Generating real business value with generative AI for SMBs and beyond

It’s no secret that generative artificial intelligence (AI) has enormous economic potential. McKinsey & Company predicted that the benefits to the global economy could total more than $4.4 trillion. But the dust is still settling on a wave of companies investing heavily in generative AI, and many are finding that ROI remains elusive.

Unsurprisingly, technology teams have been eager to experiment with different proof-of-concepts (POCs). But as Ben Schreiner, head of AI and Modern Data Strategy Business Development at AWS, notes, “The real bottleneck is getting POCs into production. This is partly because large language models (LLMs) are the Swiss army knives of technology – a tool that can perform so many different tasks.” With such a vast scope of applications across product development, customer experiences, and more, what makes generative AI so exciting is ironically what’s holding companies back from getting the most value out of it.

“Many struggle to focus on working backwards from a real business problem. Even once they uncover a well-defined business use case, failure to achieve internal alignment hinders ROI. To make investments profitable, a paradigm shift is needed,” Schreiner adds. His recommendation for boosting the revenue potential of generative AI? “Companies of all sizes must pursue a purpose-driven strategy that spans employees, business processes and customers.”

Value Driver #1: Grow your people

As the saying goes: time is money. From writing emails faster to automating menial tasks, the value of improving employee productivity is undeniable. But as Schreiner notes, “You can’t achieve new levels of efficiency if your people are uncomfortable with generative AI. Any barrier to adoption will be a barrier to the bottom line.” According to a recent survey by Prosper Insights & Analytics, the top three employee concerns about AI were that it requires human insight (32 percent), that it may provide incorrect information (29 percent), and that it will cause job losses (27 percent).

Ongoing training and support will help alleviate these concerns, while also enabling teams to reap the benefits of the tools. As Schreiner says, “While some functions will be automated, most humans will be supplemented by generative AI, not replaced by it. To achieve the best results, we must analyze its impact on employees, prepare them for the evolution of work and take them along on the journey.”

He continues: “Ultimately, AI systems are predictive and not deterministic, making a human-in-the-loop approach critical. By training people to verify the results, you can both prevent hallucinations and alleviate concerns.” Manufacturer Georgia-Pacific is just one company seeing the fruits of this strategy. By leveraging employee expertise to create a routine maintenance tool, they have empowered employees to become more effective and efficient. Their LLM works in harmony with subject matter experts and machine data to give operators quick answers to maintenance questions and improve their experiences. Georgia-Pacific now estimates millions in potential savings from generative AI.

Value driver #2: Rethink business processes

Workflows inevitably become more profitable with greater throughput and improved quality. Data is every company’s ally here, identifying where AI can create efficiencies. But you also have to identify bottlenecks. As Schreiner says, “You don’t want to automate a bad process. To optimize workflows, companies must work backward from the customer experience and the employee experience. By proactively looking for ways to free up time for employees, they can focus on value-added tasks.”

Schreiner’s experience shows that aligning people across the organization is once again critical in this effort: business and technology teams must work together. As he explains: “IT cannot work in silos. Only business leaders know the questions that need to be answered, and therefore the data needed to answer those questions. Then you can properly determine which models, algorithms and technology you should use.”

“From the start, there needs to be a clear understanding of where data comes from, what it is used for and how it should be protected,” advises Schreiner. Prosper Insights & Analytics survey findings also show that 86 percent of employees are concerned about their privacy being compromised by AI’s use of data, and 85 percent of small business owners have the same concerns shares. To overcome these barriers, Schreiner recommends building enterprise-level security from the start: “This will protect sensitive data to avoid fines in the event of breaches, while also promoting trust in AI-driven results.”

With regulations in the works, Schreiner explains the importance of proving your control over generative AI systems by precisely defining the measure of success and putting in place guardrails to focus the solution on the task at hand is available. “Business applications shouldn’t be able to give you your grandma’s recipe for chocolate chip cookies. Be really intentional about what you want AI to do and what you do not If you want to do that, you avoid opening Pandora’s box and ensure that you only get contextually appropriate responses,” he explains.

Value Driver #3: Master the art of reinvention

Finally, Schreiner emphasizes that driving business value from generative AI means seizing the opportunities for creation with both hands. That’s because reinventing products, experiences and business models not only accelerates revenue, but also opens up entirely new revenue streams. “There is no shortage of ideas for using generative AI, but by figuring out how to increase their competitive advantage with AI, companies can really win by realizing higher sales, value per customer and value per average selling price,” he adds to it. .

It goes without saying that those who do not continuously improve the offering are putting their competitive position at risk. As Schreiner points out, “Retaining current customers and acquiring new ones means analyzing and responding to changing needs, especially as AI changes the way users interact with technology and get value from it.”

He continues: “For software companies, transforming customer experiences can mean developing new features and capabilities. But for traditional businesses, including SMEs, leadership should consider how to develop customer engagement. One example of this is the use of intelligent customer service agents. Instead of taking the time to manually look up answers, AI agents can relay answers to human agents as they call customers, streamlining solutions and removing friction for both the employee and the customer.”

Manage risks, maximize rewards

Schreiner’s overarching advice? “For the best chance of realizing business value, companies must choose viable projects where they have the skills to deliver tangible impact or find a partner to help. Quantifying the benefits of solving the problem for a given use case is critical for strong ROI and avoiding wasted time. For example, with the cloud you can simply pay for what you use, so that the benefits offset the costs. Technology partners can prove extremely valuable here, as they enable you to find the right opportunities to transform processes, services and the way your people work.”

It is clear that with all the possibilities that AI offers, prioritization becomes extremely important. To put it into perspective, McKinsey assessed more than 2,100 work scenarios to analyze the economic potential of generative AI – and there are many more to be explored. As Schreiner summarizes, “You could have the best AI tools at your disposal, but without strategic priorities, business alignment, and change management, you’re unlikely to get the results you hoped for.”