GenAI Taking Off: FOMO Replaces Fear of Flying

Wikipedia says “pilot fatigue” is prevalent among captains of the air because of “unpredictable work hours, long duty periods, circadian disruption and insufficient sleep.” The crowd-sourced encyclopedia is talking about airline personnel, of course, but that description sure sounds like teams leading enterprise GenAI pilot programs. The past couple of years have been filled with sweeping experimentation, sky-high hopes and legitimate concerns.

Also Read: What is a CAO and are they needed?

Now, we see massive eagerness to get proven use cases off the ground and into business-transforming production. But, just like airline crews must go through stringent safety checks before takeoff, business leaders must be thoughtful even as they accelerate into their next steps with GenAI. High on the checklist is closing critical gaps in company policies and aligning technology strategies with business objectives.

Here are some eye-opening data points: 99% of organizations have invested or plan to invest in GenAI, yet 72% said they have no policy in place for internal use of this technology. That’s according to a recent survey of 2,300 IT and business leaders across 12 industries in 34 countries. The survey was conducted last October and November.

Considering that powerful GenAI apps with free service are available on every app store, the lack of guidance for employees – much less hard rules – is alarming! Further, while 83% of respondents said their organization has a well-defined GenAI strategy in place, a majority – 51% to be precise – have not aligned that strategy with their business plans. Maximizing ROI depends on strategic alignment across the board.

Don’t take me as a contrarian about GenAI. I think humankind was meant to fly, and GenAI gives us wings (with apologies to Red Bull). The gap between investment plans and absolute organizational readiness is a result of just how fast GenAI is penetrating the enterprise. GenAI pilots have become like Wi-Fi access points or luggage carriers at O’Hare – they’re everywhere!

But we’ve reached the point where organizations are turning their attention from experiments to specific use cases that will turn hype into superior business performance. In fact, more than 90% of organizations are assessing enterprise-wide applications.

Reality Bytes

 Among Chief Information Security Officers, one-third are uncomfortable with the “black box” nature of some GenAI models, and almost half (45%) say they feel “pressured, threatened or overwhelmed” by the technology. Fear of flying is a real thing, and from my personal conversations with technology leaders, the pace of change is the most deep-seated source of discomfort.

Meanwhile, across the rest of the C-Suite, two-thirds view GenAI as a revolutionary game changer. And overall, 72% of respondents say they are now very satisfied with their GenAI efforts so far, up from just 41% who were “very satisfied” a year ago.

My own anecdotal evidence shows more people are running to GenAI than avoiding it. The leaders who had early FOMO (Fear of Missing Out) are now “in it to win it.” Even those who were skeptical early on are now coming on board.

Also Read: Taking Advantage of Gen AI With Next-level Automation

Prioritizing Opportunities over Costs

The primary motivations for investing in GenAI vary by industry. But the need to improve productivity and efficiency is high on the list regardless of industry, no surprise there. But it is surprising – given expectations (or paranoia) that GenAI will replace humans en masse – cost reduction is not even among the top five motivating factors.

After “improving productivity,” the other top motivators are, in order:

• Improving sustainability and ESG metrics
• Improving compliance and process adherence
• Improving employee experience
• Maintaining competitiveness

Aggregated across all industries, the top five uses cases are:

• Personalized service recommendations and knowledge management
• Product and service design, research and development
• Quality control
• Risk assessment and fraud detection
• Process automation

Lessons Learned and Obstacles Encountered

An old goal (having clean data) is the new biggest lesson learned from GenAI pilots to date: Most organizations agreed that high-quality and clean data sources are paramount. Respondents also recommend beginning with focused projects and expanding only as warranted by outcomes. In addition, you can avoid siloed GenAI projects by ensuring that learnings and outcomes are shared across the entire, and I do mean entire enterprise. Creating an enterprise-wide GenAI forum is a best practice for disseminating lessons and increasing expertise.

As for challenges, organizations first cite the need to assess complementary architectures including cloud, platform as a service and infrastructure as a service. The focus is now expanding to include complementary capabilities such as IoT, 5G and Edge.

The architecture challenge is followed by the task of identifying preferred expert partners, wrestling with infrastructure complexity, building in-house skills for GenAI development, and establishing clear ethical and AI safety frameworks and ownership.

Building a Strategy for GenAI Success: Fail fast, learn and iterate

GenAI is not optional for most enterprises, and there should be no GenAI strategy apart from the core business strategy. Here’s a roadmap for success:

1. Identify and prioritize the key areas or domains where GenAI can be applied.
2. Assess the ROI for those priorities and then focus on developing specific assets and capabilities. Critical considerations range from infrastructure to data management and digital workplace solutions.
3. Find your platform and create a team. Now is the time to develop a secure and scalable platform for implementing GenAI uses cases. This must be done in compliance with regulations and ethical standards. To most efficiently accomplish that, create a diverse and expert GenAI team to coordinate the effort.
4. Manage your data to ensure it’s clean, consolidated and governed by clear data policies.
5. Experiment and scale, beginning with contained proofs of concept you can then scale into successful high-impact initiatives.
6. In tandem with your technology, deploy change management. Change management overcomes resistance and streamlines adoption.
7. Finally, never stop. Continue to increase awareness of GenAI at all levels. Train employees, address regulatory and ethical concerns, improve business processes, and keep change-management on track.

The faster companies lean into GenAI, the faster they will achieve competitive advantage. My personal advice is “fail fast, learn and iterate” your way to clear strategies and advanced capabilities. At every step of the way, keep ethics and governance in mind to maintain stakeholder trust as you establish your position in the AI-powered world.

[To share your insights with us as part of editorial or sponsored content, please write to psen@itechseries.com]