Future-Proofing Your Organization: Key Principles for Leading Through the AI Transition

By Jonathan H. Westover, PhD

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Abstract: As artificial intelligence (AI) advances rapidly, organizations must develop meaningful long-term strategies to realize opportunities and mitigate risks, as academic research shows AI will significantly impact over 50% of jobs by 2030, requiring workforce planning, while industries like travel agencies have faced disruption from failing to adapt to digital technologies like AI. Effective AI strategies include establishing integrated 3-5 year technology roadmaps that articulate adoption timelines, skill development, and business transformations, identifying core capabilities to augment with AI, such as diagnostics for healthcare, while data and technology foundations are also critical for deploying AI projects at scale and continuous skills training is needed to fill emerging roles and offset potential job losses. The article discusses strategic considerations for industries like healthcare, where strategies focus on augmenting clinicians, standardizing data, and establishing governance, transportation organizations must plan for autonomous vehicle testing and adoption, and reskill mechanics, and financial institutions can develop robo-advisors and lending platforms while modernizing legacy systems. Therefore, organizational leaders need such integrated roadmaps and ongoing governance to leverage AI's productivity benefits while guiding responsible adoption and workforce evolution amid ongoing disruption through proactive strategic planning that will help direct, rather than undergo, technological transformation.

As artificial intelligence and related technologies continue to advance at an exponential rate, organizations face growing questions around how to develop meaningful technology strategies in light of AI's widespread impacts. While AI promises opportunities for new forms of innovation and business transformation, it also brings risks of disruption that could undermine entire industries if companies fail to proactively shape strategic responses. 4

Today will provide practical guidance for organizational leaders seeking to set appropriate technology strategies for the age of AI.

The Need for Proactive AI Strategies

Academic literature provides a strong foundation for understanding both the opportunities and threats created by AI that demand proactive strategic planning. Researchers at MIT Sloan have found AI will impact over 50% of activities that comprise current jobs in advanced economies by the early 2030s (Manyika et al., 2017). While new jobs will be created, workforce transformations on this scale require significant lead time to manage through retraining and redeployment of displaced workers, necessitating advanced strategic planning from organizations.

Other studies highlight that lagging industries risk facing existential threats from AI disruption. Research from Stanford GSB examining the impacts of digital technologies including AI found entire industries like travel agencies have been nearly eliminated due to failure to adapt amid digital change (Wyllie, 2020). Bain & Company has similarly cautioned any company failing to develop clear AI strategies runs major risks, positing those that invest early in AI will gain significant competitive advantages while latecomers may struggle to survive (Bughin et al., 2018).

Taken together, this research highlights the need for proactive, long-term thinking around AI strategy from organizations across all industries. Merely reacting to AI threats as they emerge risks crisis management where advance strategic preparation could enable controlled transformation leveraging AI's benefits. The following sections outline principles to operationalize AI strategies supported by this body of knowledge.

Key Principles for Developing AI Strategies

Build multi-year technology roadmaps

Effective AI strategies require strategic roadmaps extending 3-5 years into the future that clearly articulate how new technologies will be adopted, skills developed, and businesses transformed over the medium term. Setting intermediate milestones ensures ongoing assessment and calibration while demonstrating long-term vision and commitment to employees and partners.

Organizations should outline technology priorities, investments, capability development, workforce plans, and transformations for major business functions or divisions in their strategic roadmaps. Case studies show tech leaders like Apple, Amazon, and Intel have consistently outperformed by following multi-year innovation roadmaps (Afuah & Tucci, 2001). Without clear direction, firms risk losing focus amid disruption.

Identify core capabilities for augmentation

Most industries will see initial AI benefits from augmenting human capabilities rather than fully automating jobs. Strategies should pinpoint core human expertise—diagnostics for doctors, negotiating for sales teams—to rapidly scale through AI tools supporting but not replacing workers.

For instance, radiologists at Johns Hopkins leveraged AI to improve diagnoses by 27% through augmenting human pattern recognition with machine learning algorithms (Li et al., 2019). Careful capability mapping enables focusing investments on productivity gains versus destabilizing layoffs through full automation too quickly.

Establish data and technology foundations

Collecting and managing high-quality data is essential for effective AI use across operations. Strategies must identify key data sources, standardize formats/protocols, address privacy/security, and establish data quality metrics as foundations for AI projects. In parallel, common technology infrastructures simplify later deployments by consolidating costly AI platforms, tooling, and training resources.

Leaders like Amazon built extensive data lakes and cloud services before delivering most business AI. Companies lacking strong foundations risk complex, fragmented, and costly AI initiatives unable to scale. Front-loading these underpinnings allows readier leverage of emerging AI opportunities.

Continuously develop new skills

Rapid technological change necessitates ongoing workforce development to avoid skills gaps. AI strategies outline 3-5 year talent needs, recruiting priorities, and skills development programs spanning professional training to leadership coaching. Progressive reskilling offsets potential job losses while equipping current staff for higher-value roles amid business change.

General Motors took a proactive approach, partnering with Udacity to reskill over 9,000 employees on subjects including AI, machine learning, and robotics (D'Onfro, 2017). Tied to multi-year plans, continuous skills evolution builds organizational agility and engagement in technological transformation.

Strategic Considerations for Healthcare, Transportation, and Financial Services

Healthcare

Healthcare faces widespread impacts from AI-based diagnosis, treatment recommendations, drug discovery, and more. Addressing pressing cost and access issues demands thoughtfully embracing this disruption. Strategies in healthcare focus on:

• Augmenting clinician expertise through tools assisting diagnoses/treatment while maintaining human oversight and trust.

• Standardizing EHR/medical images to maximize data leverage across organizational siloes limiting early AI.

• Investing in pilots to evaluate AI risks/benefits, inform development needs, and establish governance/explainability guidelines.

• Developing new roles at the intersection of care delivery and AI like AI nurse specialists supporting patients and clinicians.

Progressive strategic adoption allows transitioning to improved, accessible care models leveraging AI's full potential responsibly.

Transportation

AI is transforming transportation through self-driving vehicles, predictive maintenance tools, and optimized logistics networks. Transportation organizations require strategies to:

• Outline test plans and regulatory engagements to bring self-driving technologies safely online over the next 3-5 years.

• Augment fleet operations through intelligent tools monitoring equipment health, routing efficiencies, and remote vehicle supervision.

• Develop new digital services like AI-optimized freight marketplaces to diversify revenue amid disruption to traditional businesses.

• Invest in reskilling mechanics, drivers, and logistics planners for roles maximizing autonomous systems while overseeing operations.

Proactive planning ensures transportation leaders direct disruption through innovation rather than risk disruption from followers.

Financial Services

AI is enabling new fintech models while reshaping traditional financial management. Strategies focus on:

• Developing AI-enhanced robo-advisors, lending platforms, and fraud detection maintaining compliance and explainability.

• Augmenting wealth managers and brokers through AI-based portfolio recommendations, client screening, and relationship insights.

• Modernizing legacy systems, normalizing data, and establishing governance/oversight models to leverage burgeoning AI capabilities responsibly at scale.

• Evaluating entirely new business opportunities servicing AI startups through banking, underwriting, and investment services.

Financial institutions directing strategic transformation stand to gain significant competitive differentiation in coming years through well-planned AI enablement.

Conclusion

Setting appropriate technology strategies requires proactively envisioning AI's impacts to guide responsible adoption, workforce evolution, and business model transformation. The principles and industry examples outlined provide a framework for organizational leaders across functions and industries to develop multi-year roadmaps leveraging AI's productivity benefits while mitigating risks of disruption.

However, effective strategies also establish ongoing governance and review processes recognizing AI's uncertainties. Regular recalibration ensures plans continue adapting to new capabilities, use cases, risks, and opportunities that will inevitably emerge. With diligent strategic oversight, AI promises to augment human capabilities in transformative ways across the economy for organizations proactively directing change. Those lagging risk facing disruption, underscoring the importance of establishing a strategic vision and plans today for technological transformation in tomorrow's AI-powered world.

References

• Afuah, A., & Tucci, C. L. (2001). Internet business models and strategies: Text and cases. Boston: McGraw-Hill/Irwin.

• Bughin, J., Seong, J., Manyika, J., Chui, M., & Joshi, R. (2018, November). Notes from the AI frontier: Modeling the impact of AI on the world economy. McKinsey Global Institute. https://www.mckinsey.com/featured-insights/artificial-intelligence/notes-from-the-ai-frontier-modeling-the-impact-of-ai-on-the-world-economy

• D'Onfro, J. (2017, July 18). GM is retraining over 9,000 employees on AI and robotics amid massive transformation. CNBC. https://www.cnbc.com/2017/07/18/gm-is-retraining-over-9000-employees-on-ai-and-robotics-amid-massive-transformation.html

• Li, S., Wang, L., Chu, W., Bañuelos, C., Lai, Y., Ji, J., Cai, W., Li, B., Qiu, T., Xia, Y., Zhou, L., Sun, Y., Liu, M., Qiao, J., Deng, J., Lin, Y., Yan, W., Shi, L., Wu, F., & Zheng, Y. (2019). Development and validation of a deep-learning algorithm for the detection of pulmonary nodules and its applications to screening CT images. Journal of Thoracic Oncology, 14(11), 2009-2020. https://doi.org/10.1016/j.jtho.2019.06.004

• Manyika, J., Chui, M., Miremadi, M., Bughin, J., George, K., Willmott, P., & Dewhurst, M. (2017, January). A future that works: Automation, employment, and productivity. McKinsey Global Institute. https://www.mckinsey.com/featured-insights/digital-disruption/harnessing-automation-for-a-future-that-works

• Wyllie, J. (2020, March 2). Digital disruption case study: How travel agencies were nearly eliminated. Businessstudent.com. https://businessstudent.com/industries/digital-disruption-travel-agencies/

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Jonathan H. Westover, PhD is Chief Academic & Learning Officer (HCI Academy); Chair/Professor, Organizational Leadership (UVU); OD Consultant (Human Capital Innovations). Read Jonathan Westover's executive profile here.

Suggested Citation: Westover, J. H. (2024). Future-Proofing Your Organization: Key Principles for Leading Through the AI Transition. Human Capital Leadership Review, 13(4). doi.org/10.70175/hclreview.2020.13.4.3