Companies that view AI as a productivity tool are making a mistake

Despite all the fuss around generative artificial intelligence, many organizations have yet to make significant investments in the technology. But that may not be a bad thing, as it gives executives an opportunity to prepare their companies for the widespread use of AI tools and services before committing money.

According to a new research report from professional services firm Genpact and research and analytics firm HFS Research, only 5% of senior executives at global organizations say their companies have achieved mature AI initiatives, while roughly 45% are postponing investments and taking a wait-and-see approach.

Sreekanth Menon, global leader of AI and machine learning at Genpact, said one reason for the slow spending is that senior executives have the wrong view of Gen AI: The survey found that about half of executives see it only as a productivity tool.

“This is an example of shortsightedness,” Menon said. “Generational AI is still in its infancy, and combined with misconceptions about its potential, has led to reluctance to spend.”

When it comes to AI, organizations often focus on short-term, low-hanging fruit, “resulting in significant technology and process debt that becomes more expensive to manage as the organization grows,” said Paul Paras, vice president of applied AI at technology consulting firm Searce.

“This is why very few organizations are able to leverage AI at scale and effectively disrupt their markets,” Pallas says. “If organizations pursue short-term goals or focus only on immediate gains without long-term strategic planning, the true potential of AI will never be realized.”

A Genpact and HFS survey found that business leaders are dedicating up to 10% of their IT budgets to Gen AI projects, but factors like data governance concerns, talent shortages and access to proprietary data are slowing spending and widening the barriers between pilot and production, Menon said.

So what do companies need to do before implementing AI across their business functions?

One is getting clarity on priorities, Menon says. “To successfully move AI from pilot to production, organizations need to stop and ensure their AI generational plans are aligned with specific business goals, not just productivity,” he says. “Many organizations focus too much on what it can do to improve productivity, rather than the broader benefits of AI, limiting the deployment of a successful long-term AI strategy.”

Taking the time to focus on the bigger picture will position the organization for more success in the future, Menon said.

AI is transformative and requires a comprehensive reevaluation of current business processes, data strategies, technology platforms and talent strategies, Pallas said.

“To effectively adopt AI, business processes must be simplified and modernized with an AI-first mindset,” says Paras. “Effective change management and governance are essential to ensure the entire organization is prepared for and on board with this transformation.” Too often, says Paras, employees worry about how AI will impact their work, rather than how to leverage technology to work smarter, preventing them from making the process changes necessary to make AI successful.

Executive leadership and sponsorship are also important. “AI efforts need strong leadership support to overcome inertia and acquire the necessary resources,” says Pallas. “Without a clear vision from the top, AI projects are likely to stagnate or weaken.”

A dedicated AI team led by a chief AI officer will help ensure success. “A dedicated leader, ideally at the C-suite, will keep AI as a top priority and drive its integration into the enterprise,” says Paras. “AI requires significant investments in technology, people, and infrastructure. Strong leadership will prioritize these resources and allocate them effectively to maximize return on investment.”

An AI team could include data scientists, machine learning engineers, AI specialists with domain expertise, and software engineers who design, build and deploy predictive models and algorithms. And while building specialized teams, companies should prepare their existing workforce for an AI-driven future, Pallas said. This includes informing everyone in the company about the importance of data, how to use it ethically, and how data is being used within the business.

Another good way is to establish a corporate culture of responsible AI: “Companies need to start their AI journey with a clear understanding of responsible and ethical AI considerations and ensure they are understood across the organization,” says Menon.

To achieve this, we need to create a Responsible AI Framework that provides a map for achieving responsible AI, including key components such as privacy/security, trust/safety, and explainability/traceability.

Beyond the framework, to create a culture around responsible AI, Menon said companies need to raise awareness of responsible AI, identify the processes that gen AI will impact, and take steps to mitigate legal, security and ethical concerns.

Finally, data quality will be critical to the success of Gen AI. As the research report notes, companies hoping to realize business benefits from Gen AI within two years are grappling with challenges including data quality and strategy, highlighting the urgent need for a robust data strategy.

“AI is a disruptive technology, but its effectiveness relies heavily on the quality of data,” says Pallas. “Many companies struggle to develop a comprehensive data strategy that includes proper governance and quality processes. Data is often an afterthought, and investments in data management are often viewed as a cost rather than a revenue driver. This mindset leads to a swamp of data buried in silos that impedes the development of high-quality AI solutions.”