18 Sep 2025 15 min read Responsible Innovation

Responsible Innovation at Scale: Escaping Innovation Theatre

The Cost of Theatre

Innovation often conjures images of labs with kombucha on tap and hackathons galore. But when those activities produce little concrete value, they veer into “innovation theatre” – a set of performative gestures rather than strategic investments. As one industry analysis wryly notes, superficial moves like redesigning workspaces or throwing one-off hackathons have “little impact on [innovation] by themselves”[1]. Another expert observes that organizations “get caught up in performative change-making,” yielding non-measurable results and undermining their culture of innovation[2]. In practice, innovation theatre often manifests as siloed “innovation” teams churning out vanity projects that lack business integration, unsupported by metrics. Common signs include chasing the latest buzzword (blockchain, metaverse, etc.), copycat solutions, one-off contests, and vague “R&D” budgets with no tie to customer needs[3][4]. Such efforts may look busy on the surface, but they waste resources and erode credibility. (See exhibit: indicators of innovation theatre might be “hyped but unmeasured outputs,” “siloed pilots,” or “costly initiatives without ROI.”)

The hidden cost of this theatre is high. Budgets sink into projects that never scale, talent burns out on never-ending fluff, and real problems go unsolved. Senior executives end up justifying lackluster outcomes with buzzwords – the very definition of “just for show” innovation[3]. Harvard’s Gary Pisano warns that while creativity is inherently messy, it “needs discipline and management”[5]. Without that discipline, even well-intentioned innovation efforts devolve into vanity work. Steve Blank has similarly argued that when leadership confuses activity with progress, companies settle for theatre instead of true breakthroughs. The cumulative effect is stagnation: markets evolve, competitors capture genuine value, and public trust in the organization’s innovation claims erodes.

Ultimately, innovation theatre is costly not only in wasted dollars but in lost credibility. By contrast, responsible innovation at scale means embedding rigorous process and governance so that new technologies deliver measurable benefits and earn stakeholder trust. The remainder of this paper contrasts the two approaches, showing how to move from lofty principles and flashy pilots to robust programs with governance, evidence, and executive accountability. We will illustrate how leaders can exit the theater and build innovation that truly scales.

From Principles to Proof

Many organizations pay lip service to AI ethics and responsible innovation. They cite the latest principles – OECD’s innovative, trustworthy AI that respects human rights[6], UNESCO’s transparency and fairness[7], IEEE’s guidelines on ethical design, or even their own corporate values. But principles alone won’t escape the theater; companies must translate values into concrete evidence. Fortunately, international frameworks now provide blueprints for doing just that. The NIST AI Risk Management Framework (AI RMF), for example, was developed collaboratively with industry and government to help organizations “incorporate trustworthiness considerations into the design, development, use, and evaluation of AI”[8]. Likewise, ISO/IEC 42001:2023 – the first AI Management System standard – explicitly structures governance controls so companies can “demonstrate…responsible use of AI,” increasing stakeholder trust and accountability[9]. These are not vague exhortations but operational guidelines: ISO 42001, for instance, is aimed at any organization using AI and revolves around building an Accountable AI management system for ethical deployment[9].

Public policy models reinforce this direction. The OECD AI Principles call for AI that is innovative and trustworthy, anchoring on values-based guidance[6]. UNESCO’s 2021 Recommendation on the Ethics of AI (the first global AI ethics standard) likewise mandates human rights, transparency and auditability. Notably, UNESCO’s guidance declares that “AI systems should be auditable and traceable”, with built-in oversight and impact assessment mechanisms[10]. Similarly, the incoming EU AI Act (to be enforced across member states) goes beyond ideals: it legally requires high-risk AI providers to build end-to-end documentation, risk management, and automated logging into their systems[11].

What all these frameworks share is a shift from aspirational ethics to actionable accountability. In practice, that means moving from “We value transparency” to demonstrating transparency via artefacts and metrics. Examples abound: organizations now publish model cards – structured documents describing how an AI model was built, its intended uses, limitations, and evaluation results[12]. Governments use Algorithmic Impact Assessments to score an AI project’s risks before deployment – Canada’s AIA tool, for example, is a 65-question (risk) plus 41-question (mitigation) questionnaire that quantifies a system’s impact based on design, data and use[13]. Regulatory-friendly countries add further proof: the UK’s Algorithmic Transparency Recording Standard (ATRS) requires public agencies to publish details on their automated tools and why they use them[14]. These concrete deliverables (model documentation, risk scores, published transparency reports) turn fuzzy principles into verifiable facts.

By demanding this proof, leaders can distinguish substance from window-dressing. A principled approach isn’t negated – it’s reinforced – because stakeholders see living evidence. For instance, an organization might cite NIST AI RMF’s emphasis on accountability and risk management, then accompany it with a published risk-management plan and logged test results[8][15]. Or a company may align with the OECD’s call for fairness and inclusion, but back it up with disaggregated performance metrics and documented mitigation of biases. In short, to escape theatre, one must measure what matters. We will next explore how designing systems for auditability makes this proof possible.

Designing for Auditability

True scale demands that AI systems and processes be auditable – that every decision and dataflow can be traced, reviewed and remediated. This design principle is now codified in leading frameworks. The EU AI Act, for example, explicitly requires high-risk AI systems to be built for record-keeping: providers must “design their high risk AI system for record-keeping to enable…events relevant for identifying…risks and substantial modifications throughout the system’s lifecycle”[11]. In essence, continuous logs and version histories become mandatory. NIST’s AI RMF similarly stresses accountability: it flatly states that trustworthy AI “depends upon accountability. Accountability presupposes transparency”[16]. In practice, this means embedding detailed documentation and audit trails from the outset.

Operationally, auditability can take many forms. It starts with data: maintain provenance logs of training data and inputs, and store them in an immutable way. At model-building time, teams can use model cards and datasheets (per Gebru et al., 2018) to capture model architecture, training parameters, dataset sources, intended uses and known limitations[12]. Such artifacts document how the model was developed and should be regularly updated. At decision-time, systems should emit transparency records. For instance, organizations building AI for public impact can adopt something like the UK ATRS’s approach: producing a public dossier on each automated tool’s scope and logic[14]. Similarly, risk mitigation records (such as completed Algorithmic Impact Assessments) and internal pre-mortems – structured “what could go wrong” analyses done before launch – should be version-controlled.

Embedding auditability pays off in operational resilience. For example, Canada’s AIA explicitly includes questions on procedural fairness – it asks whether audit trails and decision-justification logs are in place[17]. Indeed, part of auditing a model is checking if, at decision time, the system records why it made a prediction. When workers can see the chain of data, code changes, and human overrides behind every automated outcome, organizations can spot and fix errors long before they blow up in public. As UNESCO puts it, systems should be “auditable and traceable,” with oversight mechanisms (impact assessments, audits, due diligence) baked in[10].

To illustrate, imagine an AI recruiting tool. A responsible, auditable design would automatically log each stage: candidate data inputs, model version used, scores assigned, and any manual review steps. Alongside this, the team would maintain disaggregated performance statistics (e.g. pass rates by gender and ethnicity) and track whether fairness issues remain open. Any incident – say a mis-hire or identified bias – would go into an incident database (like the AI Incident Database[18]) along with its root cause. In this way, every outcome is documented so that compliance checks (internal audits or external reviews) can systematically verify that procedures were followed and risks managed.

In practice, designing for auditability means rethinking toolchains and habits: versioning not just code but data labels, setting alerts for unexpected data drift, and keeping a changelog of all model retraining events. It means treating explainability not as an afterthought but as a requirement – e.g., building features that record model explanations at prediction time, so that regulators or affected users can interrogate outcomes. In short, it is a shift from secrecy to systematic openness. By doing so, companies generate a growing dossier of evidence: every artifact, log entry, and AIA report becomes proof of genuine governance, distinguishing them from organizations merely staging a facade of compliance.

Assure to Scale

Auditability alone is a foundation; to scale innovation with confidence, organizations must integrate assurance into their core operating model. Assurance here means continuous governance, monitoring and improvement guided by standards. In effect, companies must treat AI systems like safety-critical products – subject to routine checks, audits and certifications. This shift is captured in standards like ISO/IEC 42001, which positions an “AI Management System” alongside quality or security management systems[9]. Such systems demand dedicated roles (e.g. an AI ethics officer or risk manager), steering committees, and regular reporting.

The emerging playbook is to establish assurance as a parallel development stream. As one review puts it, organizations now need a tech-driven governance process – akin to financial audit – for AI projects[19]. Examples abound: financial institutions are building AI model risk management teams that vet models before release, analogous to underwriting committees. Health-tech companies audit their diagnostic algorithms through independent expert panels. Even product teams in consumer tech increasingly run internal “AI policy reviews” that stage-gate deployments.

Key to scaled assurance are metrics and operational KPIs. Just as software dev uses build-time tests and uptime logs, AI assurance tracks things like: risk closure rate (how quickly identified risks are mitigated over time), data quality indices, and bias/robustness metrics across all active models. For instance, a dashboard might display how many high-risk issues were opened vs. closed last quarter, or show current fairness gaps against targets. The idea is that metrics that used to be “nice-to-have” (e.g. average performance by demographic group) become essential. These measures transform ethical intentions into board-room numbers.

Frameworks support this approach. The NIST AI RMF defines four core “functions” – Govern, Map, Measure, Manage – that together form a lifecycle of assurance[20]. For example, the MAP function is about identifying and mapping out potential AI risks at each stage; MEASURE is about quantifying risk and performance; MANAGE is about implementing controls and mitigation plans. This cascading structure ensures that once a risk is identified (e.g. a privacy gap or bias), it is tracked through to closure. NIST explicitly ties these to trust objectives – safe, secure, transparent, and bias-managed systems[15].

Similarly, assurance scales when embedded in procurement and vendor management. Large enterprises now routinely insist on third-party audits and certifications (ISO 42001 compliance, IEEE 7000 alignment, etc.) before deploying external AI tools. They leverage the Data & Trust Alliance’s M&A diligence rubrics to evaluate startups for acquisition, assessing not just code but culture – whether a target’s developers “incentivize a learning mindset” and have policies for bias mitigation[21]. In doing so, buyers prioritize companies with mature assurance practices over those dazzling with hype.

Trustworthy scaling also leverages external accountability. Public bodies like the UK encourage cross-industry use of ATRS so that even private companies adopt similar transparency norms[14]. Nonprofits curate incident databases (e.g. the AI Incident Database[18]) so industry can learn from each other’s mistakes. All these create a marketplace where “doing the right thing” is measurable and comparable. The net effect: companies that invest in robust assurance can grow their AI use with far less fear of a downstream scandal. Investing in these systems pays off in fewer costly outages and regulatory fines, and greater investor confidence.

In sum, assure to scale means treating responsible AI not as a one-off checkbox but as a perpetual program. It requires structured processes, dedicated oversight, and metrics to prove compliance. By contrast, the innovation theatre model treats ethics as a campaign (“We’ll have an AI ethics week and some slides”) – which can never suffice for large-scale adoption. Embracing comprehensive assurance is the antidote: it aligns innovation with enterprise risk management, making growth sustainable.

Regulatory Goodwill as Strategy

Rather than view regulation as a threat, forward-looking organizations treat engagement with regulators as strategic. Creating regulatory goodwill means actively working with policymakers and industry consortia to shape sensible rules and build trust. This in turn reduces uncertainty and can become a competitive advantage. For example, companies that aligned early with forthcoming laws tend to get easier compliance clearance and influence rulemaking. When the UK introduced ATRS, agencies that proactively published tool records found that future audits became smoother – they were seen as partners, not adversaries[14]. Similarly, firms that engaged with the European Commission during the AI Act drafting gained better understanding of the high-risk categories and could tailor their compliance processes before the rules hit.

On the international stage, building goodwill involves adopting globally recognized principles even before they are mandatory. Many tech firms, for instance, voluntarily align with the OECD AI Principles (adopted by 40+ countries) to signal commitment to human-centric innovation[6]. Some even go further by embedding UNESCO’s core values – human rights, transparency, explainability – into their governance charters[7][10]. By doing so, they are “future-proofing” their designs: as one expert put it, they ensure “international law and national sovereignty…are respected” and that AI has “capacity for human agency and oversight”[22][10]. This level of proactive transparency can yield reputational capital: civil society and regulators reward companies seen as responsible collaborators.

Wealth of frameworks and toolkits also facilitates this strategy. Industry groups – from WEF’s Global AI Council to the Data & Trust Alliance – publish guidelines that help firms align with best practice. For instance, the Data & Trust Alliance’s M&A diligence tool was developed by dozens of corporate counsel and risk experts to assess exactly these issues[23]. Those companies using these toolkits can credibly claim to have met the emerging “gold standard” of due diligence. Likewise, non-profits like Mozilla actively track corporate AI behavior. Mozilla’s Trustworthy AI program, for example, monitors AI products for harms and publicly grades companies on privacy, fairness and transparency[24]. By staying ahead of such watchdogs – adopting data privacy loudly, implementing fairness audits – businesses can mitigate negative reports and even shape the narrative.

In short, regulatory goodwill requires a mindset shift: see rules not as bureaucratic hurdles but as guardrails that, if embraced, allow safer scaling. One might say it’s a form of insurance. When governments see a firm genuinely testing and reporting on its AI (via model cards, impact assessments or transparency records), they tend to view that firm as a responsible actor worth engaging[25]. This can translate into favorable outcomes: fast-tracked approvals, influence on standards committees, or even exemptions for trusted companies. Ultimately, positioning the company on the right side of regulation enables smoother long-term innovation and can ward off heavy-handed backlash.

Operating Model Shifts

Embedding responsible innovation at scale isn’t just a matter of policies and checklists – it demands fundamental changes in how organizations operate. Unlike theatre tactics, which are often siloed in innovation labs, a mature approach permeates every layer of the operating model. A common shift is creating dedicated governance bodies. Many enterprises now have AI ethics boards or review committees, comprising cross-functional stakeholders (engineers, legal, privacy, business units and sometimes independent experts). These boards set the tone: for any major AI project, proceeding requires sign-off on risk assessments, data privacy reviews, and monitoring plans. This “accountability by design” contrasts sharply with ad-hoc approvals of headline-grabbing projects.

Alongside committees, new roles are emerging. “AI Trust Managers,” “Data Ethics Officers,” or even just empowered Data Protection Officers now sit alongside CIOs and CTOs. These leaders ensure that day-to-day processes reflect governance requirements. For example, data scientists attend training in bias detection, and product managers incorporate fairness metrics into KPIs. When a model update is proposed, it triggers a checklist: Has the new data been audited for representativeness? Is there a plan for continuous monitoring? In other words, ethical checkpoints become as automatic as code reviews or security scans.

Culture and incentives must align too. Teams are encouraged (and sometimes rewarded) for flagging potential issues early, not hiding them. Some firms use pre-mortems as a matter of course: before writing a line of code, the team imagines how the system could fail for society, identifying fixes in advance. Other companies publish regular updates on their trust metrics internally – for instance, a quarterly scoreboard showing model performance by demographic group, or the percentage of identified risks that have closure plans. Transparency of this data means that executives can no longer claim ignorance; it builds a shared sense of ownership.

Another shift is in talent. Organizations broaden hiring to include ethicists, social scientists and legal experts on data teams. In tech giants, it’s no longer just engineers in the room: ethicists vet chatbots, and economists check algorithmic pricing tools. Startups too are joining this trend; the Data & Trust Alliance notes that investors increasingly ask about a target’s responsible-data culture before deals[23]. Thus ethical capacity becomes a part of M&A diligence – underscoring that company culture and process matter as much as code.

Finally, firms extend assurance beyond their walls. Software development lifecycles now frequently include vendor questionnaires on AI ethics, and many tech providers offer “explainability as a service” or third-party model audits. In essence, the entire AI supply chain is being rearchitected for transparency. This operating-model reboot is vital: without it, even the best intentions will revert to theatre. Only by embedding governance into everyday ways of working can organizations sustain responsible innovation as they grow.

Scorecard and Call to Action

Escaping innovation theatre is ultimately a matter of measurement and accountability. Companies should think in terms of scorecards that map values to proof. Such a scorecard would track concrete indicators of governance maturity. Possible metrics include: the number of AI systems covered by documented impact assessments or model cards; the rate of closure of identified ethical risks; diversity of test datasets (a proxy for fairness checks); and external milestones like published ATRS records or participation in certification programs. It might even include “trust signals” such as how often the company’s AI practices are highlighted (or criticized) in public policy discussions. A mature organization would score high on both “commitments” (signed up to OECD, UNESCO, standards) and “compliance” (completed audits, external validations, incident prevention).

Consider a hypothetical “Theatre vs. Scale” scorecard: on one axis we place governance sophistication (low to high), on the other we place evidence of impact (minimal to significant). Companies stuck in theatre cluster in the low-low quadrant – many claimed values but few proofs. The goal is to move to high governance and high impact. For example, a values-to-proof maturity map might outline stages: at the lowest stage a company only publishes broad principles; the middle stage it runs periodic AI ethics training and occasional audits; the highest stage it continuously monitors AI metrics, publishes transparency records, and iterates on policy with stakeholder feedback. Each step on that path can be measured.

Actionable steps: To operationalize this shift, we recommend the following, which align with the frameworks and cases discussed: - Adopt comprehensive standards. Charter responsibility by implementing recognized frameworks (NIST AI RMF, ISO 42001, IEEE 7000, OECD principles). Align internal policies to these to avoid piecemeal effort[8][9]. - Document rigorously. For every AI product or tool, publish key documents: model cards, dataset records, algorithmic impact assessments[12][13]. Use the UK’s ATRS or similar public reporting as a template for transparency[14][26]. - Implement end-to-end audit trails. Build systems that automatically log data sources, model versions, and decision rationales. Maintain an internal incident database (or contribute to public ones like AIID[18]) so lessons are captured and shared. - Measure what matters. Instrument all AI systems with performance dashboards that include fairness and safety metrics. Track and publicly report KPIs such as error rates by subgroup, response time to security vulnerabilities, and risk closure rate (the percentage of identified issues that have been fixed). - Engage regulators and standards bodies proactively. View upcoming regulations (EU AI Act, future OECD guidelines, etc.) as design targets, not surprises. Participate in multi-stakeholder forums (e.g. IEEE working groups, WEF alliances) to influence good practice. Publishing early results or white papers (à la Mozilla or WEF toolkits) can earn goodwill. - Transform culture and incentives. Build cross-functional AI governance teams and embed ethical criteria in project funding. Reward teams for achieving “trustworthy AI” milestones (independent audits passed, certification obtained) as much as for product features. Encourage a culture where raising an ethical concern is valued, not penalized.

In conclusion, responsible innovation at scale demands moving from empty rhetoric to embedded rigour. The playbook we’ve outlined – drawing on concepts from Blank, Pisano and the AREA framework[27][28] – is about anticipating consequences, reflecting on values, engaging stakeholders, and acting decisively. Executive leaders should champion this shift, ensuring that every investment in AI is paired with equivalent investment in assurance. The prize is a new innovation maturity: one in which cutting-edge technology truly serves business and society, rather than serving as a props for PR. Only then will organizations escape the theatre of innovation and perform in the real world of outcomes.

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https://www.bcg.com/publications/2023/innovation-culture-strategy-that-gets-results

[2] [3] [4] Pulling back the curtain on innovation theater

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[5] Innovation Isn’t All Fun and Games — Creativity Needs Discipline

https://hbr.org/2019/01/the-hard-truth-about-innovative-cultures

[6] AI principles | OECD

https://www.oecd.org/en/topics/sub-issues/ai-principles.html

[7] [10] [22] Ethics of Artificial Intelligence | UNESCO

https://www.unesco.org/en/artificial-intelligence/recommendation-ethics

[8] AI Risk Management Framework | NIST

https://www.nist.gov/itl/ai-risk-management-framework