Creating and Implementing an Impactful AI Strategy: Steps for Economic Development Leaders

Artificial intelligence has become a crucial driver of innovation, competitiveness, and transformation across sectors. As a result, economic development organizations have a vital role to play in steering their region’s approach to AI adoption. By creating and executing a robust AI strategy, they can ensure local industries intelligently capitalize on AI while mitigating disruption. Here are key steps economic development leaders should take:

External AI Strategy

First, convene an AI advisory council with diverse voices from business, academia, government and community groups. This group can objectively assess regional strengths, pain points, and assets to underpin an AI strategy. Make sure the council represents all who stand to gain or lose from AI progress.

Next, work with the council to define priority focus areas where AI could drive growth, efficiency and positive impact. Potential targets include manufacturing, healthcare, education and sustainability. The strategy should play to the existing competitive advantages in your region. Still, don’t limit the vision – identify moonshot initiatives that could catalyze entire new sectors.

With target areas defined, turn attention to talent development. Partner with schools and colleges to modify curriculums and expand access to AI skills training, prioritizing programs for at-risk, low-income and marginalized groups. Simultaneously help incumbent workforce skill up through mid-career retraining programs. Talent is the fuel to power AI innovation.

The strategy should also boost research and entrepreneurship by designating an AI Innovation Zone with special resources and incentives. Foster collaborations between academia and industry within this zone to translate cutting-edge R&D into commercial ventures. Provide seed funding and shared lab infrastructure to promising startups through an AI accelerator.

Moving beyond this foundation, enact policies to incentivize rapid, responsible AI adoption. For example, develop regulatory sandboxes where companies can deploy experimental AI under flexible oversight. Or provide tax breaks for impactful AI implementations. Though avoid over-regulation – companies may otherwise resist adoption.

With the strategy set, ensure cross-departmental alignment during execution. For instance, have workforce agencies co-own talent programs with AI council members. And continually monitor progress through defined key performance indicators. Strategy without disciplined execution is hallucination.

The opportunities from AI are tremendous, but every region must chart its own unique path to harness AI based on local context and priorities. An inclusive, collaborative approach to developing an AI roadmap allows economic development leaders to maximize prosperity and equity as AI permeates society. Those who fail to plan, plan to fail when it comes to the AI era. The time for action is now.

Here is a bullet point timeline for creating and implementing an AI strategy for economic development:

Month 1:

– Assemble diverse AI advisory council with representatives from business, academia, government, and community groups

– Schedule first council meeting & set objectives

Months 2-3:

– Council completes assessment of regional landscape related to AI

– Identify target industries/areas where AI could drive growth

– Define specific goals and success metrics

Months 4-5:

– Audit existing talent pipelines and training programs

– Outline new AI skills initiatives needed

– Start conversations with educational partners

Months 6-9:

– Advisory council designs policy recommendations to support AI innovation

– Establish funding mechanisms and incentives (e.g. AI accelerator program)

– Scope out shared R&D infrastructure/resources

Months 10-12:

– Launch specialized AI talent development programs with schools

– Set up AI Innovation Zone backing key sectors

– Begin phased rollout of financial incentives

Year 2:

– Open shared R&D labs and AI accelerator

– Expand talent programs beyond pilots

– Monitor key performance indicators quarterly

Year 3:

– Continually refine strategy based on data and feedback

– Introduce new policies/programs to address gaps

– Benchmark progress against other regions


– Maintain alignment across government agencies

– Convene AI advisory council every 6 months

– Report progress and learning to stakeholder groups

The cross-functional involvement, continuous monitoring, and gradual phase-in are key to maximizing the impact from an AI strategy over the long-term.

Here is an AI strategy and implementation plan focused on internal AI adoption:

Internal AI Strategy  


Become a leading economic development organization in AI capability and innovation by transforming internal operations with AI technologies.

Internal AI Opportunities:

– Intelligent customer service agents

– Process automation

– Data analytics and decision support

– Predictive modeling

Implementation Roadmap:

Phase 1 (Months 1-3):

– Assess processes suitable for AI adoption

– Identify available data sets

– Run pilot chatbot and process automation projects

– Initiate basic AI skills training for staff

Phase 2 (Months 4-8):

– Hire dedicated AI leader and data engineers

– Continue staff training in AI literacy

– Refine prototype AI systems for full launch

Phase 3 (Months 9-12):

– Scale AI adoption in customer-facing functions

– Launch analytics dashboards providing key insights

– Use predictive modeling to improve targeting

Phase 4 (Year 2+):

– Achieve 30%+ improvement in productivity metrics

– Expand AI adoption to all applicable areas

– Become recognized innovator in AI transformation


– Create internal AI review board with heads of departments

– Complete rigorous testing for biases before launch

– Monitor AI systems closely, evaluate against metrics


– Develop clear data governance policies

– Maintain in-house AI talent through competitive benefits

– Foster culture embracing experimentation with AI

The phased approach allows you to build internal capability with AI, adopting use cases bringing the highest value and ROI first before expanding. Progress is governed closely to ensure fairness, transparency and continuous improvement.