Your Company Needs AI Right Now…Or Does It? The Myths and Realities of AI Adoption

In its 2023 report, McKinsey & Company optimistically predicted that artificial intelligence will annually add between $17.1 and $25.6 trillion to the global economy. Undoubtedly, this technology holds great potential for companies. When applied where appropriate, it can automate mundane routines and boost efficiency. However, AI is often marketed as a silver bullet capable of solving any business challenge. Companies are swayed by these compelling pitches and pursue AI integration without having a clear goal, well-structured proprietary data, and well-oiled processes. In this article, we’ll explain where it truly delivers ROI, and where, conversely, adopting this costly and complex solution is unwarranted.

Some statistics on AI adoption in companies

According to McKinsey & Company and its division focused specifically on AI, QuantumBlack, as of mid-2025, world-famous technology behemoths have poured more than $155 billion into AI-related innovation. Hard to believe, but this sum exceeds the U.S. government’s yearly budgets for education, labor, and social initiatives.

No wonder that, given the emergence and rapid evolution of AI programs, companies are actively implementing this technology—78% have already put it to work in at least one area of their business.

So, the hype around AI is real. What about the value it delivers? Surprisingly, only 11% of companies have reported considerable returns on their AI initiatives, while only 15% of all AI projects make it to full-scale implementation.

Driving business value with AI: applications that yield impressive results

Andersen’s experts have helped dozens of customers in healthcare, finance, manufacturing, retail, and other major industries embed AI into their workflows. We provide AI consulting services to organizations worldwide. Therefore, we can speak with authority about where this technology truly delivers value.

Based on our experience, AI is an effective solution for well-defined, data-intensive, and repeatable tasks. Conversely, without a clearly defined business case and sufficient data, it fails to deliver measurable outcomes.

Below are critical industries where AI tools, in the right context, can deliver significant ROI:

• Manufacturing: Our customers in the automotive, electronics and semiconductors, pharmaceuticals and biotechnology, food and beverage production, aerospace and defense, apparel manufacturing, heavy machinery and equipment, and other sectors have experienced the transformative power of AI on their manufacturing lines. Popular use cases include robotic automation, process engineering, production planning, demand forecasting, inventory management, resource allocation, predictive maintenance, and quality control.
• Banking and fintech: Commercial and investment banks, wealth management departments, insurance companies, payment processing divisions, firms handling cryptocurrencies and digital assets, providing lending and credit services and ensuring RegTech and compliance have proved to avail themselves of AI-powered software. They use it for advanced analytics, risk management, regulatory compliance, customer onboarding and KYC, fraud prevention, investment advisory, personalized customer service, credit analysis and underwriting, anti-money laundering, and conversational chatbot analytics.
• Sales and marketing: Firms specializing in e-commerce and retail, technology and software, real estate and property, travel and hospitality, etc., employ AI-fueled software to efficiently handle tasks related to sales automation, digital marketing, customer journey optimization, lead generation, scoring, and nurturing, campaign management, social media and influencer analytics, content generation and optimization, hyper-personalization engine, customer segmentation, and virtual personas for product testing.
• Healthcare: Hospitals, diagnostic imaging centers, pharmaceutical companies, medical device manufacturers, telemedicine and remote monitoring providers, insurers, medical education institutions, and public health organizations are actively employing AI for their needs. Sought-after use cases include clinical operations, medical diagnosis and imaging, patient care management, patient recovery monitoring, medical education and training with AR simulations, healthcare administration, pharmacy and medication management, e-receipts, quality assurance and safety, population health management, and personalized treatment plans.

The aforementioned use cases yield tangible outcomes in case there are centralized, well-organized proprietary data pipelines (which are three times more likely to yield strong ROI), a clearly outlined business challenge, and repeatable processes.

When AI falls short: the importance of data and process readiness

According to Capgemini, in most projects, insufficient data maturity is the major roadblock to AI adoption. A mere 12% of organizations admit their data is optimized for AI use. No wonder that only slightly more than half of AI initiatives (53%) reach production, and a mere 22% subsequently scale.

AI doesn’t deliver the desired outcomes without consistent processes and clearly formulated business challenges in place; in other words, if the underlying foundation is weak. Furthermore, this technology is inefficient in compact teams and for highly specialized B2B services and emerging products.

So, is your company fully prepared for AI implementation yet?

Statistically, 80% of business leaders underestimate the infrastructure needed for AI. We at Andersen are aware of that and thus, advocate for ensuring that the software we build meets a specific business need and feeds on quality data.

To guarantee the feasibility of AI integration, our experts first audit our customers’ infrastructure and evaluate its architectural readiness, including data flows, APIs, and scalability. Our teams then outline the real value the software will deliver for each business case and evaluate it against the complexity of the proposed solution. If the customer’s infrastructure isn’t prepared for AI implementation yet, we recommend that they prioritize data and systems readiness before proceeding.

As an accomplished AI software development company, we believe that only by having this systemic approach to AI adoption can we develop architecturally reliable solutions where AI is embedded into business processes, integrated with data flows, compliant, and secure.