Business leaders are eagerly expecting generative AI to jumpstart productivity and create a more engaged workforce. However, the reality of AI implementation often falls short of the hype. Some companies are already getting real value and better performance. For others, the results are less apparent.

The question is why.

Explaining the expectations gap

Recent surveys shed some light on this problem. While 96% of C-Suite leaders are confident that generative AI (GenAI) will improve productivity, this optimism isn’t shared by everyone. Over half of the general public is nervous about the impact, and about 75% of employees believe it may increase their workload.

Why is there a gap between expectations and reality? In a word, execution. Too many businesses have embraced a spray-and-pray approach that bolts an AI model onto their organization and waits for it to perform some magic.

Given this approach to GenAI, it shouldn’t come as a surprise if some workers feel less productive, especially when they are given the wrong GenAI solution along with insufficient time, training, and skills.

AI at work

While GenAI amazes people with its ability to answer questions and create images, to be truly transformative, it has to justify its investment in the workforce and deliver clear results.

Fortunately, the last two years of trial and error have taught us a lot about the best way to achieve these results.

“There have been a lot of hammers looking for nails,” Louise Herring, a partner with QuantumBlack AI by McKinsey, said in a recent interview.

“To achieve value from GenAI, organizations need to take a top-down approach to align their strategy with business goals that will make a difference,” Herring says. “It can’t be treated narrowly as just a tech initiative; it needs sponsorship, ownership, and accountability at all levels, especially from P&L owners.” Lastly, she says, “Businesses need to keep it simple. They should focus on scaling with clarity and efficiency rather than creating too much complexity in the tech stack or across projects.”

Let’s look at some examples. Last year, accounting firms reported healthy increases in revenue and profitability. However, several challenges are emerging that could impede that growth, namely, the need to retain good talent and meet tightening deadlines.

GenAI is already helping with both challenges at Johnson Lambert LLP, an accounting and consulting firm specializing in the insurance industry. Their work is incredibly labor-intensive, often requiring 60 to 80 hours per audit. They determined that saving time during audits would free up their staff for more rewarding strategic and customer-focused tasks. They turned to Cohere for a tailored GenAI solution.

They used Cohere Command to extract data from unstructured PDF files. A suite of validation tools ensured accuracy and a user-friendly interface (a vital part of winning worker acceptance) provided smooth interactions between auditors and the AI.

The result? Audit time was cut by 50% while efficiency improved by 20%. Johnson Lambert was able to get its GenAI solutions up and running in less than two months, thanks to rapid prototyping and onboarding.

Seamless and secure

Very real concerns about the implications of giving AI systems access to sensitive company data can paralyze the rollout of solutions, with security executives and workers wary of mistakes. Progress is being made here as new partnerships between AI companies and cloud providers create increasingly secure GenAI services for organizations worldwide.

Oracle, for example, has made data privacy and security central to its move to seamlessly integrate GenAI into its customers’ existing workflows. Using Oracle Cloud Infrastructure, customers can securely incorporate their data to train specific models and deploy them on AI infrastructure. The result is better functionality while giving customers control over their data governance.

Another option is private deployments via on-premise (on-prem) or virtual private cloud (VPC). Businesses can ring-fence their data to enhance security and meet the growing regulations around sensitive data and how it is used with GenAI.

When employees are provided with these tailored, secure solutions, they are relieved of mundane and tedious tasks and concerns about the technology’s safety. This empowers them to pursue more creative activity, which can lead to a virtuous cycle of improved productivity, job satisfaction, and talent retention.

Delivering results

Some businesses have struggled to get the most out of GenAI with poorly suited products, little or no onboarding, or insufficient training. This has created a gap between what leadership expects and what workers can achieve.

While some businesses' rush to embrace GenAI has left some workers feeling confused and overworked, it doesn’t have to be this way. There are enterprise AI solutions available that are designed to immediately impact productivity.

GenAI, designed this way, is increasingly delivering results where it matters most. The benefits are emerging across a growing range of key companies and industries, from healthcare and software to insurance and logistics.

Thanks to rapidly improving solutions and adoption, most businesses can realize those benefits now—with sharply focused, carefully customized, and secure solutions that address specific problems and opportunities.

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