How leapfrogging with AI can empower lagging enterprises

Industries like healthcare and financial services are limited by regulation and outdated technology. Is AI the answer to real transformation?

The enterprise world has been talking about digital transformation for what feels like forever. For industries formed in the pre-Internet age of computing, with operating models that focus on physical assets and capabilities rather than information and technology, digital transformation represents a long, incremental process.

The pandemic quickly exposed the chasm between digital-native companies and traditional laggards that still relied on analog processes. Consider how healthcare companies and government agencies scrambled (and still struggle) to catch up to B2C-level delivery of mobile user experiences, personalization, and digital payments.

Now, as we witness the advance of AI-driven automation, enterprises face a tectonic shift in how business is conducted. The choice facing enterprise leaders is stark—lag behind, slowly play catch-up, or potentially leapfrog ahead into disruptor territory.

What is leapfrogging?

In the context of business innovation, "leapfrogging" refers to the strategy of bypassing traditional stages of development or technology to adopt more advanced or innovative solutions. This often happens in emerging markets where companies or countries skip older technologies entirely to implement cutting-edge alternatives. For example, a less-developed nation might move directly to mobile banking without first implementing extensive banking infrastructure. Leapfrogging allows businesses to gain competitive advantages and improve efficiency by leveraging the latest innovations.

The appetite for leapfrogging is huge

Enterprise leaders are well aware of AI’s transformative potential. A recent report by Arize finds that over half (64.6%) of Fortune 500 companies mention AI in their most recent annual report. And more than one in five enterprises specifically reference generative AI. However, over two-thirds (69.4%) of companies mentioning AI also mention risk disclosures. That can range from risk through the use of emerging technology, failing to keep pace with competitors using AI, or security risk to the business.

Even so, the value of change is outweighing the burden of change, and use cases for AI are emerging among highly regulated industries. For example, in healthcare, doctors use the technology to improve diagnosis accuracy in image scanning, and in real estate, AI is used to create more reliable property valuations.

One advantage established enterprises have over newer firms is that they are usually sitting on a vast amount of proprietary data. GenAI can be trained on these data stores to uncover patterns and trends, build agents around them, and bubble up valuable insights for a competitive edge.

Navigating obstacles

Ambitions for implementing AI are often met with the complexities of legacy tech systems. According to a survey of IT leaders by SnapLogic, nearly one-third of respondents said up to 25% of their legacy systems are unable to support AI tools and workloads. Technical debt, outdated code, and costly patch fixes are common challenges in traditional digital ecosystems.

Other roadblocks can stem from multiple barriers such as governance and privacy, technological lift, skillset deficiencies, or strategic misalignment. Typically, most companies face a combination of these components.

But there’s also movement: we do see large organizations exploring AI use cases internally, often with low-risk, internal applications that are not customer-facing. By starting with small pilot programs, businesses can start to automate routine tasks, streamline processes such as document management, and quickly develop new products.

Making the jump

Leapfrogging will require deliberate action, but the opportunities are vast. Imagine a future where your organization has computer vision capabilities for all critical documentation, where all of your knowledge workers have an AI assistant that supports routine work. Rather than spending hours on form completion workflows, workers can be focused on complex decision-making, improving the customer experience, and building relationships.

So how can enterprise leaders get a jumpstart on AI? At Modus, we recommend starting with an AI maturity assessment to understand your holistic capabilities across departments and build a sustainable roadmap. We also recommend workshops to quickly get stakeholders up-to-speed, ideate use cases, and prepare strategies.

Orchestrating AI at the enterprise level requires an understanding of the need for security, compliance, and governance, as well as change management. Companies need to prioritize challenge resolution because the innovation curve on AI hasn't receded but rather is spawning new continual innovation. The risk of falling behind has exponential implications which is why at Modus, our digital transformation consulting and AI services promote meaningful improvements in a condensed period of time.

The opportunity that AI presents for enterprises to leapfrog their competition is a significant one. Organizations that view this shift as an advantage rather than something to fear will be poised for near-term success and long-term value.