How many times have you talked about generative AI recently? It seems to come up in every single business meeting, irrespective of the agenda or topic of conversation. Given this trend, it’s no surprise that business spend on generative AI technology is following one of the steepest ascents ever. Large global enterprises spent $15 billion on gen AI solutions in 2023, representing about 2% of the global enterprise software market in the technology’s first full year. While that percentage may seem small on the surface, consider the fact that it took four years for SaaS to reach that level. And by 2027, spending on gen AI is expected to soar even higher – as high as $250 billion.
What does this all mean? That enterprises’ attention will be focused heavily – and in some cases maybe even exclusively – on ramping up gen AI in their technology stacks. Is that a good thing? The answer, of course, is complicated.
Yes, experts such as McKinsey & Co. expect gen AI’s impact on overall productivity to add trillions of dollars in value to the global economy. But overinvestment in gen AI, at the expense of building a basic foundation for success, could actually be counterproductive for enterprises that haven’t already built a strong foundation for their technology stacks and business processes.
This happened, to an extent, during the early days of cloud. When the cloud revolution hit hard, back in the late 2000s, business and technology leaders doubled down on transformation. And because of limited budgets, they diverted spending from everyday operations. The result: Companies deployed new and innovative business models on top of underfunded technology tools and underdeveloped processes.
It could happen again with gen AI. While the technology promises to help enterprises write code, create content, research technical solutions, sell more products and train employees, attention needs to be paid to the underlying facets of the business, so their gen AI investments can generate the most bang for their buck.
The most important goal? Enterprises need to prioritize modernization and fix existing technology and process issues to make space for new and exciting innovations like gen AI.
There are six stages enterprises should tackle before – and during – their ramp-up into the world of AI.
First, optimize what you have. The clean-up operation starts here. Assess the strength of the technology stack, examine the organizational structure, and review the basic policies. Identify red flags and try to tweak what you have by applying industry best practices. Pay close attention to your data stack for both structured and unstructured data. This is foundational for AI, including gen AI.
Second, accelerate the optimization. Once enterprises clean up the initial issues, they can identify opportunities for improvement. Try to standardize and improve processes without ripping them out by the roots. Even high-level review can sharpen processes and improve your competitive advantage.
Third, modernize your resources, but make sure to keep humans in the loop. This is perhaps the most important step. Human creativity, after all, is the principal driver of organizational success. So, look at ways to replatform, improve workflow design and add automation, but keep human beings central to the process. Free up employees to focus on higher-level work, and maintain the irreplaceable value of human intellect in the final product.
Fourth, reimagine the areas where AI can support business strategy. Are there new markets to target? New products to introduce? Better ways to serve customers? Leaders should encourage employees at every level of the business – across operations, finance, marketing, sales, software development – to think about how they can get more done with AI. The possibilities are endless now that you’ve reduced your technology debt and leaned into the power of AI.
Fifth, look at ways to continuously innovate. All transformation needs to be continuous and foolproof. Establishing a baseline and a foundation is important. But projecting success into the future, as AI becomes a bigger part of the everyday business toolset, is critical.
Last, put a premium on skill development. Relying more on gen AI will force organizations to revise and elevate certain job roles. To do this, they need to invest in upskilling and reskilling programs, giving individuals the chance to learn new skills and transition into those emerging roles. This creates a compounding impact on entrepreneurship. While AI enables individuals to innovate, institute new practices and improve on the status quo, the individuals themselves need to develop new skills and take active roles managing the technology itself.
Building an AI-enabled modernization approach is based on the belief that business innovation should be sustainable.
Here’s an example of how a leading technology business prepped for its foray into gen AI. The company had been dominating its market and was content with its position. But it was being challenged by agile, brave, adventurous startups that were ready to embrace gen AI without the burdens of legacy infrastructure.
We worked with the firm to guide the business through the six stages of AI-enabled modernization. We even confronted the company’s fear of new technologies like gen AI by showing how employees could use it to decipher thousands of lines of code from its legacy systems. The more readable code empowered business leaders to identify opportunities for the modernize, reimagine and innovate phases. Today, the company is embarking on its gen AI project, leaving the limitations of the past behind.
Conclusion
Gen AI is here, and it’s promising to revolutionize business strategies going forward. Enterprises should invest, but also learn from some of the mistakes made with cloud strategies in the past. They need to start their clean-up operations – following an AI-enabled modernization mindset – to embed gen AI into the heart of the enterprise and lead sustainable growth for the future.
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