maddaisy – cultivating innovation

Tag: automation

  • Enterprise AI Finds Its Middle Ground: How Pega Blueprint Solves the Governance Gap

    Enterprise software development has a governance problem. Teams want the speed of AI-powered “vibes coding” – where natural language commands generate working applications – but enterprise requirements demand the control and predictability that casual AI tools cannot provide. Pegasystems’ latest update to its Pega Blueprint platform offers a glimpse of how this tension might resolve.

    The March 2026 Blueprint update transforms what was originally a linear design-to-handoff tool into a continuous conversational interface. Users can now modify enterprise applications through natural language – via text or speech – while maintaining the governance standards that large organisations require. It is a practical solution to what consultancies increasingly encounter: clients who want AI development speed but cannot sacrifice compliance and control.

    From Static Design to Living Conversation

    When Pega Blueprint launched in February 2024, it addressed a specific pain point: the slow, expensive upfront process of designing enterprise applications. The original version followed a conventional pattern – users described their application idea, Blueprint’s AI generated a structured starting point, then handed it to developers.

    The conversational update represents a different approach entirely. Rather than a one-time design exercise, Blueprint becomes what the company calls a “continuous copilot” – an interface that allows ongoing modification and refinement through natural language while preserving enterprise-grade security and governance requirements.

    “Organizations can create workflows more quickly, improve data and logic, and preserve control and predictability across mission-critical applications,” according to the company’s announcement. This combination – speed with governance – addresses what has become a fundamental challenge for enterprise AI adoption.

    The Consulting Opportunity  

    For consultancies, this development signals both opportunity and competitive pressure. The broader market context supports this view: recent Capgemini research shows that 85% of corporate clients plan to engage with non-bank providers within the next year, while only 23% believe traditional banks meet current expectations.

    The data reveals a broader pattern: organisations are seeking more agile, technology-forward partners. Traditional providers struggle – 82% of banking executives report no revenue gains from new products, and 51% see no expected cost reductions from innovation initiatives. Only 29% of IT budgets are directed toward transformative technologies.

    This creates space for consultancies that can effectively bridge the gap between AI capabilities and enterprise requirements. The challenge is not just technical – it is organisational. As Fortune reported, AI companies have discovered they need consultants to help sell their AI agents, as effective AI implementation requires significant organisational transformation: cleaning up data, redesigning workflows, and strategic thinking about competitive advantage.

    The Implementation Reality

    The consulting industry’s relationship with AI has evolved in an unexpected direction. Rather than eliminating consulting roles, AI complexity has created new demand for implementation services. OpenAI employs approximately 70 “forward deployed engineers” for customer implementation, and Anthropic maintains a similar number of implementation specialists.

    “AI still suffers from a trust deficit – most boards would still rather put their faith in advice from McKinsey or BCG than ChatGPT,” the Fortune analysis noted. This trust gap creates opportunities for consultancies that can position themselves as essential partners in AI implementation.

    The Pega Blueprint evolution illustrates this dynamic clearly. The platform promises that completed blueprints can be deployed as working workflows “in minutes,” but the enterprise governance layer – security, compliance, audit trails, role-based permissions – requires careful implementation and ongoing management.

    Market Signals

    Financial markets are taking a measured view of these developments. Citigroup raised its price target for PEGA stock to £75 from £73, citing “stable Q4 software results” and the company’s position in “defensive end markets.” The modest adjustment and “stable” language suggest measured progress rather than breakthrough momentum.

    This reflects a broader pattern in enterprise AI adoption: incremental evolution rather than revolutionary transformation. Conversational interfaces for development are not new, but solving the enterprise governance problem while maintaining development speed represents meaningful progress.

    What This Means for Consultancies

    The Blueprint update signals several trends worth monitoring. First, expect similar conversational interfaces to appear across enterprise platforms. The pattern – natural language interaction with robust governance – addresses a real market need.

    Second, the consulting market appears to be splitting between firms that can deliver AI-assisted solutions with enterprise governance and those constrained by traditional delivery models. The 85% of corporate clients planning to engage non-traditional providers suggests demand for more agile implementation partners.

    Third, the governance gap creates a consulting opportunity. Organisations need partners who understand both AI capabilities and enterprise requirements – not just one or the other. This requires a different skill set from traditional systems integration: understanding AI model behaviour, data governance for machine learning, and the organisational change required for AI-assisted workflows.

    The Pega Blueprint evolution represents enterprise AI finding its practical middle ground. For consultancies, the question is whether they can navigate this balance effectively – delivering AI-powered innovation without sacrificing the control and predictability that enterprise clients require. The market opportunity appears significant, but it demands a more sophisticated approach than either pure AI enthusiasm or traditional enterprise delivery.

  • 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.