maddaisy – cultivating innovation

Tag: data-analytics

  • CX Metrics Are Broken and Enterprises Know It. The Question Is What Comes Next.

    One in three customer experience professionals cannot connect their organisation’s Net Promoter Score to financial performance. That is not the conclusion of a contrarian think piece – it is a finding from Medallia’s 2026 State of Customer Experience Report, which surveyed more than 1,500 consumers and 550 CX practitioners globally. The metric that has anchored boardroom CX conversations for the better part of two decades is, for a significant share of the profession, unmeasurably disconnected from the outcomes it is supposed to predict.

    And the profession knows it. According to the same report, 78 per cent of CX professionals plan to adopt at least one new metric in 2026.

    The perception gap that should worry everyone

    The most striking number in the Medallia research is not about NPS at all. It is the gap between how enterprises think they are performing and how their customers actually feel. Two-thirds of CX professionals believe their brand’s experience has improved over the past year. Only 16 per cent of consumers agree.

    That is not a minor calibration error. It is a 50-percentage-point disconnect between corporate self-assessment and market reality. And it has consequences: 40 per cent of consumers in the study reported switching brands within three months, often without the kind of dramatic service failure that would register on traditional satisfaction surveys. They simply drifted, quietly, toward something better – or at least less friction-filled.

    The implication is uncomfortable. Many enterprises are not just using the wrong metrics. They are using metrics that actively mislead them into believing things are going well when customers are already leaving.

    Too many metrics, too little insight

    The problem is not a shortage of measurement. If anything, it is the opposite. Research published in MIT Sloan Management Review found that large organisations routinely track between 50 and 200 CX metrics across multiple channels and touchpoints. Some track even more. The result is not better decision-making but what one report from Usan’s 2026 CX findings calls “data paralysis” – mountains of signals with no clear path from insight to action.

    MIT Sloan’s researchers, working with 14 subscription-services companies, found that most organisations collect metrics because they are industry-standard, not because they have been validated as useful for their specific customer journey. The recommendation is counterintuitive for an era obsessed with data: collect fewer metrics, but align the ones you keep to specific stages of the customer lifecycle where they can actually inform decisions.

    Medallia’s own data supports this. Among CX professionals using 10 or more data sources, 92 per cent said they could demonstrate return on investment. Among those using five or fewer, the figure dropped to 73 per cent. More data sources help – but only when they are deliberately chosen and connected, not accumulated by default.

    The structural problem underneath

    Even when CX teams produce meaningful insights, the organisational infrastructure often fails to act on them. Medallia’s survey found that 41 per cent of CX professionals worry their teams are too siloed from the rest of the business. One-third said entire departments take no action on CX findings. Half said their organisation does not respond often enough or with enough impact.

    Budget control compounds the issue. Fifty-eight per cent of CX practitioners said their initiatives are funded from budgets they do not directly oversee – a structural dependency that limits their ability to move quickly or invest in new approaches.

    This echoes a pattern maddaisy.com has tracked across several domains. When this site examined how enterprises are designing digital experiences for both humans and AI agents, the core finding was similar: organisations recognise that the landscape has shifted, but their internal structures have not caught up. The CX measurement problem is another instance of the same gap – not a lack of awareness, but a failure of organisational plumbing.

    Where the replacement metrics are coming from

    The 78 per cent of CX professionals looking for new metrics are not starting from scratch. A CX Network analysis of customer listening trends suggests three directions gaining traction.

    First, Customer Effort Score – a measure of how easy or difficult a specific interaction was – is increasingly favoured over NPS for transactional touchpoints, because it maps directly to a friction point rather than measuring a generalised sentiment. Second, frontline employee input is being formalised as a data source, recognising that the people handling customer interactions daily often have a more accurate picture of experience quality than any survey. Third, AI-powered analysis of unstructured data – support transcripts, social mentions, behavioural signals – is enabling what practitioners call “continuous listening”, replacing periodic surveys with ongoing, passive measurement.

    None of these are new ideas. What is new is the urgency. Transcom’s 2026 CX paradoxes report makes a pointed observation: many organisations are layering AI-driven CX tools on top of broken measurement foundations. Automating a process that was never validated does not fix it. It scales the dysfunction.

    What practitioners should be watching

    The shift away from legacy CX metrics is not a technology problem waiting for a technology solution. It is a governance problem. Organisations that want their CX measurement to mean something will need to do three things that are simple to describe and difficult to execute.

    They will need to audit their existing metrics ruthlessly – not asking “is this metric popular?” but “does this metric predict a financial outcome we care about?” They will need to connect whatever survives that audit to specific journey stages, so that insights map to decisions rather than floating in quarterly reports. And they will need to give CX teams the budget authority and cross-functional access to act on what they find, rather than producing dashboards that nobody is structurally empowered to respond to.

    The 50-point perception gap in Medallia’s data is not a measurement artefact. It is a mirror. Enterprises that cannot close it will keep telling themselves that customer experience is improving, right up until the churn numbers say otherwise.

  • PwC Built an AI That Can Actually Read Enterprise Spreadsheets. Here Is Why That Matters.

    Most enterprise AI demonstrations involve chatbots, code generation, or image synthesis — capabilities that are impressive but often disconnected from the workflows where organisations actually make decisions. PwC has taken a different approach. On 19 February, the firm announced a frontier AI agent that can reliably reason across complex, multi-sheet enterprise spreadsheets — the kind of messy, formula-dense workbooks that underpin deals, risk assessments, and financial modelling across virtually every large organisation.

    The announcement would be easy to dismiss as incremental. It is, in fact, one of the more practically significant AI developments of the year so far.

    The Spreadsheet Problem No One Talks About

    AI has made rapid progress with text, images, and code. But enterprise spreadsheets have remained stubbornly resistant. The reason is structural: a typical enterprise workbook is not a neatly formatted data table. It is a sprawling, multi-sheet artefact containing hundreds of thousands of rows, cross-sheet formulas, hidden dependencies, embedded charts, and formatting inconsistencies accumulated over years of manual editing by multiple authors.

    Conventional AI systems — including the most advanced large language models — struggle with this complexity. They can process a clean CSV file or answer questions about a simple table. But ask them to trace a formula chain across five sheets in a workbook with 200,000 rows and inconsistent column headers, and accuracy collapses. For regulated industries where precision is non-negotiable — auditing, tax, financial due diligence — this limitation has kept spreadsheet analysis firmly in the domain of human practitioners.

    PwC’s agent addresses this directly. Combining multimodal pattern recognition with a retrieval-augmented architecture, the system can process up to 30 workbooks containing nearly four million cells. In internal benchmarks, it achieved roughly three times the accuracy of previously published methods while using 50% fewer computational tokens — a meaningful efficiency gain that reduces both cost and energy consumption.

    How It Works, Without the Hype

    The technical approach mirrors how experienced analysts actually work. Rather than attempting to ingest an entire workbook at once — a strategy that overwhelms even million-token context windows — the agent scans, indexes, and selectively retrieves relevant sections. It can jump across tabs, trace logic through formula chains, integrate visual elements like charts, and explain its reasoning with what PwC describes as “defensible precision.”

    Two internal use cases illustrate the practical impact. In engagement documentation, PwC teams work with large, nominally standardised workbooks that document business processes and controls. In practice, these files vary significantly — column names shift, fields appear in different orders, structures change between engagements. The agent handles this in two stages: first mapping the workbook’s structure, then extracting specific details using targeted retrieval rather than brute-force ingestion.

    In risk assessment, the agent replaces what was previously weeks of custom development work. Each new set of files could break existing programmatic approaches due to formatting variations. The agent indexes and extracts directly, regardless of these inconsistencies. PwC reports that what previously required weeks of configuration can now be completed in hours.

    The ROI Connection

    The timing of this announcement is worth noting. Earlier this month, maddaisy examined PwC’s own 2026 Global CEO Survey, which found that 56% of chief executives could not point to measurable revenue gains from their AI investments. Only 12% reported achieving both revenue growth and cost reduction from AI programmes.

    The spreadsheet agent is, in a sense, PwC’s answer to its own data. Rather than pursuing the kind of ambitious, organisation-wide AI transformation that the survey suggests most companies are failing at, this tool targets a specific, bounded problem: making AI useful where decisions actually get made. Spreadsheets are unglamorous, but they remain the substrate of enterprise decision-making across every industry. If AI cannot work reliably with them, the ROI gap that PwC’s own research documented will persist.

    Matt Wood, PwC’s Commercial Technology and Innovation Officer, was notably direct about the origin: “This didn’t start as a research project. It started because our teams were spending weeks manually tracing logic through workbooks that no existing tool could handle.”

    A Broader Pattern: Consulting Firms as Technology Builders

    This development fits a pattern that maddaisy has been tracking across the consulting industry. Firms are not merely advising clients on AI — they are building proprietary capabilities that change the economics of their own delivery. McKinsey’s 25,000 AI agents. Accenture’s ongoing automation of delivery operations. Now PwC, with a tool that converts weeks of manual work into hours.

    The competitive implications are significant. A firm that can process complex financial workbooks in hours rather than weeks can bid more aggressively on engagements, take on more work with the same headcount, and offer the outcome-based pricing models that clients increasingly prefer. The spreadsheet agent is not just a productivity tool — it is a structural advantage in the shifting economics of professional services.

    What Practitioners Should Watch

    For consultants and enterprise leaders, the PwC announcement carries a practical message: the AI value gap may start closing not through headline-grabbing deployments, but through targeted tools that tackle specific bottlenecks in existing workflows.

    The broader FP&A landscape is moving in the same direction. IBM’s 2026 analysis of financial planning trends highlights that 69% of CFOs now consider AI integral to their finance transformation strategy, with the primary applications centring on data ingestion, budget analysis, and narrative generation — precisely the kind of spreadsheet-adjacent work that PwC’s agent addresses.

    The question is no longer whether AI can handle enterprise data complexity. It is whether organisations will deploy these capabilities against the right problems — the mundane, time-intensive, precision-critical workflows where the return on investment is most measurable and most immediate.

    PwC appears to have started there. Given the firm’s own data on the AI ROI crisis, that is arguably the most credible place to begin.