INSIGHTS
INSIGHTS

The Missing Middle

The Missing Middle

The Missing Middle

Jeffrey Tjendra

Oct 10, 2024

#enterpriseai #midmarket

#enterpriseai #midmarket

#enterpriseai #midmarket

The Missing Middle
The Missing Middle
The Missing Middle

Large enterprises are at the forefront of AI implementation, but the other crucial segment is being left behind.

Large enterprises are at the forefront of AI implementation, but the other crucial segment is being left behind.

The “it” growth mechanism of 2020’s is undoubtedly AI.

In the 2010s, every company needed to become a tech company - which drove the digital transformation era. 

The Era of AI

By 2020, global data generation skyrocketed to 64.2 zettabytes, up from just 2 zettabytes in 2010 - which created a foundation for AI maturity.

Now, in the 2020s, every company must evolve into an AI company.

An “AI company” or “AI-first company” or “AI-driven company” utilizes AI as a core component in its strategy in order to gain competitive advantage.

These companies don’t just treat AI as a tool; instead, they build their entire business models, products, and processes around AI-driven insights and automation.

By 2025, it's estimated that AI-first companies could increase profitability by 38% across industries.

Companies that don’t use AI will get out-competed by those that use AI, and therefore cannot afford not to use AI for its:

  1. Massive effectiveness gain in elevating the product experience.

  2. Massive efficiency gains in how companies operate 

  3. Massive productivity boost in how the workforce do their jobs.

  1. Massive Effectiveness Gain

Medical institutions are increasingly adopting AI to enhance the effectiveness of their practices. 

For example, AI systems for breast cancer detection have demonstrated accuracy rates of up to 94%, improving early detection by 30% compared to traditional methods. This technology can reduce false positives by 11%, leading to more precise diagnoses and better patient outcomes.

  1. Massive Efficiency Gains

Palm oil producers in Indonesia and Malaysia, which account for approximately 85% of global palm oil production, are increasingly leveraging AI to manage their plantations more efficiently. 

For example, AI is being used to grade the ripeness of palm fruits for harvesting, improving yield prediction accuracy by up to 90%, and reducing labor costs by as much as 20%.

  1. Massive Productivity Boost

Analysts from investment firms are increasingly implementing AI to streamline their processes. In fact, 70% of investment firms now utilize AI for data analysis, and AI-driven investment strategies have been shown to outperform traditional methods by up to 8%. 

By allowing AI to handle the heavy lifting in analyzing market trends and making investment decisions, firms can reduce research time by 40% and improve decision-making accuracy by as much as 20%.

Large enterprises are going all-in on AI. In fact, 63% have already exceeded their AI budgets due to unexpected scaling costs, with enterprise-wide AI initiatives typically running 30-50% over budget. Despite the significant CAPEX, 85% of executives believe that AI will give their company a competitive edge, driving increased investment across the board.

Underserved Market

However, we are at a critical juncture where mid-sized enterprises, which contribute around 50% of global GDP, are being left behind in the AI race. 

While 76% of large enterprises have adopted AI in some capacity, only 23% of mid-sized firms have implemented AI solutions, largely due to budget constraints and a lack of technical expertise. 

This gap risks widening the competitive divide, leaving a significant portion of the economy struggling to keep pace with AI-driven innovation.

Mid-sized enterprises are being underserved in the AI era, just as they were in the "digital" era of the 2010s.

We’ve spoken to numerous companies around the world, and the problems they face are clear:

  • Information fragmentation: 67% of mid-sized businesses report difficulty in locating specific information due to scattered data.

  • Heavy manual analysis: Over 60% of employees, not just analysts, spend significant time on manual data analysis, resulting in inefficiencies.

  • Data sprawl: A decade of data, spread across various tools, leads to inefficiencies. Research shows that data professionals spend 80% of their time searching for and preparing data, rather than analyzing it.

  • Siloed data across tools: Employees are often limited to searching within a single tool at a time, hindering productivity.

  • Stale data: 45% of mid-sized companies report that their data is often outdated, lacking real-time feedback loops.

  • Obfuscated tools: Employees frequently struggle to identify where to search for relevant data.

  • Engineering search in tech departments: While tools like Wiki/TechDocs search have made inroads, they still leave gaps in broader enterprise-wide search capabilities.

These companies want to implement AI models, at least for productivity gains , with 53% indicating that AI could reduce repetitive tasks by up to 20%. 

However, they resort to uploading company data into personal accounts, creating a risk-prone environment of Shadow AI users.

Their best current option is subscribing to pro plans of tools like ChatGPT. Unfortunately, the big players like Microsoft and Google don’t engage with them, as their AI budgets fall below the minimum deal size.

While major AI providers overlook them, mid-sized enterprises need the most help. 

That’s why we at Shieldbase are democratizing AI tools, making it easier and safer for mid-sized businesses to adopt AI. This ensures they have the opportunity to compete in the AI-driven economy - leveling the playground by giving them access to enterprise AI capabilities without the enterprise-level budget.

Make AI work at work

Learn how Shieldbase AI can accelerate AI adoption with your own data.