From Tech Consumers to Creators, AI Turns Every Leader into a Tech Creator: Genius or Gamble?

Over my 17+ years in Cybersecurity, I’ve witnessed a profound shift in how businesses engage with technology. Every industry, healthcare, finance, retail, manufacturing is now a tech industry. Companies are not just consumers of technology; many are becoming producers, driven by the accessibility and affordability of artificial intelligence (AI). This revolution is reshaping the role of business leaders, empowering them to solve problems creatively and build solutions in ways unimaginable a decade ago. But with this newfound power comes responsibility. Let’s explore this transformation, its upsides and downsides, and what it means for business leaders over the next 6 months to 2 years.

The Evolution: From Shopping to Building

Historically, business leaders faced a binary choice when addressing operational challenges: shop for a tech solution or abandon the process if none existed. Before the cloud era, deploying on-premises solutions required significant investment in infrastructure, often taking months and millions to implement. The cloud changed this, reducing deployment times to weeks and costs by leveraging scalable, pay-as-you-go models. SaaS platforms like Salesforce and Workday became go-to solutions, offering flexibility without the burden of hardware.

But what happens when no solution fits your unique problem? In the past, leaders had to make tough calls, scale back ambitions or shut down processes. Today, the AI revolution, fueled by LLM APIs (e.g., OpenAI’s GPT-4, Google’s Gemini), is flipping this paradigm. Non-coders can now build applications in minutes using low-code platforms and AI-driven tools.

As a business leader, you’re no longer limited to shopping; you can create. If a solution doesn’t exist, you can build one tailored to your needs and potentially sell it to others, turning a problem into a revenue stream.

The democratisation of AI has unleashed a wave of creativity, making every leader a potential innovator.

The Upsides: Unleashing Creativity and Agility

The ability of business leaders to build tech solutions offers transformative benefits:

1. Speed and Agility: AI-driven tools like Microsoft Power Apps or Google’s AppSheet allow leaders to prototype solutions in hours, not months. This agility aligns with the fast-paced demands of today’s markets, where 25% of startups in 2025 are AI-powered, many leveraging LLMs for rapid development (Springs, 2025).

2. Cost Efficiency: Cloud-based AI eliminates the need for on-premises infrastructure. For instance, deploying a custom chatbot using AWS Lambda and an LLM API costs a fraction of traditional software development—often under $1,000 for initial setups versus $100,000+ for on-premises systems (DataCamp, 2023).

3. Empowering Non-Coders: Platforms like Bubble or Adalo enable leaders with no coding expertise to create functional apps. This democratisation means a marketing head can build a customer analytics tool or a supply chain manager can design a route optimisation app, bypassing lengthy IT backlogs.

4. Innovation and Revenue Potential: Building a solution for your business can lead to scalable products. Take Duolingo, which started as a language-learning app and now uses LLMs for personalised education, generating over $500M annually (TechAhead, 2024). Leaders can turn internal tools into SaaS offerings, creating new revenue streams.

5. Tailored Solutions: Off-the-shelf software often requires compromises. AI-built solutions can be customised to specific needs, such as integrating Indic-language LLMs like OpenHathi for India’s diverse market, enhancing customer experience (Express Computer, 2024).

These advantages align with my observation: AI has spoiled business leaders with choices, making imagination the only limit. I’ve seen startups and enterprises alike leverage LLMs to address local challenges, from fraud detection to multilingual customer support.

The Downsides: Risks of Rapid Innovation

However, this empowerment comes with significant risks that business leaders must navigate:

1. Quality and Reliability: Non-coder-built apps often lack the robustness of professionally developed software. AI-generated code can contain errors or “hallucinations,” leading to unreliable outputs. For example, a healthcare app built hastily could misinterpret patient data, risking lives (CIO, 2025).

2. Security and Privacy: Cloud-based AI solutions process sensitive data, amplifying risks of breaches or unauthorised access. Public cloud environments, used by 80% of AI startups, face heightened scrutiny for data privacy (Nutanix, 2024). Without proper encryption or access controls, a leader’s custom app could expose proprietary data.

3. Vendor Lock-In: Relying on specific LLM APIs or cloud providers can trap businesses in ecosystems, limiting flexibility. Switching providers often incurs significant time and cost, especially if proprietary data formats are involved (DataCamp, 2023).

4. Ethical and Compliance Risks: LLMs can perpetuate biases in training data, leading to unfair outcomes. For instance, a hiring tool built with an LLM might inadvertently discriminate if not audited for bias. Regulations like the EU AI Act (enforced August 2024) mandate transparency and fairness, with fines up to 7% of global revenue for non-compliance (Medium, 2025).

5. Maintenance and Scalability: Non-coder apps often lack scalable architectures. A solution built for 100 users may crash under 10,000, requiring costly reengineering. My experience securing cloud migrations highlights this: 30% of rushed deployments fail scalability tests (personal observation, 2023–2024).

These risks underscore a critical question: Is it safe for business leaders to build their own solutions? The answer depends on execution. With proper governance, testing, and expertise, it’s not only safe but transformative. Without these, it’s a gamble.

Is It Safe? A Balanced Approach

Building tech solutions as a non-coder is safe if leaders adopt a structured approach:

• Leverage Expertise: Partner with IT and cybersecurity teams to ensure secure, compliant designs.

• Use Trusted Platforms: Opt for reputable low-code platforms with built-in security, like Salesforce Flow or AWS Amplify, which offer compliance with standards like SOC 2.

• Test Rigorously: Validate AI-generated code for accuracy and scalability. Tools like Selenium or Postman can automate testing, catching common errors.

• Monitor Compliance: Align with regulations like India’s DPDP Act or the EU AI Act. AI governance frameworks, such as NIST AI RMF, provide checklists for ethical AI use.

• Start Small: Pilot solutions on non-critical processes to minimise risk. For example, automate internal reporting before tackling customer-facing apps.

With these guardrails, leaders can safely harness AI’s potential. My view, the AI revolution empowers leaders, but success hinges on balancing creativity with discipline.

The Future: Business Leaders as Tech Creators

Looking ahead 6 months to 2 years (May 2025–May 2027), business leaders who embrace solution development will thrive, but their needs and challenges will evolve:

• 6 Months (Nov 2025): Leaders will demand user-friendly AI tools with stronger guardrails. Low-code platforms will integrate automated compliance checks, reducing regulatory risks by 20% (projected, PwC 2024). Small language models (SLMs), like Google’s Gemma, will gain traction for task-specific applications, offering cost-effective alternatives to LLMs (CIO, 2025).

• 1 Year (May 2026): Hybrid cloud adoption will surge, with 60% of enterprises using multi-cloud strategies to avoid vendor lock-in (AceCloud, 2024). Leaders will seek AI-as-a-Service (AIaaS) platforms that simplify model training, enabling custom solutions without deep technical expertise. Ethical AI will become a priority, driven by regulations like India’s AI Mission.

• 2 Years (May 2027): Quantum machine learning and edge AI will emerge, enabling real-time, localized AI processing for industries like healthcare and logistics (Open OCO, 2024). Leaders will need tools to integrate these technologies seamlessly, with 30% of AI budgets allocated to edge deployments (Gartner, 2025). Sustainability will also drive demand for “Green AI,” minimising energy use in model training.

The future favours leaders who blend business acumen with tech fluency. Those who master AI-driven development will disrupt markets, much like startups leveraging LLMs for SaaS in 2023–2025 (Springs, 2025). However, they’ll face pressure to upskill, as 98% of executives see AI as central to strategy by 2028 (Accenture, 2023).

Emerging Tech Needs and Challenges

Business leaders will prioritise the following in emerging tech:

1. Simplified AI Tools: Platforms that abstract complexity, like autoML or no-code AI builders, will be critical. Leaders want to focus on outcomes, not algorithms.

2. Robust Security: With 50% of organisations citing data privacy as an AI barrier (Hyperstack, 2024), leaders will demand end-to-end encryption and compliance-ready platforms.

3. Cost Transparency: Pay-as-you-go models must clarify long-term costs to avoid budget overruns, especially in multi-cloud setups.

4. Ethical AI Frameworks: Tools to audit and mitigate bias will be non-negotiable as regulations tighten.

5. Scalable Infrastructure: Solutions must handle exponential growth, with 70% of top-performing firms adopting cloud-native architectures (PwC, 2024).

Challenges include skill gaps (40% of firms lack AI expertise, Statista 2024), regulatory complexity, and managing AI’s environmental impact. Leaders who address these will lead the next wave of innovation.

Call to Action: Embrace the Creator Mindset

The AI revolution has turned business leaders into tech creators, limited only by their imagination. But with great power comes great responsibility. Build with purpose, secure with rigour, and innovate with ethics. Whether you’re solving a niche problem or launching the next SaaS unicorn, the tools are at your fingertips. Let’s shape a future where technology serves business and humanity alike.

#wethehumans