A Different Approach to AI Integration
Understanding how evidence-based implementation differs from traditional technology consulting
Return HomeWhy This Comparison Matters
When exploring AI integration, you'll encounter various approaches. Understanding these differences helps you choose a path that matches your needs and risk tolerance. We believe transparency about methods matters more than marketing claims.
Traditional Approach vs Our Approach
Traditional Consulting
- • Emphasis on comprehensive strategy documents and roadmaps before any practical testing
- • Focus on latest technology trends and vendor partnerships
- • Large upfront commitments required for implementation programmes
- • Success measured by deployment completion rather than business outcomes
- • Standardised solutions adapted to fit various industries
Our Approach
- • Begin with practical testing to generate actual evidence before significant investment
- • Technology choices based on what genuinely addresses your specific needs
- • Small initial engagements that demonstrate value before expanding
- • Success defined by measurable improvements to your actual processes
- • Solutions designed around your existing workflows and capabilities
What Sets Us Apart
Evidence Before Commitment
Rather than asking you to commit based on possibilities, we generate actual data about how AI performs in your context. This approach emerged from seeing too many organisations invest heavily in solutions that didn't deliver practical value. Our proofs of concept are designed to answer specific questions about feasibility and effectiveness.
Honest Assessment
We recognise that AI isn't always the answer. Our assessments consider whether simpler solutions might serve you better, whether your data infrastructure can support AI effectively, and whether the technology has actually reached maturity for your use case. This sometimes means recommending against AI implementation.
Sustainable Implementation
We build solutions your team can actually maintain and develop internally. This contrasts with approaches that create dependency on external expertise. Our training focuses on building genuine capability within your organisation rather than just showing people which buttons to press.
Risk Reduction Focus
Every recommendation considers implementation risk, from technical complexity to change management challenges. We structure engagements to provide clear exit points if results don't justify continuing. This approach protects your investment while allowing you to explore possibilities.
Effectiveness Comparison
Results Over Time
High initial investment, extended planning phase, significant change management requirements, outcomes uncertain until late in process
Modest initial investment, rapid practical testing, evidence-based scaling decisions, clear value demonstration at each stage
Of our proof of concept projects provide clear decisions within six weeks, whether to proceed or not
Average reduction in implementation risk through early testing and validation
Faster time to practical value compared to traditional implementation programmes
Investment Comparison
| Aspect | Traditional Consulting | Our Approach |
|---|---|---|
| Initial Engagement | £15,000 - £50,000+ for strategy and planning | £450 - £1,350 for initial assessment or proof |
| Time to Results | 6-12 months for initial deployment | 2-6 weeks for proof conclusions |
| Risk Profile | Significant commitment before knowing outcomes | Small investment with clear exit points |
| Ongoing Dependency | Often requires continued consulting support | Designed for internal team capability |
| Value Certainty | Uncertain until late in implementation | Evidence generated at each stage |
Long-term Value Perspective
While our initial engagements cost less than traditional consulting, the real value comes from reduced risk and faster decision-making. You invest incrementally based on demonstrated results rather than projected benefits. This approach typically results in lower total cost for successful implementations and eliminates costs associated with failed projects that traditional approaches often obscure.
Client Experience Comparison
Traditional Journey
Discovery Phase
Extensive documentation and requirements gathering, 4-8 weeks
Strategy Development
Roadmap creation and vendor selection, 6-10 weeks
Implementation
Large-scale deployment with significant business disruption, 12-24 weeks
Training & Handover
User training after system built, ongoing support needed
Our Journey
Initial Discussion
Understand your needs and identify test opportunities, 1 week
Proof Development
Build and test prototype with your actual data, 2-4 weeks
Evidence Review
Decide together whether to proceed based on real results, 1 week
Gradual Scaling
Expand only where evidence supports, building team capability throughout
Sustainability & Long-term Results
Internal Capability Development
Traditional implementations often create systems that only external consultants fully understand. Our approach builds genuine capability within your team from the start. Staff participate in proof development, understand the underlying logic, and develop the skills to maintain and enhance solutions independently.
This matters because AI technologies evolve rapidly. Teams that understand principles rather than just procedures can adapt as capabilities improve and requirements change.
Maintenance Realism
AI systems require ongoing attention. Models need retraining as data patterns shift, edge cases require handling, and integration points need maintenance. We design with these realities in mind, choosing approaches your team can actually sustain.
This sometimes means recommending simpler solutions than what's technically possible. A maintainable system that delivers 80% of potential value serves you better than a sophisticated system that becomes unmaintainable.
Results That Compound
The capability you build through our approach compounds over time. Staff who understand AI principles can identify additional opportunities, evaluate vendor claims critically, and adapt solutions as your needs evolve. This contrasts with dependency relationships where capability remains with external consultants.
We measure success not just by initial implementation outcomes but by whether your team can independently extend and improve solutions two years later.
Common Misconceptions
"Larger investments produce better results"
Investment size and outcome quality don't correlate as strongly as many assume. Small, well-targeted implementations often deliver more practical value than comprehensive programmes. The key is matching investment to demonstrated value rather than assumed benefit. Our approach lets you scale investment as evidence accumulates.
"You need the latest AI technology"
Technology recency matters far less than appropriateness for your specific needs. Mature, well-understood approaches often serve better than cutting-edge capabilities with unclear behaviour. We recommend technology based on reliability and suitability rather than novelty. Sometimes the right answer is established machine learning rather than generative AI.
"Implementation requires major business transformation"
Effective AI integration can work with existing processes rather than requiring wholesale transformation. Starting with current workflows and making targeted improvements reduces risk and disruption. Transformation, when needed, can happen gradually based on demonstrated benefits rather than upfront assumptions.
"Small tests don't provide meaningful insights"
Well-designed proofs of concept generate crucial evidence about feasibility, performance, and practical value. They reveal integration challenges, data quality issues, and user acceptance factors that strategy documents can't predict. The insights from contained tests inform better decisions about whether and how to proceed.
Why Choose Our Approach
Lower Risk
Small initial investments with clear decision points protect your resources while exploring possibilities
Faster Value
Evidence-based approach means you see practical results in weeks rather than months or years
Team Capability
Build internal expertise that compounds over time rather than creating external dependencies
Interested in This Approach?
If you value evidence over promises and prefer to test ideas before significant commitments, our approach might suit your needs. Let's discuss whether this way of working makes sense for your situation.
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