The Dangers of Rushing AI Adoption: Key Risks and Considerations for Businesses
- Christopher Roberts
- Aug 31
- 6 min read
Artificial intelligence is no longer futuristic, it’s here, reshaping industries from finance to healthcare to logistics. For enterprises, early, disciplined adoption of AI can unlock efficiencies, create new products, and reimagine customer experiences. Executives are right to feel urgency.
But urgency can too easily turn into real issues. In the rush to adopt, enterprises risk making avoidable mistakes:
Rushing past proper commercial and technical diligence
Launching pilots without business cases
Deploying tools that don’t scale
Underestimating long-term costs (license fees, incremental usage tiers, and operational support costs)
Leaving governance gaps that expose the organisation to risk (especially compliance)
The lesson is simple: move fast, but don’t skip the steps that matter. AI must be treated like any other enterprise technology with commercial checks, technology validation, architectural alignment, data governance, and commercial planning. Cut corners, and what looks like innovation can become a liability.
Make Sure You Have a Business Case
Every successful AI initiative starts with a clear business case. Without it, fast tracked adoption projects risk becoming expensive experiments that never deliver value.
A solid business case defines:
ROI: Is the goal cost savings, revenue growth, or operational efficiency?
Measures: How will success be tracked, cycle time, error rates, conversion rates, SLA adherence, customer satisfaction?
Scaling plan: How a pilot translates into sustainable, enterprise-wide adoption
Be especially careful with projects that promise to replace staff, particularly in call centres, sales or operations. These almost never deliver the promised outcomes:
Customers still want human empathy for complex or emotional issues
AI needs humans to handle exceptions and supervise edge cases
Service quality often drops when bots replace people outright
At best, AI in these functions should be framed as augmentation: helping teams handle more work, cut routine tasks, or focus on higher-value interactions. The successful AI business case is almost always about doing more with the same team, not doing the same with fewer people.
Where Enterprises Go Wrong
The “Shiny Demo” Trap
A retailer rushed a generative chatbot into production after an impressive vendor demo. It looked like a cost-saver. Instead, it invented policies, frustrated customers, and had to be pulled back within weeks. This mistake is not rare.
Real-world Examples
Klarna (2024): The fintech giant replaced 700 customer service staff with AI agents. Service quality degraded. Klarna admitted the failure and is now rehiring humans. See video below for the full story.
Commonwealth Bank of Australia (2025): Tried replacing 45 call centre staff with a voicebot. Instead of cutting volumes, calls spiked, complaints rose, and the bank rolled back the plan, apologised, and rehired staff.
These examples show how impressive demos and big promises can lead down a rushed path. But without proper diligence, a solid business plan and supporting operational model, AI replacements rarely work as planned.
Run Diligence Like You Mean It
Especially when engaging AI start-ups, diligence can’t stop at “that was an impressive demo, lets give it a try” Many AI vendors don’t survive more than a year or two, they can burn through VC funding without a sustainable model, or discover their product can’t scale.
And crucially, many don’t realise that for enterprises, their product may be viewed as external system. That triggers compliance and data governance requirements (audit logs, residency controls, deletion rights, security certifications) they may not be ready for.
Your Diligence Checklist Should Include
Company health: runway, burn rate, paying customers, churn, audited financials
Investors & governance: who backs them, board quality, signs of down-rounds or instability
Strategy & roadmap: history of delivering features vs. slideware, commitment to compliance/security
Technology: scale testing, resilience under peak load
Data governance: encryption, residency, audit trails, deletion guarantees, compliance with GDPR/PCI
Compliance fit: do they understand what it means to operate as an external system in an enterprise environment?
Integration & architecture: APIs, SDK maturity, RBAC (Role-Based Access Control), observability, incident SLAs
Lock-in & exit: data portability, export formats, escrow agreements
References: don’t be afraid to ask for them, and then actually call them. Speak with other customers to verify that ROI was delivered in reality, not just on a slide. If a vendor can’t provide credible references, that is a serious red flag
If a vendor can’t support these checks, the demo doesn’t matter
Big-Name Examples:
Air Canada (2024): A chatbot gave false information about bereavement fares. The airline argued it wasn’t responsible, the court disagreed. Reputational and legal fallout followed.
Zillow Offers: Zillow’s AI-driven home-buying algorithm overpaid for homes. When the market cooled, the company lost $500 million and shut down the programme.
Don’t Forget the Commercials:
AI solutions often fail not because of bad tech, but because of bad commercials.
Hidden costs: re-training, monitoring, cloud costs and compliance overhead
Teaser pricing: low entry points, huge jumps at enterprise scale
Vendor fragility: many AI start-ups don’t survive two years, pivot away from their product, or burn through VC funding
External system issues: if the vendor hasn’t designed for enterprise governance, the costs of compliance (auditability, residency, deletion rights) may fall on you
Commercial diligence must cover:
TCO (Total Cost of Ownership) scenarios
Price protections and exit terms
Vendor health and stability
Data governance maturity
Migration paths if the vendor fails
Why Discipline Matters
You would never deploy an ERP, Commerce Platform or CRM without procurement reviews, architecture alignment, and compliance checks. AI deserves at least as much discipline (arguably more). AI is probabilistic, not deterministic. It drifts. It touches sensitive data. It influences decisions that affect people, money, and trust. The risks of skipping steps are higher, not lower.
A Smarter Way to Move Fast
Run real pilots: your data, your scale, your risks
Aligned ROI: cost, revenue, or efficiency, steer away from staff cuts (as the driver)
Do tech and governance diligence: architecture, scale, compliance
Enforce governance: compliance, bias checks, audits, human oversight
Plan lifecycle: re-training, incident playbooks, rollback, vendor shifts
Fully check the commercials: TCO, vendor health, lock-in risk, exit strategy
Check references: ask other customers if they saw ROI delivered. If a vendor won’t provide references, that’s a red flag
Encouragement With Guardrails
AI is not something to delay. Competitors, customers, and employees are already experimenting (and/or fully using for some time now). But moving fast does not mean cutting corners. It means piloting quickly, proving value, and scaling responsibly. It means treating AI as an enterprise system (with no shortcuts).
The Upside Is Enormous
This article is to remind us of the importance of proper diligence and checks for AI (just like any tech solution). With that said, Artificial intelligence is transformative and if done right, it creates efficiencies, unlocks new revenue opportunities, and enables forms of human/machine collaboration that far exceed what either could achieve alone.
The message here isn’t "be afraid" it’s "be disciplined". Do the right checks, align with your enterprise architecture, enforce governance, and pilot well.
If you do, the benefits will be exceptional. AI will become a key enabler on how your company operates and drives real results. Move fast, but do it right. The payoff is worth it.
Success Stories (When Companies Get it Right):
The common thread here? Disciplined adoption. These companies tied AI to measurable ROI, validated with real pilots, and built governance in.
3 Men Movers (family-owned): Adopted AI for distracted driving detection and dynamic routing. Careful testing and staff buy-in cut accidents by 4.5% in three months.
Daily Harvest (start-up): Used AI to personalise recommendations and optimise packaging logistics. Success came from cautious pilots and strong cost modelling.
VWV Law (UK, £54m revenue): Launched an AI innovation programme where tools halved note-taking time and cut report drafting by 50%. ROI was framed around efficiency and productivity gains, not job cuts
Mid-market logistics firm: Used no-code AI to predict delivery delays. After careful piloting, they reduced complaints by 22% and improved SLA adherence by 18%.
Colmenero IO (AI Assessments & Diligence Practice)
At Colmenero IO, we can help you conduct an AI opportunity assessment, run the diligence, evaluate the commercials, and develop the business case. We can also assess how AI solutions will work within your enterprise (people, teams, business, operations and technology). As an ISO/IEC 42001 Artificial Intelligence Lead Implementer, we bring structured methodology and compliance to every engagement.
