Inspire teams to think bigger with the boldness of your ambitions. 

Cut through complexity by setting goals so sharp that everyone understands them—and silos must crumble to meet them. 

Break big-picture transformation into manageable chunks to build momentum for massive change. 

Connect the dots between today’s business demands and the future talent, tech, and other resources that will be required to meet them. 

Seed transformation through earmarked investment.

Fund the innovations that have the greatest potential to drive long-term growth. 

Make real-time adjustments as needs change and shift budgets accordingly.

Don’t over-optimize your budgeting process or technical infrastructure for today’s business environment.

Prioritize adaptability over efficiency to avoid painting your business into a corner.

Arm teams with tools that help them visualize how changes in one area could affect others.

Track the domino effect each change has on the organization’s reputation, customer relationships, and employee engagement to improve models, inform future decision-making, and avoid surprises. 

Be willing to break with the past. 

Lean into what you want your business to look like in three years—even if it seems impossible today. 

Take a product development approach to transformation, encouraging teams to quickly adopt new strategies, measure their success, and then iterate based on what they’ve learned to avoid executing on outdated long-term plans. 

Promote a model-agnostic approach to AI development.

Encourage teams to experiment with alternative models, compare model performance, and select the most effective and efficient model for each task, rather than defaulting to large, complex models. 

Adopt open-source AI technologies to encourage interoperability and retain the freedom to tailor models to business needs. 

Clearly outline how AI assistants and agents should operate—and who is responsible for validating their work. 

Establish metrics and monitoring systems to assess AI effectiveness and create a culture of accountability. 

Determine where it’s appropriate for AI to automate decision-making and where AI should play a support role, helping humans better understand all of their options before they make mission-critical calls. 

Prepare for a world shifting away from static dashboards and reports into more conversational and predictive AI-fueled insights.

Identify the different data sources available across the organization and assess their current state, flagging gaps and weaknesses.

Start by creating a unified view of organizational data—something much more democratized than the control tower approach—with self-service data capabilities, data virtualization, and integration across systems of record.

Create a data fabric that makes it possible to integrate AI agents and assistants into daily operations—automating routine tasks, augmenting human decision-making, and supporting high-value work.

Establish a data quality framework that ensures accuracy, completeness, and consistency.

Establish a data access and security framework that ensures data is protected and access is restricted to authorized personnel.

Use a cloud-native data platform that enables real-time collaboration across silos and makes data more accessible, no matter where it resides.

Develop a training framework that provides AI assistants and agents with the necessary data and information to automate end-to-end workflows.

Help them understand the unique aspects of your organization, including ethical considerations, security and privacy policies, and desired customer experience outcomes. 

Establish a feedback loop that allows AI assistants and agents to provide feedback and suggestions to improve data quality and accessibility, as well as business processes.

Prioritize initiatives based on their ROI prospects.

Link the performance of innovation teams, including AI agents, to enterprise outcomes.

Broaden the set of KPIs used to measure innovation ROI to include metrics such as data value realization, innovation yield, and time-to-insight.

Focus less on quantity and more on quality.

Define KPIs for each innovation initiative— even if the work is experimental.

Look beyond incremental gains to drive transformational growth.

Gauge where you see the greatest opportunity to deliver top- and bottom-line results and direct resources toward those efforts. But don’t get caught in the productivity trap.

Fail fast and move on.

Use a formal project management process to track what works—and what doesn’t—so teams can learn from each other.

Decide to continue, modify, or cancel the work based on performance tied to pre-defined KPIs.

Use AI tools for job analysis and skill assessments to identify your most severe AI skills gaps.

Put your best tech talent on your most valuable strategic initiatives.

Build an AI board that blends technical and business skills to drive real change.

Give them the mandate and authority to look at the bigger picture and work across functional silos—and have them report directly to you.

Define the roles and responsibilities of each party and outline the terms and conditions of the partnership, including the scope of work, payment terms, and intellectual property rights.

Provide partners with access to the organization's systems, processes, and people.

Participate in joint planning, decision-making, and problem-solving to keep core partners aligned with the organization's goals and objectives.

Continuously evaluate how and why you are partnering, keeping shared values top of mind.

Bring people with a growth mindset—and technical expertise—into the fold to accelerate change.

Make borrowed talent feel at home by helping them understand the role they play in driving business outcomes.

Steep them in the organization’s culture, values, and goals by providing orientation, training, and support as if they were your own employees.