Put humans at the centre of gen AI strategy to succeed

Contributors: Antoine Mindjimba, Partner, People Advisory Services

Robert Alexander, Partner, People Advisory Services

Andrea K. Webster, Senior Manager, People Advisory Services

Dean Selby, Senior Manager, People Advisory Services

Learn more how aligning AI with a human-centred business strategy enables organizations to harness its full potential.

In brief

• Generative artificial intelligence (gen AI) represents a ground-breaking leap in artificial intelligence technology, unlocking the ability to generate new and authentic artifacts on a large scale. 
• Easily accessible, simple to use and inexpensive, gen AI is one of the most rapidly adopted technologies the world has ever seen – with the potential to increase productivity, create economic impact, transform work, improve data governance and transform human resources.
• Aligning gen AI with a human-centred business strategy enables organizations to harness its full potential while also mitigating potential risks and ethical concerns.

More than 40% of organizations plan to spend at least $US1M on gen AI in 2023 alone.¹ Why? gen AI holds the potential to drive innovation and growth. But with those possibilities come new risks, including potential bias, privacy concerns and more. Organizations looking to mitigate these risks and implement gen AI transparently, fairly and accountably should put people at the heart of the strategy. Doing so now can shape a transformational partnership in which humans and technology work collaboratively to shape the future.

EY research shows giving specific focus to a series of human factors can increase the probability of business transformation success to more than 70%. Now consider that fact in the context of gen AI — a massively influential modern technology that is fundamentally transforming the way people work, but still requires human decision-making.

We know gen AI technologies are creating value-added use cases, from marketing and sales to human resources. In fact, Microsoft’s Work Trend Index report showed 31% of business leaders are looking to empower people with AI, while only 16% see this tool as a means of replacing humans in the workplace. ² These leaders are two times more focused on using AI to increase productivity than to cut headcount; reducing headcount was last on the list of what leaders said they’d value from implementing AI. Put simply: this technology goes beyond simply improving efficiency through domains like data analytics and offers opportunities to reduce the quantity of mundane, process-focused work employees do — allowing people to instead focus on value-added work that benefits them and the broader organization.

To make the most of those possibilities, teams must first understand how to use the tool effectively and responsibly to prevent mistakes, confidential leaks or other issues. This requires organizations to rethink everything from required skills to success metrics, cultural impacts, organizational risks and employee lifecycle impacts. Above all? It means thinking through the multitude of ways gen AI will affect people, and proactively managing that change.

Failing to ground your strategy on your workforce’s very human realities could put your business at risk of losing out on this technology’s potential while opening a digital door to new vulnerabilities and risks. The time to consider those human-centric factors is right now, before your workforce embraces the tool without clear direction or governance to guide their work.

What does a successful gen AI strategy look like? 

To build an effective gen AI strategy for your organization, you must start by asking these questions:  

1. Is our corporate culture primed to support gen AI implementation?

Implementing gen AI into the workplace can lead to considerable changes in ways of working. To succeed, the workplace culture and people who bring it to life must be fundamentally open to innovation. You need an environment in which people not only recognize gen AI’s potential benefits, but where they’re also willing to explore and adopt AI solutions. Employees will also need to feel comfortable, and value their new tools and “digital colleagues.”

2. Are our leaders ready to adapt to this change?

As employee roles evolve alongside gen AI, the next generation of leaders will need to take on even more responsibility and decision-making duties. This includes creative thinking, employee development and bridging the gap between technology and people. According to one survey of C-suite executives, 77% of IT decision-makers are convinced that employees can be taught to work with new job positions that AI will bring to the organization.³ Some 90% of survey respondents also anticipate quantifiable advantages from the organizational deployment of AI.

3. What adaptive skills do employees need to work alongside gen AI?

For people to work seamlessly alongside AI, they’ll need to equip themselves with new skills to work iteratively in this transformational work pattern. By developing soft skills and targeted hard skills, and by fostering a culture of continuous learning, organizations can equip employees to work alongside generative AI, effectively taking advantage of its capabilities while driving innovation and adapting to the changing technological landscape.

4. How can gen AI support and improve our employee lifecycle process?

Organizations that embrace gen AI to improve employee experiences can positively impact retention in the organization. Further, tasks such as analyzing vast amounts of candidate data from various sources to identify suitable candidates, automating performance data to provide real-time insights into employee progress and offer feedback accordingly, or identifying high-potential employees to support succession planning efforts can all become easier and more efficient. With gen AI, talent management and development opportunities abound.

By implementing these strategies, organizations can manage the people experience across the hire-to-retire lifecycle for roles that will work with gen AI. This approach fosters employee engagement, growth and a positive working environment.

5. How will we measure the impact of implementing gen AI?

Metrics that reflect the influence of the technology on other data and analytics investments and the business are more valuable than focusing on direct return on investment. In this sense, key performance indicators provide a quantitative and measurable way to track the impact and effectiveness of generative AI initiatives, enabling organizations to make data-driven decisions and optimize their AI strategies.

6. How should we evolve data governance to support gen AI?

Data governance is fundamental to support data quality, protect privacy, mitigate biases, comply with regulations and make ethical and trustworthy use of AI-generated content. Implementing generative AI well requires organizations to simultaneously adopt a governance structure that accounts for the nuances this technology brings. By doing so, organizations can harness the benefits of gen AI while reducing potential risks and maintaining public trust.

7. Are we casting a broad enough net where risks are concerned?

Proactively mitigating risks such as bias, transparency, privacy and regulatory requirements is crucial to preserve trust with customers, employees and stakeholders while deploying and implementing gen AI. By addressing these risks, organizations can navigate the ethical challenges and potential harm associated with generative AI, while also fostering a responsible approach to its implementation.

8. How will we manage this change across the organization?

Change management is key because it addresses the human aspects of the transition, encourages employee buy-in and engagement, provides necessary skills and training, and supports the cultural shift needed to successfully integrate AI technologies into an organization's operations. By considering change management from the outset, organizations can reap the benefits of generative AI while reducing resistance and disruptions during the implementation process.