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Remember when the topic of business agility took center stage at the start of the pandemic? Organizations needed to change their business models almost instantly and in multiple dimensions. There was a need to enable a remote workforce with increased investments in collaboration and security software. There was a greater push to the cloud to offset operational complexities. There was a drive for predictive analytics and rapid insight to understand potential business scenarios and outcomes.
Agility was at the forefront as organizations looked to rapidly adopt new technologies and establish new business processes while minimizing the damage from business disruption. A few years later, as large language models (LLMs), foundation models and generative AI takes center stage across virtually every industry, the agility lessons from the pandemic are being put to the test, and we're seeing organizations adopt and integrate this new technology as fast as they can.
Over the last few months, we've continued to see the changes businesses are making based on the success of OpenAI's ChatGPT. We've seen the competitive landscape emerge with some expected names, including Google Bard, Meta's LLaMA and NVIDIA's NeMo. We've seen existing partnerships get bolstered, the largest being Microsoft's relationship with OpenAI. We've seen announcements about new LLM capabilities fueling several technology offerings, from Salesforce and Adobe to C3AI and DataRobot to Databricks and ThoughtSpot. But the more eye-opening aspect here is that LLMs and generative AI have been around for a while now. Maybe they initially weren't as good as they are now, but we've hit critical mass and the world has taken notice.
I've never seen a technology work its way faster to becoming an acceptable market offering. Competing solutions, privatized solutions and augmented solutions have popped up everywhere. I've watched as organizations have rebuilt the pillars of their technology offerings and platforms in a matter of months to work on top of a foundation model. While putting existing roadmaps on hold, these organizations have pivoted to dedicating significant engineering resources to incorporate this game-changing LLM and generative AI technology into their products going forward.
These are leading multibillion dollar organizations whose platform offerings serve as the heartbeat to not only their own businesses, but also their customers' businesses. Even they recognized the power of this technology and the need to pivot. They saw that, in order to scale their business, they needed to modify -- or in some cases rebuild -- the very products that they've relied on for so long. If these massive businesses with hundreds of engineers and complex management structures can pivot this quickly, we all must recognize the potential and the need to pivot ourselves.
Business agility will again be put to the test as organizations scramble to establish best practices, usage guidelines and strategies going forward. Businesses must take a two-pronged approach to the incorporation of generative AI internally and externally:
- Internally, it's all about productivity and efficiency gains for the workforce. The idea of working smarter but not harder will enable pockets of employees or business units to easily exceed expectations. But understanding the risks of using the technology will be critical, especially as it relates to governance, security and compliance.
- Externally, it's all about the customer experience. Organizations must ask themselves how to incorporate this technology into their solutions as a capability that will enhance user experience and improve customer satisfaction. They must also recognize that the addition of such technology could very well disrupt future product roadmaps.
As businesses strive to remain agile, I will continue to keep an eye on the generative AI revolution that is upon us. Expect far more announcements from big tech as well as M&A. Expect new markets to emerge. Finally, expect significant disruption as organizations scramble to capitalize.