What Great
Transformational
Leaders Expect

Great leaders want responsibility for business and operational transformation, not just IT delivery, dashboards, models or proofs of concept.

Manufacturers should provide:

• A written role charter.
• Three to five priority business outcomes.
• Clear enterprise, business-unit and plant scope.
• Defined ownership of AI strategy and execution.
• Authority to challenge low-value projects.
• Permission to stop unsuccessful pilots.
• Accountability for adoption and value realization.
• A clear distinction between the CIO, CDAO, CTO, COO and digital-manufacturing mandates.

The candidate wants a sponsor who does more than approve the budget. The sponsor must defend the transformation when results take longer than expected, resolve conflicts and hold business leaders accountable for adoption.

The company should identify:

• The primary executive sponsor.
• The CEO’s role in AI governance.
• The COO’s responsibility for operational adoption.
• The board’s expectations.
• Which executive resolves IT-versus-operations disputes.
• How business presidents and plant leaders will be held accountable.
• How often the AI leader will meet with the CEO and executive committee.

A great leader will investigate whether the position carries authority or merely accountability without control.

The manufacturer should define decision rights for:

• AI investment priorities.
• Data ownership and remediation.
• Technology and architecture standards.
• Cloud, edge and industrial-platform choices.
• Build-versus-buy decisions.
• Vendor and consulting-partner selection.
• AI governance and model-risk processes.
• Hiring and organization design.
• Production deployment approval.
• Benefits measurement.
• Retirement of obsolete systems and unsuccessful initiatives.

Candidates will be cautious if every important decision requires approval from unrelated functions or incumbent vendors. CIO research also warns that premature dependence on large vendors can restrict the organization’s ability to determine its own technology direction.

High-caliber leaders do not expect perfect data or a completely modern technology estate. They expect the company to tell the truth.

Before final interviews, the company should disclose:

• Data-quality and accessibility problems.
• ERP, MES, PLM, QMS, EAM and historian fragmentation.
• Unsupported applications and infrastructure.
• IT/OT integration maturity.
• Cybersecurity and operational-resilience gaps.
• Existing AI pilots and their actual adoption.
• Vendor dependencies.
• Internal capability gaps.
• Previous transformation failures.
• Political resistance.
• Budget constraints.
• Acquisitions or divestitures affecting the architecture.
• Regulatory, safety and intellectual-property restrictions.

The leader expects agreement on how value will be identified, approved and measured.

The company should provide:

• Finance-approved operating baselines.
• Access to operating and financial data.
• Named business owners for every major use case.
• Total-cost-of-ownership standards.
• Benefit-attribution rules.
• Adoption and usage metrics.
• Stage-gate funding.
• Stop, redesign and scale criteria.
• A process for tracking sustained benefits after deployment.

A strong candidate will not accept responsibility for speculative ROI based on employee time theoretically saved. The company must be willing to measure whether behavior, decisions, workflows and operating results actually changed.

The best leaders want freedom to balance early results with long-term foundations. They do not want to spend two years cleaning every data source before producing value, but they also do not want pressure to deliver disconnected quick wins that create more technical debt.

Manufacturers should permit the leader to:

• Select a few high-value, feasible use cases.
• Fix only the data required for those initial decisions.
• Build reusable data and integration components.
• Establish common security and governance patterns.
• Capture early value.
• Reinvest lessons and capabilities into subsequent deployments.
• Scale successful products across plants and functions.

This is the desired loop:

Identify value → establish a baseline → prepare minimum viable data → redesign the workflow → deploy safely → measure adoption → validate value → reuse the foundation → scale.

The leader will want to know whether the company treats data as an enterprise asset or as something controlled by individual plants, functions and vendors.

The company should provide:

• Named data owners.
• Authority to resolve conflicting definitions.
• Access to enterprise and operational data.
• An inventory of critical sources.
• Data-quality measures.
• Master-data ownership.
• Industrial-data contextualization.
• Data-product funding.
• Metadata and lineage capabilities.
• Appropriate security and access controls.
• Rights to retrieve data held by vendors.
• A plan for connecting plant, engineering and enterprise systems.

A transformation leader expects to build multidisciplinary teams, not inherit an organization composed only of infrastructure professionals, analysts or external contractors.

The company should support teams containing:

• AI and data-product leaders.
• Manufacturing and engineering experts.
• Data engineers.
• AI and analytics specialists.
• Software and integration engineers.
• Cloud and edge architects.
• OT and industrial-data specialists.
• Cybersecurity and responsible-AI professionals.
• User-experience and workflow designers.
• Change-management leaders.
• Finance partners.
• Product owners from the business.

Great candidates do not want to become contract managers overseeing a permanently outsourced capability. They want a balanced build, buy and borrow strategy.

The company should agree that:

• Strategy remains internally owned.
• Business-product ownership remains internal.
• Critical data knowledge remains internal.
• Architecture authority remains internal.
• Key engineering and governance capabilities are developed internally.
• Partners cover temporary gaps or specialist needs.
• Every major partner has knowledge-transfer obligations.
• Internal employees work alongside partner teams.
• Partner dependency declines as the internal team matures.

The AI leader will closely examine existing relationships with consulting firms, systems integrators, cloud providers and industrial-platform vendors.

The manufacturer should give the leader authority to:

• Reevaluate incumbent partners.
• Inspect contracts and commercial commitments.
• Change partners when performance is inadequate.
• Require named delivery personnel.
• Establish outcome-based milestones.
• Demand complete technical documentation.
• Secure data-export and portability rights.
• Retain source code, configurations and evaluation assets where appropriate.
• Require cross-training.
• Establish transition assistance.
• Prevent partners from controlling architecture decisions.

The principle should be:

Partners provide acceleration and specialist expertise; the manufacturer retains strategy, architecture, data, product ownership and operational accountability.

A candidate will hesitate if a consulting firm understands the architecture better than employees, a vendor controls access to industrial data or changing platforms would require rebuilding the entire operating environment.

Great leaders know AI value comes from changing work. They will examine whether business leaders and frontline employees are prepared to participate.

The company should support:

• Operator and engineer involvement in product design.
• Workflow redesign.
• Role and responsibility changes.
• Manager training.
• Frontline AI education.
• Protected time for subject-matter experts.
• Adoption measures.
• Feedback channels.
• Appropriate incentives.
• Transparent communication about workforce effects.
• Human oversight of consequential decisions.

Manufacturing AI leaders accept that production environments require stronger controls than office-productivity applications. What they resist is an approval system that treats every experiment as equally dangerous.

They expect:

• Use cases classified by risk.
• Fast paths for controlled, low-risk experiments.
• Security review integrated into development.
• Human oversight for consequential decisions.
• Defined production-approval criteria.
• Model evaluation and monitoring.
• Incident-response procedures.
• Protection of proprietary manufacturing information.
• Clear rules governing vendor use of company data.
• Additional safeguards for AI connected to equipment or OT.
• Authority for operators to override or stop safety-sensitive systems.

Great leaders look for a company where bad news can travel upward.

They want:

• Permission to challenge unrealistic AI promises.
• Transparent reporting of failed experiments.
• No pressure to manufacture ROI.
• Evidence-based disagreement.
• Leaders who distinguish learning from negligence.
• Willingness to stop favored executive projects.
• Accountability without blame-driven behavior.
• Respectful collaboration between corporate and plant teams.

High-performing leaders are often already well paid and professionally secure. A new role must offer a larger platform for impact.

Attractive opportunities provide:

• Enterprise-level influence.
• CEO and board exposure.
• Scope across plants or business units.
• Responsibility for building a new capability.
• A path from functional leadership into broader enterprise leadership.
• Visibility into strategy and capital allocation.
• Authority to develop future leaders.
• The opportunity to create externally recognized outcomes.
• A credible succession or expanded-scope path.

Purpose and scope matter, but the personal economics must remain competitive.

The company should address:

• Base compensation.
• Annual incentive.
• Long-term incentives or equity.
• Performance measures within the leader’s control.
• Sign-on compensation for forfeited incentives.
• Relocation support.
• Travel expectations.
• Location and flexibility.
• Severance and change-of-control protection where appropriate.
• Executive benefits.
• Family considerations.
• Job security and organizational stability.