Companies that view gen AI as a productivity tool are making a misguided decision.

• A recent study by Genpact and HFS Research reveals that only 5% of companies have successfully implemented mature generative AI initiatives, while 45% have postponed their investment in this technology.
• According to research, approximately half of executives view AI technology as a tool solely for increasing productivity.
• In order to transition AI from a trial phase to full-scale implementation, businesses must ensure that their AI strategies align with their specific business goals.

Although there is much excitement about generative artificial intelligence, many companies have not yet made significant investments in the technology. This could be a good thing for the C-suite, as it allows them to prepare their companies for widespread use of AI tools and services before making a financial commitment.

A research report by Genpact and HFS Research reveals that only 5% of senior leaders at global organizations claim their companies have fully developed gen AI initiatives, while 45% are postponing investment and adopting a wait-and-watch strategy.

Sreekanth Menon, global leader, AI and machine learning at Genpact, stated that part of the slow spending approach on gen AI is due to senior executives misinterpreting it as a productivity tool, according to research that showed about half of the executives hold this view.

""The reluctant budgetary spending is due to the myopic vision of gen AI's potential, which is a result of its infancy and a warped conception of its capabilities," Menon stated."

Don't focus on quick wins

Paul Pallath, vice president of the applied AI practice at technology consulting firm Searce, stated that AI often leads to substantial technology and process debt, which becomes costly to manage as organizations grow.

"Pallath stated that only a few organizations have successfully utilized AI at scale and disrupted the marketplace effectively because the true potential of AI will never be fully realized if organizations prioritize short-term goals or solely focus on quick wins without a long-term strategic plan."

According to research by Genpact and HFS, business leaders are allocating up to 10% of their IT budgets to AI projects. However, concerns about data governance, talent shortages, and proprietary data accessibility have led to low spending and have made it difficult to transition from pilot to production, Menon stated.

Before deploying AI across business functions, companies need to assess their readiness and identify the right use cases.

To successfully transition AI from a trial phase to implementation, companies must prioritize their AI plan in line with specific business goals, not just productivity, Menon stated. Many organizations are hindering the implementation of successful, long-term AI strategies because they are too fixated on its potential to increase productivity, rather than its broader advantages.

Menon stated that concentrating on the larger perspective would increase the likelihood of future success for organizations.

Pallath stated that AI is transformative and necessitates a thorough reassessment of existing business processes, data strategies, technology platforms, and people strategies.

To successfully implement AI, it is necessary to simplify and revise business processes with an AI-first mindset, according to Pallath. However, change management and governance are essential to ensure that the entire organization is prepared for and engaged in this transformation. Often, employees focus more on the potential impact of AI on their jobs, which can hinder the necessary changes in process to make AI successful.

Strong leadership support is crucial for AI initiatives to overcome inertia and secure the necessary resources, as Pallath stated. Without a clear vision from the top, AI projects are more likely to get stalled or diluted.

A dedicated AI team, led by a chief AI officer, can help ensure success by prioritizing AI as a top priority and championing its integration into the company.

To design, build, and deploy predictive models and algorithms, an AI team may consist of data scientists, machine learning engineers, an AI specialist with domain expertise, and software engineers. However, as companies create specialized teams, they must also prepare their existing workforce for a future where AI is widespread. This includes educating everyone in the company about the significance of data, ethical data usage, and how it is being utilized within the business.

Establish a clear view of responsible AI

To ensure responsible and ethical AI considerations are understood across the organization, companies must begin their AI journey with a clear vision of responsible AI.

They should establish a framework for responsible AI that outlines a roadmap to achieving responsible AI, incorporating essential elements such as privacy/security, reliability/safety, and explainability/traceability.

To establish a culture where responsible AI is at the forefront, companies must prioritize building awareness of responsible AI, assess which processes will be impacted by gen AI, and take proactive measures to address legal, security, or ethical issues, according to Menon.

To achieve business gains from gen AI within two years, companies must address challenges such as data quality and strategy, as highlighted in the research report, which underscores the urgent need for a robust data strategy.

"Pallath stated that although AI is a transformative technology, its success is largely contingent on the accuracy of the data. Many organizations face challenges in establishing a comprehensive data strategy that incorporates effective governance and quality procedures. Often, data is neglected, and investments in data management are perceived as expenses rather than catalysts for growth. This perspective results in data overload trapped in isolated repositories, impeding the creation of robust AI solutions."