Why AI fails in business: Eight reasons companies struggle with AI implementation

Every company seems to want to use AI. According to Cisco’s second annual AI readiness report, 98% of executives felt an increased urgency to deliver on AI. Many businesses are already implementing it in some way, but based on the findings of the RAND report, more than 80% of AI projects fail. In this article we look at why AI fails in business, and the steps that can be taken to help drive better outcomes.

The promise of AI vendors is that it is transformative. Businesses look at glossy marketing and well-crafted demos and think, wow, it’s new, it’s shiny, it’s going to solve all my problems.

You will undoubtedly have seen all your existing tech vendors wedge AI into their product over the last year or so. They now promise the earth – enhanced efficiency, data-driven decision-making, increased revenue generation and even entirely new business models.

If it’s all so easy and such amazing benefits are on offer, why are so many businesses unable to implement AI correctly?

1. Lack of a clear AI strategy

Many companies jump on the AI bandwagon without a well-defined AI strategy. In Microsoft’s recently released Agents of Change report, 54% of leaders admitted their organisation lacks a formal AI strategy.

Leaders see competitors using AI and assume they need it too – without understanding why or how. This is the number one killer of AI success: starting with the tech and trying to find a problem to fix.

How to avoid this pitfall:

• Assess your AI readiness – Take our quick AI readiness assessment to get a snapshot of where your business stands.
• Map your use cases – Identify real business problems AI can solve.
• Create an AI strategy – A simple plan should outline:
  – Your AI vision and principles
  – Mapped and prioritised AI and automation use cases
  – A clear action plan to increase AI maturity
  – Align AI with your business goals – AI should support your strategy, not exist in isolation.

Without a strategy, AI initiatives become aimless experiments.

2. Underestimating change management

AI isn’t just a technology issue – it’s a fundamental shift in how people and businesses operate.

From our staff survey data across all our clients, 70% of employees fear job losses due to AI.

Less than 10% of employees have received proper AI training, and only 16% are satisfied with their company’s AI communication.

If employees feel unprepared and threatened, they won’t support AI adoption.

How to manage change effectively

• Start communication early – Involve employees in use case identification and communicate early about what you are doing.
• Focus on AI augmentation, not replacement – Make it clear AI supports their jobs, rather than eliminates them.
• Get leadership buy-in – Without executive support, AI initiatives won’t get the budget or traction needed.

3. Overreliance on vendors

Many companies outsource AI implementation entirely, assuming AI is too complex to manage in-house. While third-party expertise is valuable, excessive reliance increases costs and risks.

How to build internal capabilities:

• Develop internal AI capabilities – Train your team on low/no-code tools like Power Automate or Copilot Studio.
• Train your whole team on AI – AI isn’t just for IT; every person in every department can benefit.
• Balance vendor support with internal control – Avoid vendor lock-in and unnecessary costs by using your people.

4. Expecting instant results

AI is not magic. It requires time, training, and iteration to deliver value.

Many businesses roll out AI tools (e.g., Copilot) without training and then wonder why adoption is low.

Gartner research indicates that 85% of AI projects fail to deliver expected outcomes, often due to unrealistic expectations and lack of proper implementation.

AI ROI isn’t just about time saved or revenue generated – it includes benefits like better decision-making and customer retention. Read our AI ROI guide for more insights on how to measure ROI.

Set realistic expectations:

• Set realistic AI expectations – AI success takes time.
• Provide training and change support – AI adoption needs investment in people.
• Measure AI success beyond hard metrics – Consider broader business impacts.

5. Not having the right talent

AI impacts every part of a business, yet companies assume IT can handle everything AI. AI ownership must be cross-functional. Read our article on who owns AI in the business.

Build the right teams:

• Establish an AI council – A cross-functional team ensures AI adoption is business-driven, not just IT-led.
• Invest in upskilling – Equip employees with AI knowledge to drive innovation internally.

6. Scaling before proving value

Many businesses jump straight into big-ticket AI projects. These often fail due to high complexity and risk.

Prove value gradually:

• Start small – Focus on quick wins before major rollouts.
• Prove AI’s value – Use successful pilots to build momentum.
• Scale gradually – Avoid burning resources on untested initiatives.

7. Ignoring AI ethics and compliance

Are your team uploading client data into DeepSeek – which is stored on Chinese servers?

Are they using Grok 3 to generate NSFW content on work systems?

Only 19% of employees we surveyed have read their company’s AI policy.

Ignoring AI compliance risks reputational damage and regulatory fines up to 7% of annual turnover (EU AI Act).

Embed ethical considerations:

• Ensure compliance with regulations like GDPR and the EU AI Act.
• Establish and communicate a clear AI policy.
• Train employees on AI ethics and compliance.
• Monitor AI use within your organisation.

8. Poor data quality

Companies assume AI can work straight out of the box with existing data. In reality, most business data is incomplete, outdated, or siloed.

Improve data quality:

• Improve data quality gradually – Don’t wait for a perfect dataset.
• Address data challenges per use case – Fix issues as you go.
• Invest in data governance – Ensure AI models get accurate inputs.

Key takeaways: Why AI projects fail

1. Lack of an AI strategy
2. Underestimating change management
3. Overreliance on vendors
4. Expecting instant results
5. Not having the right talent
6. Scaling too soon
7. Ignoring AI ethics and compliance
8. Poor data quality

Conclusion: How to ensure AI success

AI is not a guaranteed success – it requires a clear strategy, investment, and patience. Companies that rush in without proper planning will fail. Those that take a measured, business-driven approach will thrive.

Want to get AI right? Take our AI readiness assessment or contact us.

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We are iwantmore.ai – an AI consulting firm who specialise in delivering AI strategy and AI training courses to small and medium-sized businesses. Contact us for a free no obligation conversation about how we can help your business.

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