they can amplify impact.

    You’re stepping into the art of team design. And yes, it’s harder than reading résumés. But if you get this right, your leverage, your speed, and your resilience scale in ways you probably haven’t yet experienced.

    Here’s how to think about doing it well.

    The “Right People, Right Places” Equation

    At first glance, “right people” often means technical credentials, domain experience, and past success—reasonable checks. But exceptional teams demand a deeper level of discernment.

    Jim Collins’ famous advice captures this higher standard: get the right people on the bus, but then ensure those people sit in the right seats. That “seat” matters as much as the person.

    So when you evaluate candidates (or current team members), look beyond the baseline:

    1. Attitude and Mindset

    Skills get you in the door; attitude determines how far you’ll go.

    You want people who approach challenges with curiosity, not defensiveness—who see obstacles as opportunities to problem-solve, not as reasons to stall. This kind of mindset creates momentum.

    When things get messy, does this person look for blame or for solutions?

    The best team members absorb ambiguity and still move forward. They don’t need constant direction—they find ways to keep the mission alive when the path isn’t clear.

    It’s not about toxic positivity. It’s about grounded optimism—the belief that progress is always possible, even when it’s hard.

    2. A Learner’s Stance

    In fast-changing industries, the best skill isn’t mastery—it’s adaptability.

    You want people who are humble enough to admit what they don’t know and curious enough to go find out.

    “I’ve never done that before, but I’d love to figure it out.”

    A learner’s stance is the antidote to stagnation. It fuels innovation because learners naturally test, iterate, and improve. They don’t cling to old playbooks; they write new ones.

    When you’re interviewing or evaluating, watch for the language of learning: people who ask thoughtful questions, who talk about mistakes as growth moments, who light up when describing how they built new skills.

    3. Emotional Intelligence (EQ)

    Technical excellence without emotional awareness is a liability.

    EQ is the connective tissue of your team—it enables communication, empathy, and trust.

    Teams break down not because people can’t do the work—but because they can’t work with each other.

    • Sense when tension is rising and address it constructively.
    • Adjust their communication style for different audiences.
    • Listen deeply, not just to reply but to understand.
    • Offer feedback in a way that lands, not wounds.

    These aren’t soft skills—they’re performance multipliers. A high-EQ team makes smarter decisions faster because people can navigate complexity and conflict without derailment.

    4. The Ability to Lift Others

    True team players elevate the people around them. They don’t hoard credit or guard knowledge—they share it freely.

    This trait often hides in plain sight. It shows up when someone takes extra time to mentor a peer, covers for a teammate having a tough week, or quietly fixes a problem without demanding recognition.

    “Does this person make others better, or do they make others smaller?”

    These individuals make your team’s collective output greater than the sum of its parts. They’re culture carriers—people whose presence shapes a healthier, more generous environment.

    5. Values Alignment

    Skills can be taught. Values can’t.

    Misalignment here is slow poison—it starts subtle, but over time it erodes trust, consistency, and morale. That’s why you must hire (and promote) for values as deliberately as for skills.

    • “Tell me about a time you had to make a hard decision that went against the easy option.”
    • “What kind of environment brings out your best work?”
    • “What does success look like for you?”

    You’ll hear their compass in their answers. And that compass will either align with your culture—or it won’t.

    6. Problem-Solving Style

    Finally, look at how someone approaches problems, not just that they can solve them.

    Some people dive in immediately; others pause to analyze. Some thrive in collaboration; others prefer solo deep work. Neither is inherently better—but knowing this helps you build balance.

    You want diversity in problem-solving patterns. As a leader, your job is to orchestrate that mix—to pair complementary thinkers and make sure every style finds its place.

    The best teams have both the dreamers and the doers, the planners and the improvisers, the cautious and the bold.

    The Hidden Multiplier: Interpersonal Dynamics

    Imagine you drafted a roster of superstar players—but they never talk, trust one another, or resolve friction. You won’t win games.

    A team is not just a collection of individuals—it’s a social system. And how that system operates will make or break you.

    Interpersonal dynamics determine whether your team’s energy compounds or cancels itself out. When you ignore them, even the most talented people end up frustrated or leaving. When you nurture them intentionally, you unlock exponential performance.

    If You Ignore Team Dynamics, You Risk:

    1. Broken Communication and Misunderstanding

    The silent killer of productivity. Information gets trapped in pockets, intentions are misread, and people start making assumptions instead of asking questions. Collaboration slows, and small misalignments become major conflicts.

    The antidote: overcommunicate. Clarify purpose. Reinforce context. Encourage transparency—even when it feels repetitive. Repetition builds alignment.

    2. Escalating Conflict That Bleeds Energy

    Conflict isn’t bad—it’s necessary. But when it festers, it drains momentum. Energy that should fuel progress gets redirected into self-protection.

    The leader’s job: contain it, guide it, and convert it into constructive debate. Model calm inquiry instead of defensiveness. Address tension early instead of waiting for it to explode.

    3. Psychological Risk and Withheld Voices

    When people don’t feel safe to speak up, you lose your most valuable asset: truth. Innovation plummets, groupthink creeps in, and only the loudest voices get heard.

    Build safety: ask more than you tell. Reward candor. Celebrate speaking up—even when it challenges your thinking.

    4. Burnout, Disengagement, and Turnover

    Unchecked dynamics compound pressure. People feel unseen, communication turns transactional, and collaboration becomes emotional labor. The result: good people disengage or leave.

    Your role: protect morale and capacity. Ask about energy, not just output. Celebrate rest as part of sustainable performance.

    When You Intentionally Foster Healthy Dynamics, You Unlock:

    1. Ideas Flow Freely

    When communication is open and trust is high, creativity accelerates. People build on each other’s ideas instead of competing for airtime.

    Who has a different view? What might we be missing? What’s the risk no one’s naming?

    Curiosity sets the tone for collective intelligence to thrive.

    2. Differences Spark Creativity Instead of Division

    In high-trust teams, differences are assets, not irritants. Respect allows challenge without hostility. That tension becomes creative friction—the spark of innovation.

    Encourage debate: argue the idea, not the person. Frame clashes as complementary perspectives, not conflicts of ego.

    3. Members Lean In and Take Accountability

    When trust is strong, accountability feels shared. People follow through not out of fear—but pride. They own results because they care about not letting the team down.

    Model it: admit missteps, reward responsibility-taking, and normalize growth over perfection.

    4. Resilience and Adaptability Become Default

    When storms hit, healthy teams bend without breaking. They communicate early, redistribute work, and tackle uncertainty without panic. Safety enables honesty; honesty enables agility.

    This is real resilience—not the absence of pressure, but the ability to face it together.

    How to Place the “Right People in the Right Seats” Strategically

    1. Define roles with clarity — Don’t rely on vague titles. Ask: What must this person deliver? How will they collaborate? What constraints or tradeoffs define success?
    2. Hire beyond technical comfort — Use behavioral interviews, simulations, and scenario questions. Probe culture fit, collaboration, and curiosity. Let attitude and integrity be non-negotiable.
    3. Dialogue to discover strengths and gaps — Ask what energizes or drains people. Match those patterns to where they’ll thrive.
    4. Compose for diversity of thought and style — Mix strategists and executors, creatives and pragmatists. Diversity is insurance against stagnation.
    5. Architect psychological safety — Model humility. Reward candor. Respond to mistakes with learning, not punishment.
    6. Invest in team development — Workshops, retrospectives, and offsite strategy sessions aren’t fluff—they’re performance infrastructure.
    7. Reassess, adjust, reassign — Roles evolve. People grow. Reevaluate fit regularly. Sometimes a smart re-placement beats a replacement.

    The Return on This Work

    This isn’t leadership theory. It’s leverage.

    When you invest in getting the right people in the right places—and when you build the trust, communication, and safety that make them thrive—you unlock something that can’t be faked: momentum.

    The ROI shows up everywhere:

    • Productivity soars. Work flows cleaner because people understand their strengths and play to them.
    • Innovation multiplies. Diverse perspectives collide productively instead of defensively.
    • Turnover drops. People stay because they feel seen, valued, and set up to win.
    • Discretionary effort grows. Team members give more than they have to—because they believe in what they’re building.
    • Reputation compounds. You attract stronger talent because word spreads that your team is a place where people grow and succeed.

    But the deeper return isn’t just in metrics—it’s in energy.

    When a team clicks, everything moves faster. Decisions feel clearer. Tension becomes creative fuel instead of drag. You start to see a culture where people don’t just do their jobs—they own them.

    That’s what “right people, right places” really delivers: alignment, trust, and momentum that make performance sustainable.

    And that’s the kind of leadership that lasts.

    Final Thoughts: Your Next Moves

    Creating an effective team isn’t a checkbox—it’s your most strategic task as a leader.

    • Audit your existing team: Who’s under-leveraged or misaligned?
    • Clarify role expectations and outcomes.
    • Hold one-on-ones about fit, energy, and growth.
    • Plan one small but meaningful intervention—a role redesign, feedback loop, or team rhythm change.

    If you’ve ever thought, “We have smart people but we’re just spinning,” this is your lever. The outcomes won’t just be incremental—they’ll surprise you.

    Let your team be more than the sum of résumés. Let it be a force.

    Your journey in team architecture starts now. Make the first move intentional, bold—and rooted in people, not just process.

    —

    References:

    Collins, Jim. Good to Great: Why Some Companies Make the Leap…And Others Don’t. HarperBusiness, 2001. (Provides the foundational concept of “getting the right people on the bus, the wrong people off the bus, and the right people in the right seats,” emphasizing the importance of disciplined people decisions).

    Duhigg, Charles. “What Google Learned From Its Quest to Build the Perfect Team.” The New York Times Magazine, February 25, 2016. (This widely cited article details Google’s Project Aristotle research, which identified psychological safety as the single most important factor for team effectiveness).

    Lencioni, Patrick M. The Five Dysfunctions of a Team: A Leadership Fable. Jossey-Bass, 2002. (A highly influential book that illustrates common team dysfunctions – absence of trust, fear of conflict, lack of commitment, avoidance of accountability, and inattention to results – all of which are rooted in interpersonal dynamics).

    Hackman, J. Richard. Leading Teams: Setting the Stage for Great Performances. Harvard Business School Press, 2002. (A cornerstone academic text in team effectiveness, highlighting the critical role of team design, clear goals, and supportive organizational contexts in fostering high-performing teams).

    Goleman, Daniel. Emotional Intelligence. Bantam Books, 1995. (Highlights the importance of interpersonal skills and emotional intelligence in leadership and team dynamics, reinforcing the need to assess EQ in team building).

    Eric Ford

  • Selecting the Right KPIs for Your Organization’s Success

    Selecting the Right KPIs for Your Organization’s Success

    Moving from Data Overload to Strategic Clarity

    We live in a world obsessed with data. Dashboards light up with numbers. Reports overflow with charts. Every metric seems to demand your attention.

    And yet—many organizations still can’t say with confidence whether they’re actually winning.

    The issue isn’t the lack of data. It’s the lack of direction.

    KPIs—Key Performance Indicators—only create value when they illuminate progress toward what truly matters. Without strategic intent behind them, they’re just noise with a spreadsheet attached.

    Choosing the right KPIs isn’t about measuring everything. It’s about measuring the right things—the few signals that cut through distraction and show whether your organization is moving in the direction you said it would.

    Beyond “What Gets Measured Gets Managed”: The Deeper Truth

    The adage “What gets measured gets managed” is powerful, but it’s often incomplete. The deeper truth is: “What gets measured strategically, gets managed effectively.” Without a strategic lens, you risk managing noise, pursuing “vanity metrics” that look good on paper but offer no real insight, or worse, driving behaviors that actively undermine your long-term success. The journey to better KPIs is less about a single destination and more about a continuous loop of learning, adaptation, and strategic alignment.

    Step 1: Start with “Why” — Let Strategy Lead

    Before you pick a single metric, step back and ask: What’s our purpose right now?

    What are we really trying to achieve in the next year or two? Are we trying to grow market share? Improve retention? Strengthen culture? Reduce friction?

    KPIs should follow strategy, not the other way around. If you start with the numbers, you’ll end up managing noise. But if you start with purpose, your metrics become a compass—pointing everyone toward a shared goal.

    When your direction is clear, the right indicators practically reveal themselves. When it’s not, every metric feels urgent but none are truly important.

    Step 2: Translate Intent into Measurable Outcomes

    Once the strategic “why” is clear, define how success will look in tangible terms.

    If your objective is to strengthen customer loyalty, what would proof of that look like? Higher repeat purchase rates? Stronger Net Promoter Scores?

    If your goal is to improve efficiency, where should you see the impact? Faster fulfillment? Lower error rates? Better utilization?

    The key is to make outcomes visible and measurable—so you can tell, without debate, whether progress is being made.

    The most effective KPIs aren’t random metrics; they’re signals of success, anchored in the outcomes that matter most.

    Step 3: Focus on the Vital Few

    The temptation is to track everything. After all, data feels safe. But the truth is, too many metrics create paralysis, not precision.

    When everything is a priority, nothing really is.

    Instead, choose a handful of indicators that carry the most meaning. Five to seven (5-7) key measures at the organizational level is usually enough more than tends to muddy the waters of what is truly important. Beneath that, each team might own two or three (1-3) that directly connect to those broader goals.

    The discipline is in restraint. Fewer metrics sharpen focus, create clarity, and make wins visible.

    Step 4: Make KPIs Actionable, Not Just Interesting

    A KPI should drive decisions. When it moves up or down, you should immediately know what that means and what to do about it.

    If a metric doesn’t inspire action, it’s not a KPI—it’s trivia.

    Good KPIs are specific, measurable, realistic, and time-bound. But most importantly, they’re relevant. They’re directly tied to what you’re trying to achieve and easily understood by the people doing the work.

    Measurement without action is motion without progress.

    Ensuring Your KPIs are SMART and Actionable

    Beyond S.M.A.R.T. ensure your KPIs are actionable. A good KPI should provide insights that lead to specific actions. If you see a dip or spike, it should tell you what needs to be done. If a KPI is declining, does it immediately suggest a potential intervention or area for investigation? If not, it might be an interesting metric, but perhaps not a powerful KPI.

    Step 5: Ownership and Communication Are Everything

    A KPI without a clear owner quickly becomes an orphan. Every key metric should have someone accountable—not just for tracking it, but for understanding it, questioning it, and driving improvement.

    Just as critical is communication. Everyone in the organization should know:

    • What we’re measuring
    • Why it matters
    • How it connects to the work they do

    Clarity here creates engagement. People care more when they see how their effort moves the needle.

    Step 6: Keep It Alive — Review, Learn, Evolve

    The right KPIs today might not be the right ones a year from now. Markets shift. Strategies mature. Priorities evolve.

    Make KPI review a rhythm, not a reaction. Check regularly: Are we still measuring what matters? Are these numbers still tied to our mission?

    Don’t hesitate to drop a metric that no longer tells you something useful. Agility in measurement keeps your strategy fresh and your teams focused on the work that truly drives impact.

    The Payoff

    When KPI selection is done with intention, the benefits ripple across the organization.

    People gain clarity on what success looks like. Teams make faster, more confident decisions. Energy flows toward what matters instead of scattering across distractions.

    You move from reporting activity to managing results.

    And that shift—from measurement to meaning—is what separates busy organizations from effective ones.

    Because at the end of the day, the goal isn’t to measure more. It’s to measure what moves you forward.

    References

    These sources are great if you want to dive deeper into this topic.

    Collins, Jim. Good to Great: Why Some Companies Make the Leap…And Others Don’t. HarperBusiness, 2001. (This book’s emphasis on disciplined thought, the Hedgehog Concept, and focusing on what you can be “best in the world at” implicitly underpins the “Vital Few” and strategic alignment principles of effective KPI selection).

    Drucker, Peter F. “The Practice of Management.” Harper & Row, 1954. (Widely attributed with the concept “What gets measured gets managed,” though the exact phrasing and context have evolved over time).

    Parmenter, David. Key Performance Indicators: Developing, Implementing, and Using Winning KPIs. 3rd ed., Wiley, 2020. (A comprehensive resource on KPI best practices, reinforcing concepts like the “vital few” and strategic alignment).

    Doran, George T. “There’s a S.M.A.R.T. way to write management’s goals and objectives.” Management Review, vol. 70, no. 11, 1981, pp. 35-36. (This article introduced the SMART criteria for goal setting, which is directly applicable to KPI definition).

    Eric Ford

  • AI’s Missing Piece: Organizational Change, the Key to Real Value

    AI’s Missing Piece: Organizational Change, the Key to Real Value

    Organizational change is crucial to any business transformation. However, Artificial intelligence (AI) is no longer a futuristic fantasy; it’s rapidly becoming a core component of business strategy across industries. Companies are investing heavily in AI technologies, from automation and predictive analytics to personalized customer experiences. However, simply implementing cutting-edge AI tools doesn’t guarantee success. In fact, without a critical and often overlooked element – organizational change management – many AI initiatives are destined to fall short of their potential, failing to achieve proper adoption, true enablement, and ultimately, a full return on investment.

    Think of it this way: introducing AI into an organization is like transplanting a sophisticated new engine into a car. While the engine itself might be powerful and efficient, if the car’s chassis, transmission, and the driver aren’t prepared for it, the new engine won’t deliver its promised performance. The entire system needs to adapt and be ready to harness the new power.

    Digital Transformation Requires More Than Just Digits

    Even in broader digital transformation efforts, the importance of organizational change cannot be overstated. Introducing new software, cloud infrastructure, or digital workflows impacts how people work, collaborate, and make decisions. Without a structured approach to manage these changes, companies often face resistance, low adoption rates, and ultimately, a failure to realize the intended benefits of their digital investments.

    AI Transformation Amplifies the Need for Change

    The need for robust organizational change management becomes even more critical with AI transformations. Here’s why:

    Fundamental Shifts in Workflows: AI often automates tasks previously performed by humans, requiring significant shifts in job roles and responsibilities. Employees may need to learn new skills, collaborate with AI systems, and focus on higher-value activities. Without proper guidance and training, this can lead to anxiety, resistance, and underutilization of AI capabilities.

    New Ways of Thinking and Decision-Making: AI can provide insights and recommendations that challenge traditional ways of thinking. Employees and leaders need to develop the ability to interpret AI outputs, understand its limitations, and integrate AI-driven insights into their decision-making processes. This requires a shift in mindset and a willingness to trust and collaborate with intelligent systems.

    Data-Driven Culture: Successful AI relies heavily on data. Organizations need to cultivate a data-driven culture where data is valued, understood, and used effectively across all levels. This involves establishing clear data governance policies, ensuring data quality, and empowering employees with the skills to interpret and leverage data insights.

    Ethical Considerations and Trust: AI implementation raises important ethical considerations regarding bias, transparency, and accountability. Organizations need to proactively address these concerns, build trust in AI systems, and establish clear guidelines for their responsible use. This requires open communication, education, and the involvement of stakeholders across the organization.

    The “Black Box” Challenge: Some AI algorithms can be complex and difficult to understand, leading to a “black box” perception. Building trust and encouraging adoption requires demystifying AI, explaining its logic in accessible terms, and demonstrating its value and reliability. Organizational change efforts can facilitate this understanding and build confidence.

    The Path to Successful AI: Integrating Organizational Change

    To truly unlock the value of their AI investments, organizations must integrate organizational change management into every stage of their AI journey. This involves:

    Clear Vision and Communication: Articulating a clear vision for how AI will benefit the organization and its employees is crucial. Open and transparent communication throughout the process helps to address concerns, build excitement, and foster buy-in.

    Stakeholder Engagement: Involving employees from all levels and relevant departments in the AI planning and implementation process is essential. Understanding their perspectives, addressing their concerns, and incorporating their feedback increases the likelihood of successful adoption.

    Comprehensive Training and Enablement: Providing targeted training programs that equip employees with the new skills and knowledge required to work effectively with AI systems is paramount. This includes technical skills, understanding AI outputs, and adapting workflows.

    Iterative Implementation and Feedback Loops: AI implementation should be an iterative process with continuous monitoring and feedback. Gathering input from users and making adjustments based on their experiences ensures that the AI solutions are meeting their needs and being adopted effectively.

    Leadership Buy-in and Championing: Strong leadership support is critical for driving organizational change. Leaders must champion the AI initiatives, communicate their importance, and actively participate in the transformation process.

    Measuring and Celebrating Successes: Tracking key metrics related to AI adoption, enablement, and business impact is essential for demonstrating the value of the investment and reinforcing positive change. Celebrating early successes can build momentum and encourage further adoption.

    Overcoming Fears of Job Displacement: A significant hurdle in AI adoption is the natural fear among employees that these intelligent systems will lead to job destruction and elimination. Recent announcements from companies like Klarna, declaring an “AI-first” strategy with potential impacts on customer service roles, and Duolingo’s integration of AI tutors, while showcasing innovation, can understandably trigger anxiety within their workforces and across the broader job market. It is crucial for organizations to proactively address these fears by clearly articulating how AI will augment human capabilities rather than simply replace them. Emphasize the creation of new roles that require uniquely human skills like creativity, critical thinking, and complex problem-solving, which AI can support but not fully replicate. Transparent communication about the evolving roles, coupled with robust reskilling and upskilling initiatives, is vital to alleviate anxiety and foster a collaborative mindset towards AI. Highlighting how AI can automate mundane tasks, freeing up employees for more engaging and strategic work, can also help shift the narrative from job elimination to job evolution.

    Wrapping this up

    AI holds immense potential to transform businesses, but technology alone is not the magic bullet. Successful AI implementation hinges on the organization’s ability to adapt, evolve, and embrace new ways of working. By prioritizing organizational change management, companies can ensure proper adoption, empower their employees, and ultimately, fully realize the transformative power and significant return on investment that AI promises. Ignoring this crucial element is a recipe for underutilized technology and missed opportunities in the age of intelligent automation.

    Eric Ford

  • Eliminating Inefficiencies: Structural Friction in the Workplace

    Eliminating Inefficiencies: Structural Friction in the Workplace

    Alright, let’s dig into one of the sneakiest kinds of workplace headaches: structural friction. Think of it like the underlying design flaws in a building that make everything just a little bit harder than it needs to be. It’s not about personalities clashing or a bad day; it’s baked into how the whole darn thing is set up.

    As someone who geeks out on how workplaces actually work (not just how they’re supposed to), I see structural friction pop up in all sorts of ways. It’s the kind of stuff that makes you think, “Why on earth do we do it this way?” and the answer is often, “Because that’s how it’s always been,” or worse, “Nobody really knows anymore.”

    So, what exactly are we talking about when we say “structural friction”? It’s the friction that comes from the very bones of the organization – its hierarchy, its processes, its systems, even its physical layout. It’s the stuff that slows everyone down, even the most motivated and talented people.

    Let’s break down some common culprits:

    The Silo Shuffle: You know this one. Different departments or teams operate in their own little worlds, barely talking to each other. Information gets hoarded, goals aren’t aligned, and it feels like you’re constantly trying to get someone in another team to just do their part. It’s like trying to build a house where the plumbers refuse to speak to the electricians.  

    The Bureaucracy Maze: Oh boy, this is a classic. Layers upon layers of approvals, endless forms, and rules that seem to exist for their own sake. You need permission to get permission to ask a question. It’s the kind of environment where getting a simple thing done feels like navigating a labyrinth. Innovation? Forget about it – who has the energy to wade through all that red tape?

    The Information Black Hole: This is where crucial information is either impossible to find, scattered across a million different platforms, or just plain doesn’t exist when you need it. You spend half your day hunting down that one document or trying to figure out who knows the answer to a basic question. It’s like trying to cook a meal when all the ingredients are hidden in different cupboards with no labels.

    The Tool Tango: You’ve got a dozen different software programs that don’t talk to each other, clunky legacy systems that crash at the worst possible moment, or tools that are so complicated they require a PhD to operate. Instead of making things easier, the technology itself becomes a source of constant frustration and wasted time. It’s like trying to build something with the wrong set of tools.

    The Unclear Ladder: When it’s not obvious how you grow in the company, what skills are valued, or what the career paths even look like, it creates friction. People feel stuck, unmotivated, and might start looking elsewhere. It’s like driving without a map – you’re not sure where you’re going or how to get there.

    What can We do?

    So, how do you go about smoothing out this deeply ingrained structural friction? It’s not a quick fix, and it often requires a willingness to shake things up a bit. Here are some ideas:

    Break Down the Silos: Encourage cross-functional collaboration through joint projects, shared goals, and regular inter-team communication. Create opportunities for people from different departments to actually talk and understand each other’s work.  

    Simplify the Bureaucracy: Take a long, hard look at your processes. Are all those approvals really necessary? Can forms be digitized? Are there steps that just add time without adding value? Streamlining processes can free up a ton of wasted energy.

    Create a Knowledge Hub: Invest in a centralized system for information sharing that’s easy to navigate and search. Make sure everyone knows where to find what they need. Think of it as creating a well-organized kitchen where all the ingredients are clearly labeled and easy to grab.

    Integrate Your Tech: Aim for a tech stack that works together seamlessly. Invest in training to make sure everyone knows how to use the tools effectively. Sometimes, it might even mean biting the bullet and upgrading outdated systems.  

    Clarify Career Paths: Be transparent about how people can grow within the organization. Outline clear career paths, identify necessary skills, and provide opportunities for development.

    Smoothing out structural friction isn’t just about making things more efficient; it’s about creating a more human-friendly workplace. When people aren’t constantly battling unnecessary obstacles, they’re happier, more engaged, and ultimately more productive. It isn’t easy and takes effort and a willingness to challenge the status quo, but the payoff – a smoother, more effective, and less frustrating work environment – is well worth it.

    Eric Ford

  • The Soul in the Machine: Reclaiming the Human Element in the Age of AI at Work

    The Soul in the Machine: Reclaiming the Human Element in the Age of AI at Work

    Alright, let’s have a real heart-to-heart about this whole AI thing shaking up our work lives. As someone who’s spent years watching how people tick at work, the tech side of AI is cool and all, but what about the human side of it. Because at the end of the day, it’s about us, right? How we feel, how we adapt, and how we keep that human spark alive when the robots start doing some of our old jobs.

    So, picture this: AI strolls into the office, not in a clanky robot suit (yet!), but as software, algorithms, the whole shebang. Suddenly, some of the stuff you used to spend hours on – sorting spreadsheets, answering the same old customer questions, even drafting basic reports – poof! The AI can handle it in a fraction of the time.

    Now, for some folks, this feels like winning the lottery. Imagine being freed from those tasks that make your eyes glaze over. You can finally focus on the stuff you actually enjoy, the creative problem-solving, the chatting with clients and building real connections, the big-picture thinking. It’s like having a super-efficient assistant who takes care of the grunt work so you can shine.

    But let’s be real, for others, this feels… well, a bit scary. You might be thinking, “Wait a minute, that was my job. If the computer can do it, where do I fit in?” That knot of anxiety in your stomach? Totally understandable. It’s a natural human reaction to change, especially when it feels like your livelihood is on the line.

    And that’s where companies really need to step up and show their human side too. Just throwing in the latest AI without a thought for the people it affects is a recipe for a grumpy, resistant workforce. So, what are the smart companies doing to navigate this and keep everyone on board?

    First off, talking, like, really talking. None of that corporate jargon that makes your brain switch off. I’m talking clear, honest conversations about what’s changing, why it’s changing, and, crucially, how it’s going to affect you. Companies need to paint a realistic picture, not just the shiny, futuristic one. They need to say, “Okay, this task will be automated, but that means you’ll have the chance to learn this new skill and work on this more interesting project.” It’s about being straight with people and not hiding the potential downsides.

    Then comes the super important part: teaching and training. If AI is going to change the game, companies have a responsibility to equip their players with new skills. Think of it like leveling up in a game. Your old skills might still be useful, but there are new ones you need to learn to thrive in this AI-powered world. This could be anything from learning how to work with the AI tools, understanding the data it spits out, or even developing entirely new skills that are more human-centric, like emotional intelligence or complex communication. Companies that invest in their people this way aren’t just being nice; they’re being smart. A skilled and adaptable workforce is way more valuable in the long run.

    But it’s not just about the hard skills. It’s also about fostering a culture of collaboration, not competition, with AI. The message needs to be: AI is a tool to help us, not replace us. Think of it like a super-powered calculator for your brain. It can do the heavy lifting, freeing you up to do the creative, strategic stuff that machines just aren’t good at. Companies that encourage their teams to experiment with AI, to give feedback, and to find ways where humans and AI can work together best are the ones that will see real success.

    And let’s not forget the human touch. In a world increasingly driven by algorithms, the uniquely human skills – empathy, creativity, critical thinking, the ability to connect with others on a real level – become even more valuable. Companies should actively nurture these skills, creating opportunities for collaboration, brainstorming, and those water cooler moments where real ideas spark. It’s about reminding everyone that even with all this fancy tech, the human element is still what makes a business truly thrive.

    Leadership plays a massive role in all of this. If the folks at the top are nervous about AI or just see it as a cost-cutting measure, that attitude will trickle down. But leaders who are genuinely excited about the possibilities, who communicate openly and honestly, and who show they care about their employees’ well-being are the ones who will build trust and inspire their teams to embrace the change.

    So, it’s about remembering that this isn’t a one-size-fits-all situation. The impact of AI will be different for different roles and different people. Companies need to be flexible and adaptable in their approach, listening to individual concerns and tailoring their support accordingly.

    Look, AI isn’t going anywhere. It’s going to keep changing the way we work. But if we focus on the human side of this revolution – by communicating openly, investing in our people, fostering collaboration, and valuing those uniquely human skills – we can navigate this change in a way that benefits everyone. It’s not about the soul versus the machine; it’s about finding a way for them to dance together, creating a workplace that’s both efficient and, well, still feels human. And that, to me, is the most important part of all.

    Eric Ford

  • AI’s Journey Through the Gulf of Disillusionment

    AI’s Journey Through the Gulf of Disillusionment

    Artificial Intelligence (AI) and Generative AI have been heralded as transformative technologies with the potential to revolutionize various industries. However, as these technologies have progressed, they have encountered significant challenges that have led them into the Gulf of Disillusionment. Below we will delves into the specific factors and use cases that have contributed to this phase, providing an analysis of the journey.

    The Peak of Inflated Expectations

    Initially, AI and generative AI were met with immense excitement and high expectations. The media was filled with stories of AI diagnosing diseases with unprecedented accuracy and generative AI creating lifelike images and text. Companies invested heavily, expecting rapid and transformative results. However, the reality of implementing these technologies proved to be far more complex.

    Entering the Gulf of Disillusionment

    As the hype began to fade, several factors contributed to AI and generative AI entering the Gulf of Disillusionment:

    1. Technical Limitations: Despite significant advancements, AI and generative AI faced technical challenges that hindered their widespread adoption. For instance, AI models often struggled with generalizing across different tasks, leading to inconsistent performance [GenAI’s Trough Of Disillusionment: Why 2025 Will Mark A Turning Point].
    2. Data Readiness: One of the critical factors contributing to the disillusionment is the lack of data preparedness. AI systems require vast amounts of high-quality data to function effectively. Many organizations underestimated the complexity of data collection, cleaning, and integration. Inadequate data can lead to poor model performance and unreliable outcomes. For example, healthcare AI initiatives often faced challenges due to fragmented and inconsistent patient data, which hindered accurate diagnosis and treatment recommendations [Unlocking AI Potential: Why Your Company’s Data is the Key to Success].
    3. Ethical and Societal Concerns: Issues such as data privacy, algorithmic bias, and the ethical use of AI became prominent. High-profile cases, like the controversy surrounding facial recognition technology and its potential for misuse, highlighted these concern [Generative AI is sliding into the ‘trough of disillusionment’].
    4. Economic and Regulatory Hurdles: The cost of developing and maintaining AI systems, coupled with regulatory challenges, slowed down adoption. Companies found it difficult to justify the investment without clear and immediate returns [Generative AI and the Trough of Disillusionment].

    Specific Use Cases

    Several specific use cases illustrate how AI and generative AI entered the Gulf of Disillusionment:

    1. Healthcare AI: AI-driven healthcare initiatives, such as IBM’s Watson for Oncology, faced significant setbacks. Despite initial promises, Watson struggled to provide accurate treatment recommendations, leading to criticism and reduced trust in AI’s capabilities in healthcare [How IBM Watson Overpromised and Underdelivered on AI Health Care – IEEE Spectrum].
    2. Autonomous Vehicles: The development of self-driving cars by companies like Uber and Tesla encountered numerous technical and ethical challenges. High-profile accidents raised concerns about the safety and reliability of autonomous vehicles, leading to increased scrutiny and regulatory hurdles [The evolving safety and policy challenges of self-driving cars].
    3. Generative AI in Content Creation: Generative AI tools, such as OpenAI’s GPT-3, initially impressed with their ability to generate human-like text. However, issues like biased outputs and the potential for misuse in creating deepfake content led to a reevaluation of their impact and ethical implications [Ethical Challenges and Solutions of Generative AI: An Interdisciplinary Perspective].

    Lessons Learned

    Analyzing these use cases provides valuable insights into the factors contributing to the Gulf of Disillusionment:

    1. Setting Realistic Expectations: Overpromising and underdelivering can lead to disillusionment. It is crucial to set achievable goals and communicate the limitations of AI technologies clearly.
    2. Investing in Robust Data and Infrastructure: Successful AI implementation requires high-quality data and robust infrastructure. Companies must invest in these areas to ensure reliable performance.
    3. Addressing Ethical and Societal Issues: Proactively addressing ethical concerns and societal impacts is essential for building trust and ensuring responsible AI use.

    Moving Forward

    Despite the challenges, there is a path forward for AI and generative AI. By focusing on realistic applications, investing in technological advancements, and addressing ethical concerns, these technologies can move towards the Slope of Enlightenment. Successful implementations and incremental progress will pave the way for AI to realize its full potential.

    The journey of AI and generative AI through the Gulf of Disillusionment highlights the complexities and challenges of adopting transformative technologies. By learning from past experiences and focusing on sustainable progress, we can navigate this phase and unlock the transformative power of AI and generative AI.

    ________________________________________

    I hope this paper provides an insightful high-level analysis of how AI and generative AI entered the Gulf of Disillusionment. If you have any further suggestions or need additional details, feel free to let me know!

    Eric Ford

  • Unlocking AI Potential: Why Your Company’s Data is the Key to Success

    Unlocking AI Potential: Why Your Company’s Data is the Key to Success

    How Data Drives AI Success

    Artificial Intelligence (AI) has transformed the way businesses operate, offering unprecedented opportunities for growth and innovation. However, the success of AI initiatives largely depends on the quality and accessibility of a company’s data. AI also comes in many forms: Generative AI (ChatGPT or Claude), Machine Learning (ML), Deep Learning, and others. No matter what for the AI takes data plays a critical role in its AI success.

    Understanding the Role of Data in AI

    Data is the foundation of AI. Imagine it as the fuel that powers the AI engine. Without good data, AI simply cannot function effectively. Data can be classified into different types, such as structured data (think of neat rows and columns in a spreadsheet), unstructured data (like social media posts, videos, or emails), real-time data (information that’s constantly updated, like stock prices or weather models), and historical data (past records that help predict future trends).

    AI algorithms and models rely on this diverse range of data to learn, make predictions, and generate insights. For instance, a recommendation system on a shopping website uses data about your previous purchases, time of year, social connections (when available), and browsing history to suggest items you might like. This process involves complex computations, but at its core, it’s all about analyzing data to make intelligent decisions.

    It’s important to understand that while AI is incredibly powerful, it isn’t magic. Its capabilities are directly tied to the data it can access. The richer and more relevant the data, the better the AI performs. This means companies need to invest in collecting and maintaining high-quality data to truly harness the potential of AI.

    Quality Over Quantity: The Importance of Data Quality

    While having a large volume of data might seem beneficial, the quality of that data is even more crucial. Imagine trying to make a decision based on flawed or incomplete information – the outcome likely won’t be positive. This is why data quality is vital for AI.

    Data quality is defined by several dimensions, including accuracy (correctness of the data), completeness (having all necessary data points), and consistency (uniformity across datasets). For example, if an e-commerce site has outdated prices or incorrect product information, its AI-driven recommendation system will likely suggest irrelevant or incorrect products to customers.

    Ensuring high-quality data involves processes like data cleaning (removing errors and inconsistencies), validation (checking the accuracy of data), and governance (establishing policies for data management). These steps help to create reliable datasets that AI can use to produce meaningful insights.

    Companies often face challenges in maintaining data quality, but the effort is worth it. High-quality data not only enhances AI performance but also builds trust with customers and stakeholders. When people know that a company’s AI systems are based on accurate data, they are more likely to rely on the recommendations and decisions those systems provide.

    Data Integration and Accessibility

    Integrating data from various sources is essential for comprehensive AI analysis. However, this process can be likened to solving a jigsaw puzzle – each piece (or data source) needs to fit perfectly to complete the picture.

    Challenges such as data silos (where data is isolated within different departments) and compatibility issues (differences in data formats) can hinder integration efforts. Think of trying to combine pieces from different puzzles – it’s not going to work unless they’re designed to fit together.

    Solutions like ETL (Extract, Transform, Load) processes, data lakes (centralized repositories for storing large datasets), data warehouses (systems used for reporting and data analysis), APIs (application programming interfaces that allow data to be shared between systems), and platforms like Microsoft Fabric can facilitate seamless data integration. These tools help to break down silos and standardize data, making it accessible for AI analysis.

    When data is integrated and accessible, AI can analyze it more effectively, leading to better insights and decisions. For instance, a healthcare system that integrates patient records, lab results, treatment histories, and population statistics can use AI to predict health outcomes and suggest personalized treatments.

    Leveraging Data for AI Insights

    AI analyzes data to generate valuable insights that can drive business decisions. Imagine AI as a detective, meticulously piecing together clues from various data points to solve a mystery or uncover hidden patterns. Furthermore, AI’s ability to analyze extensive datasets quickly allows companies to react to market changes in a timely manner, staying ahead of the competition.

    Examples of AI applications powered by data include predictive analytics (forecasting future trends based on past data), customer segmentation (grouping customers based on their behaviors and preferences), anomaly detection (spotting unusual patterns that may indicate fraud or errors), and autonomous agents (systems that can perform tasks independently based on data-driven insights). These applications are like having a crystal ball that can foresee trends and issues before they happen and in the case of autonomous agents even act on the identified insights.

    Case studies of companies successfully leveraging data for AI demonstrate its transformative potential. For instance, retailers use AI to analyze shopping habits and optimize inventory management. By understanding which products are popular and predicting future demand, they can ensure they always have the right stock levels, improving customer satisfaction and reducing costs.

    In the manufacturing sector, AI is used to enhance production efficiency and reduce downtime. Predictive maintenance powered by AI analyzes sensor data from machinery to anticipate failures before they happen. By addressing issues proactively, manufacturers can avoid costly breakdowns, extend the lifespan of equipment, and maintain uninterrupted production schedules.

    AI’s ability to generate insights from data is incredibly powerful, but it requires a solid foundation of high-quality and well-integrated data. Companies that leverage this technology can gain a competitive edge, making smarter decisions that drive growth and innovation.

    Data Privacy and Security

    Data privacy and security are paramount in AI initiatives. Imagine sharing your personal information with a company – you’d want to be sure it’s protected and used responsibly. Companies must comply with regulatory requirements such as GDPR (General Data Protection Regulation), CCPA (California Consumer Privacy Act) and HIPAA/HITECH (Health Insurance Portability and Accountability / Health Information Technology for Economic and Clinical Health) to protect sensitive information.

    Best practices for data protection include encryption (scrambling data so it can’t be read without a key), access controls (restricting who can view or modify data), anonymization (removing personally identifiable information), Data Loss Prevention (DLP) (strategies to prevent data leaks and unauthorized access), and data categorization (organizing data based on sensitivity and importance). These measures are like locking your data in a safe and ensuring only trusted individuals have the key.

    Ensuring data privacy and security is not just about compliance; it’s also about building trust. When customers know their data is protected, they’re more likely to share information and engage with AI-driven services. This trust is crucial for the success of AI initiatives especially when dealing with public and customer data.

    It is imperative for companies to remain vigilant regarding data privacy and security, continually updating their practices to address emerging threats and comply with new regulations. By adopting such measures, they can safeguard their data, uphold customer trust, and ensure the long-term success of their AI initiatives. Neglecting these responsibilities may result in fines, penalties, or even felony charges.

    Building a Data-Driven Culture

    Fostering a data-driven culture within an organization is key to maximizing the benefits of AI. Imagine a company where everyone, from top executives to junior staff, understands the value of data and uses it to make informed decisions.

    Encouraging data literacy across all levels involves providing tools and training that empower employees to use data effectively. For instance, workshops and online courses can teach staff how to interpret data and apply it to their work. This is similar to teaching someone how to read a map – it helps them navigate their tasks with greater confidence and accuracy.

    Leadership plays a crucial role in promoting a data-driven mindset. When leaders champion the use of data and demonstrate its value through their decisions, it sets a positive example for the rest of the organization. Imagine a CEO who regularly references data in meetings and decision-making processes – it signals to everyone that data is important and should be utilized.

    Building a data-driven culture is an ongoing process that requires continuous commitment and collaboration. By fostering this culture, companies can ensure that their AI initiatives are supported by a strong foundation of data-driven decision-making, leading to better outcomes and continuous improvement.

    Future Trends: Data and AI

    The relationship between data and AI continues to evolve with emerging trends such as big data, IoT (Internet of Things), IIOT (Industrial Internet of Things), Industry 4.0, and edge computing. Think of these technology trends as the next wave of technological advancements that will shape the future of AI.

    Big data refers to the massive volumes of data generated by modern technologies. While this data holds immense potential, managing and analyzing it requires advanced tools and techniques. Companies need to be prepared to handle big data to extract valuable insights and drive AI success.

    IoT involves connecting everyday devices to the internet, allowing them to collect and share data. Imagine a smart home where appliances communicate with each other to optimize energy use – this is just one example of how IoT can generate data for AI analysis. The proliferation of IoT devices will create new opportunities for AI applications, but it also presents challenges in managing and securing this data.

    IIOT, or Industrial Internet of Things, extends the concept of IoT to the industrial sector. It involves connecting machines, sensors, and devices in industries such as manufacturing, transportation, and energy to gather and analyze data. Picture a factory where machinery communicates to optimize production efficiency and predict maintenance needs – IIOT enables such advancements. This trend offers significant potential for AI, but also demands robust data management and cybersecurity measures.

    Industry 4.0 represents the fourth industrial revolution, characterized by the integration of digital technologies into manufacturing processes. This encompasses automation, data exchange, and the use of cyber-physical systems. Imagine a smart factory where machines are interconnected and capable of autonomously optimizing production – Industry 4.0 transforms traditional manufacturing into a highly efficient and intelligent operation. The synergy between AI and Industry 4.0 promises profound advancements but requires careful management of data and security protocols.

    Edge computing refers to processing data closer to where it’s generated, rather than relying on centralized servers. This approach can improve the speed and efficiency of AI analysis, especially for real-time applications. For instance, autonomous vehicles use edge computing to quickly analyze data from sensors and make split-second decisions.

    Companies must prepare for future data challenges and opportunities to stay ahead in the competitive landscape. By embracing these trends and investing in the necessary infrastructure, they can ensure their AI initiatives remain cutting-edge and impactful.

    Wrapping Up

    Data is crucial for the effectiveness of AI initiatives. Companies should focus on their data strategies to fully harness AI capabilities and promote innovation. By recognizing the significance of data, maintaining its quality, integrating it efficiently, utilizing it for insights, ensuring privacy protection, fostering a data-oriented culture, and keeping up with future trends, businesses can enhance their success with AI.

    The journey to harnessing AI’s potential is not without its challenges, but with the right approach to data management, companies can overcome many of these hurdles and proceed on their journey to thrive in the digital age. Investing in data is investing in the future, and those who do so will lead the way in AI-driven transformation.

    Eric Ford

  • Overcoming the Lack of Technical Expertise in Adopting AI

    Overcoming the Lack of Technical Expertise in Adopting AI

    Adopting Artificial Intelligence (AI) can be a game changer for Small and Medium-sized Businesses (SMBs). The potential benefits span from increased operational efficiency to enhanced customer experiences and innovative product offerings. However, one of the significant hurdles SMBs face is the lack of in-house technical expertise required to harness AI effectively. This article seeks to address various strategies SMBs can employ to overcome this challenge and successfully integrate AI into their operations.

    Understanding the Challenges

    AI technologies offer immense potential but also come with a steep learning curve. The complexity involved in developing, deploying, and maintaining AI systems can be daunting for businesses that lack specialized knowledge and skills. Additionally, hiring or training staff to manage AI systems can be a considerable financial and logistical challenge. To navigate these obstacles, SMBs need to adopt a strategic approach that aligns with their resources and business goals.

    Retain an AI Guide

    Having an AI guide or advisor can help you navigate the best options for your company based on your specific needs. The guide will assist you in selecting from various options and techniques listed below, as well as any additional specific needs for your business. This expert can also provide insights into emerging AI trends and help you stay ahead of the competition. Additionally, they can offer training sessions for your team to ensure smooth implementation and operation of AI systems. AI Guides are normally engaged in a fractional capacity which in turn helps control expenses.

    Outsourcing AI Expertise

    One of the most effective ways for SMBs to overcome the lack of technical expertise is to outsource AI capabilities. Partnering with AI consultants or firms that specialize in AI can provide access to cutting-edge technologies and expert knowledge without the need for substantial internal investments. These external partners can help businesses identify the most suitable AI solutions, implement them, and provide ongoing support.

    Some Benefits of Outsourcing

    • Cost Efficiency: Outsourcing minimizes the need for expensive hiring and training processes.
    • Access to Expertise: Partnering with AI firms provides access to seasoned professionals with extensive experience.
    • Focus on Core Business: SMBs can concentrate on their core competencies while AI experts handle the technical aspects.

    Leveraging AI Platforms and Tools

    AI platforms and tools have become increasingly accessible and user-friendly, making it easier for SMBs to integrate AI into their operations. Many of these platforms offer pre-built models, intuitive interfaces, and comprehensive documentation that simplify the adoption process. By leveraging these tools, businesses can bypass the need for deep technical expertise and quickly deploy AI solutions.

    Some Top AI Platforms

    • Google Cloud AI: Offers a range of AI and machine learning services that are easy to integrate and scale.
    • Microsoft Azure AI: Provides powerful AI capabilities with extensive support and resources. Microsoft also provides its Copilot solutions that many general access to AI feature approachable for any company.
    • IBM Watson: Known for its advanced analytics and AI solutions tailored for various industries.
    • Amazon Web Services (AWS) AI: Offers a comprehensive suite of AI and machine learning tools that are scalable, robust, and widely used across various sectors.

    Training and Upskilling Current Employees

    While outsourcing and leveraging AI platforms can meet immediate needs, it is equally important for SMBs to invest in the long-term development of their workforce. Training and upskilling existing employees allow businesses to cultivate internal expertise and adapt to evolving AI technologies. Companies should establish a clear roadmap outlining their current status and future goals, both as a business and in relation to AI utilization. This strategic planning will enable them to create an effective upskilling plan, ensuring that limited resources are allocated more efficiently.

    Steps to Upskill Employees

    • Identify Skill Gaps: Assess the current skill levels and identify areas that require improvement.
    • Provide Training Programs: Enroll employees in AI-focused courses and workshops.
    • Encourage Continuous Learning: Foster a culture of continuous learning through access to online resources and certifications.

    Collaborating with Academic Institutions

    Partnering with academic institutions can offer SMBs specialized knowledge and resources that are invaluable in advancing their AI initiatives. By engaging with universities and research centers, businesses can access the latest research, tap into a pool of talented graduates, and collaborate on projects that push the boundaries of AI innovation.
    Furthermore, academic partnerships can provide unique insights into emerging trends and technologies, helping businesses stay ahead of the curve. Such collaborations can also foster a culture of continuous learning and innovation within the organization.

    Advantages of Academic Collaboration

    • Access to Research: Gain insights from the latest academic research in AI.
    • Talent Pipeline: Connect with students and graduates with relevant expertise.
    • Joint Projects: Develop AI solutions through collaborative projects with academic partners.

    Investing in AI Education and Awareness

    Lastly, SMBs must prioritize educating themselves and their teams about AI. Comprehending the fundamentals of AI, its potential applications, and its constraints can empower businesses to make informed decisions and identify opportunities for AI integration. Additionally, consistent investment in AI literacy will ensure that the organization remains adaptive to future technological advancements.

    Educational Resources

    • Online Courses: Platforms like Coursera, Udacity, and edX offer comprehensive AI courses.
    • Industry Conferences: Attend AI conferences and seminars to stay updated on trends and network with experts.
    • Books and Publications: Read books and journals on AI to deepen knowledge and understanding.

    Looking Forward

    While the lack of technical expertise can be a significant barrier for SMBs in adopting AI, there are multiple strategies to overcome this challenge. By engaging an AI Guide, outsourcing AI expertise, leveraging accessible AI platforms, training and upskilling employees, collaborating with academic institutions, and investing in AI education and awareness, SMBs can navigate the complexities of AI technologies. These approaches will enable them to harness the power of AI, drive innovation, and maintain a competitive edge in their respective industries.

    Eric Ford