The Race to AI Maturity: Closing the Execution Gap

Artificial intelligence captivates business leaders with its potential to transform entire industries. Early adopters, in particular, race to harness AI, hoping to win a competitive advantage through early implementation. However, as research by Deloitte reveals, leading with adoption alone risks disappointing results. To fulfill AI’s immense promise, companies must balance exuberance with strategic execution.

Deloitte’s survey of 1,100 technology and business executives exposes a sizable “execution gap” in current AI initiatives. The findings highlight surging AI adoption among early adopters. Fifty-eight percent have launched multiple implementations, sharply up from 32% in 2017. Investment is also rising, with 37% investing over $5 million so far.

This enthusiasm stems from AI’s perceived benefits. Eighty-two percent of respondents reported positive returns on AI investments to date, in areas from products to processes to decisions. In fact, 63% characterized AI adoption as necessary just to stay competitive. AI, it seems, must be embraced or risk obsolescence.

However, early successes may breed overconfidence. Strategic execution has failed to mature in parallel. Key indicators of operational discipline are sorely lacking. Only half of respondents regularly measure crucial performance metrics like ROI or productivity impacts. Just 37% have an overarching AI strategy in place.

Even more troubling, executives rank implementation itself as AI’s greatest challenge. They struggle to properly integrate AI solutions into business roles and functions. Often, they cannot clearly define success or prove business value. Rather than a methodical strategy, these early AI forays resemble haphazard experimentation.

But the biggest red flag is the lack of cybersecurity vigilance. Despite ranking it their top AI risk, under 50% of companies are actively building security into AI projects. One-third have already suffered AI-related breaches. And 20% declined to pursue AI altogether due to cyber concerns. With data serving as AI’s lifeblood, cybersecurity cannot be an afterthought.

The cloud’s democratising force has fueled wider AI adoption. By offering affordable access to vast computing power, cloud services let even small firms launch AI initiatives cost-effectively. The cloud’s on-demand nature also enables rapidly scaling AI capabilities as needed. No wonder the AI-as-a-service market grows at a 48% annual clip. Already, 59% of early adopters use cloud-based AI. We can expect this trend to accelerate.

However, the cloud’s convenience may jeopardize cybersecurity. In rushing to capitalize on AI’s potential, some firms neglect proper cloud safeguards. Migrating sensitive data to public clouds without stringent controls poses a danger. Attackers have proven adept at infiltrating cloud-based AI models and hijacking their insights. As AI initiatives expand in the cloud, so too will their attack surface. Maintaining legacy security postures leaves firms dangerously exposed.

So what must be done to close this execution gap – to transition from bullish experimentation to mature strategy?

1. First, install the operational disciplines governing other enterprise-class technologies.

2. Measure impacts rigorously and hold leaders accountable for ROI. Make cybersecurity an equal priority during development, not an afterthought.

Next, pursue targeted, strategic AI opportunities rather than sporadic applications. Patchwork AI across silos cannot enable transformation. Look beyond IT to mission-critical processes across the business. Areas like finance, marketing, and product design have urgent needs ready for AI’s contribution.

Finally, balance technical talent with business leadership. The most sophisticated adopters already have acute AI talent shortages. However, overemphasising data scientists ignore critical integrative skills like project management and change leadership.

The early days of enterprise AI have surfaced promising results. But to fulfill AI’s immense potential, leaders cannot outpace their execution capabilities. Maturing AI requires maturing management, from strategy to staffing to security. With focused game plans, cybersecurity vigilance, and operational discipline, executives can build on early enthusiasm to steer AI toward the transformative role it is destined to play.

Reference:

DI_State-of-AI-in-the-enterprise-2nd-ed.pdf (deloitte.com)