Protocol Number:

1263 2025 Oct SSAH

Generative AI Adaptive Leverage Model (ALeM)

From Paradox to Practice

From Paradox to Practice: An Overview

The Hook: A Relatable Problem

"Businesses have access to incredible Generative AI tools. But imagine giving the keys to a Formula 1 car... to a novice user. This highlights a key problem facing over 5 million micro-businesses in the UK. They have been given this powerful, game-changing technology, but have no idea how to use it. They're told it will make them faster, but they're stuck in the pits."

The Core Problem: The "Gen-Dox"

This research discovered that this isn't just a skills gap—it's a paradox. This research defines it as the 'Generative AI Adaptation Paradox' (or Gen-Dox). It’s the gap between easy access to AI and the massive, hidden difficulty of adapting the business to get any real value from it. Owner-managers were interviewed who were frustrated. They were spending time, spending money, but seeing no real results. The Formula 1 car was just sitting in their garage, gathering dust.

The Solution: The "ALeM"

Most research stops at describing this problem. This project was initiated to build a solution. This thesis is a Design Science Research project. This means a practical tool was built, not just a paper written. It’s called the Adaptive Leverage Model (ALeM). Think of it as the driver's manual, the training program, AND the race strategy, all in one.

It’s a practical, step-by-step framework that helps a business owner:

1. Sense

First, SENSE the real opportunities (where can AI actually help?).

2. Seize

Second, SEIZE them by targeting high-impact, low-effort tasks.

3. Reconfigure

And third, RECONFIGURE their business to make that value last.

And it’s not just an arbitrary checklist. The ALeM is built on a solid academic foundation—Dynamic Capabilities Theory—and it’s been validated by a panel of academic and industry experts.

The Impact: Why It Matters

The ALeM moves these 5 million businesses from paradox to practice. It bridges the gap between AI's potential and the everyday reality of a small business owner. This research delivers a practical, validated tool that helps these businesses not just survive the AI revolution, but actually put their foot on the accelerator and thrive in it.

The Problem: Access vs. Capability

The "Gen-Dox"

The Generative AI Adaptation Paradox (Gen-Dox) is a critical post-adoption gap. UK micro-businesses have widespread access to GenAI tools but lack the capability to translate that access into sustained business value.

The "Bootstrap Paradox"

A deeper barrier prevents the journey from starting:

Need Time/Capability to Learn GenAI

Can't Justify Time Until Value is Seen

The Adoption Gap

Only 15% of small businesses have adopted at least one AI technology, despite making up 99.16% of the entire UK business population.

Source: British Chambers of Commerce (2024)

Micro-Business Reluctance

Over 5.2 million micro-businesses exist in the UK, yet 73% of sole traders are using AI in a limited way (31%) or have no plans to adopt it (42%).

Source: Moneypenny (2025); Money.co.uk (2025)

Shallow Adoption

Only 3% of small firms (approx. 49,300) are using 4-5 AI technologies simultaneously, compared to 20% of large firms.

Source: Dept. for Digital, Culture, Media & Sport (2022)

The Theoretical Anchor: Dynamic Capabilities Theory

To solve a problem of adaptation, we need a theory of adaptation. DCT provides the lens, explaining that advantage comes from three core competencies:

Sensing

The ability to identify and assess high-value AI opportunities, cutting through the hype.

Seizing

The ability to mobilize scarce resources (especially time) to capture value from an opportunity.

Reconfiguring

The ability to embed successful pilots into daily operations to build a sustainable advantage.

The Method: Design Science Research (DSR)

The goal of this research is to create the missing process that connects theory (DCT) to practice (GenAI) for micro-businesses. A 3-phase DSR journey was used:

Phase 1: Problem Definition

Empirically validated the Gen-Dox with N=3 interviews, analyzed via Gioia methodology.

Phase 2: Artifact Design

Systematically translated empirical findings and DCT principles into a practical solution.

Phase 3: Artifact Evaluation

Seeking critical, external evaluation from you, the expert panel.

The Artifact: The Generative AI Adaptive Leverage Model (ALeM)

This is the interactive artifact itself. It's a process, delivered as a tool, designed to solve the Bootstrap Paradox first, then systematically build capability. Select a phase to see the tools.

Phase 0: The Quick Win (Antidote to the Paradox)

The Psychological Problem

The biggest barrier to GenAI isn't skill; it's the fear of the blank page and the feeling of being overwhelmed. You can't justify spending time to find value if you don't have the time to begin with.

The Solution

This phase is designed to give you an immediate, tangible win in under 15 minutes. It has two steps: picking a tool and executing a pre-built prompt. This builds your confidence and motivation before we ask you to do any procedural work.

Step 1: The "Good Enough" Tool Selection Framework (TSF)

Don't get paralyzed by choice. Follow this 3-filter decision tree to pick a tool in 60 seconds.

Filter 1: Data Governance (The Trust Boundary)
Do you handle sensitive client data (e.g., legal, medical, financial)?
YES: Start with a secure, local LLM (like LM Studio, Ollama) to keep data on your device.
NO: Proceed to Filter 2.
Filter 2: Ecosystem Integration (Path of Least Resistance)
What system do you already use every day?
Microsoft 365 (Word, Outlook): Start with Microsoft Copilot.
Google Workspace (Docs, Gmail): Start with Google Gemini.
Neither: Proceed to Filter 3.
Filter 3: Primary Use Case (Functional Fit)
What is your main need?
General Versatility: Start with ChatGPT.
Web Research & Fact-Checking: Start with Perplexity.
Long-Form Analysis (e.g., reports): Start with Claude.

Step 2: Execute One of Five Template Prompts

Now, use your chosen tool. Click a template below, choose "Simple" or "Advanced," and copy the prompt.

Template 1: The Email Responder Objective: Reduce time and cognitive load from client emails.

**GOAL:** Quickly respond to a client email.
**TASK:** Draft a polite and professional response to the following email. Thank the client for their message and confirm that I will send the revised designs by Friday.
**CONTEXT:** [Paste the full client email here]

**ACTION:** Draft a professional and reassuring email response to a client.
**STEPS:**
1. Acknowledge the client's specific feedback and thank them for their clarity.
2. Confirm that all their requested changes are understood and will be implemented.
3. State a firm and confident delivery deadline (Friday, end of day).
4. Subtly reinforce the value of the collaboration.
5. End with a clear and positive closing.
**PERSONA:** Act as a seasoned and highly organized Senior Project Manager. Your tone is professional, confident, proactive, but also warm.
**EXAMPLES:**
- Good Tone: "Thanks for the clear feedback, John. The team is already working on these adjustments..."
- Bad Tone: "Yep, got it. Will do."
**CONSTRAINTS:**
- The entire email must be under 150 words.
- Do not use exclamation points.
- The subject line must be: "Re: [Original Email Subject] - Revised Designs Timeline"
**TEMPLATE:**
Subject: Re: [Original Email Subject] - Revised Designs Timeline
Hi [Client Name],
[Acknowledge/thank]
[Confirm revisions and deadline]
[Positive sentiment]
[Closing]
Best,
[Your Name]

**CLIENT EMAIL TO RESPOND TO:**
[Paste the full client email here]

Template 2: The Social Media Idea Generator Objective: Overcome creative blocks and generate engaging content ideas.

**GOAL:** Get ideas for social media.
**TASK:** Generate five creative and engaging ideas for Instagram posts for my local bakery.
**CONTEXT:** My target audience is young families and professionals in the Leeds area.

**ACTION:** Generate a content calendar of five unique, engaging, and on-brand Instagram post ideas (Monday-Friday).
**STEPS:**
1. For each day, create a distinct content theme (e.g., "Meet the Baker Monday").
2. For each theme, write a compelling caption (50-75 words).
3. For each caption, suggest a specific, achievable visual concept (photo/video).
4. Include a Call-to-Action (CTA) in at least three posts.
5. Include 5-7 relevant, niche hashtags for each post.
**PERSONA:** Act as an award-winning Social Media Strategist specializing in food/beverage. Your tone is creative, enthusiastic, and practical.
**EXAMPLES:**
- Good Idea: "A close-up shot of our sourdough starter bubbling. CTA: What should we name our next starter?"
- Bad Idea: "A picture of bread."
**CONSTRAINTS:**
- All visuals must be achievable with a smartphone.
- Tone: Warm, community-focused, authentic.
- Do not suggest stock photos.
- Audience: Young families (25-40) and professionals in Leeds, UK.
**TEMPLATE:**
Monday: [Theme]
- Visual: [Description]
- Caption: [Text]
- Hashtags: [List]
[Continue for Tuesday-Friday]

Template 3: The Meeting Agenda Creator Objective: Reduce admin burden and improve meeting effectiveness.

**GOAL:** Create an agenda for my weekly team meeting.
**TASK:** Make a 1-hour agenda.
**CONTEXT:** We need to discuss the Q3 marketing report, the upcoming website launch, and the new client proposal.

**ACTION:** Create a detailed, timed, and outcome-driven agenda for a 60-minute weekly team meeting.
**STEPS:**
1. Allocate a specific time slot for each of the three main discussion points.
2. Begin with a 5-min review of previous action items.
3. For each agenda item, define a clear desired outcome (e.g., "Decision on...").
4. Assign a clear owner responsible for leading each discussion point.
5. Conclude with a 5-min summary of new action items.
6. Tackle the most complex topic (website launch) first.
**PERSONA:** Act as a highly experienced Chief Operating Officer (COO) known for running efficient meetings.
**EXAMPLES:**
- Good Item: "10:15 - 10:35 (20 mins) | Website Launch Final Review (Owner: Sarah) | Desired Outcome: Final go/no-go decision."
- Bad Item: "Talk about the website."
**CONSTRAINTS:**
- Total time must not exceed 60 minutes.
- Each item must have a duration, owner, and outcome.
**TEMPLATE:**
[Provide table structure for the agenda]

Template 4: The Product Description Writer Objective: Create compelling, persuasive, and SEO-friendly product copy.

**GOAL:** Write a description for my new product.
**TASK:** Write a product description for a new handmade leather wallet.
**CONTEXT:** It's high-quality and aimed at men aged 30-50.

**ACTION:** Write a compelling and SEO-optimized product description for an e-commerce store.
**STEPS:**
1. Start with an attention-grabbing headline.
2. Use the AIDA (Attention, Interest, Desire, Action) framework.
3. Weave in 5 relevant SEO keywords naturally.
4. Create a bulleted list of 3-5 key features and their benefits.
5. Conclude with a strong call-to-action.
**PERSONA:** Act as a world-class e-commerce copywriter for luxury lifestyle brands. Tone is sophisticated, evocative, and aspirational.
**EXAMPLES:**
- Good: "Tired of bulky, disposable wallets? The Artisan Wallet is crafted from a single piece of full-grain leather..."
- Bad: "This wallet is made of leather. It has slots for cards."
**CONSTRAINTS:**
- 200-250 words total.
- Tone: Luxurious, exclusive, not salesy.
- Audience: Style-conscious male professional, 30-50.
- Keywords: "full-grain leather wallet," "handmade men's wallet," "slim minimalist wallet," "luxury leather goods," "artisan wallet."
**TEMPLATE:**
[Headline]
[Attention: Identify pain point]
[Interest: Describe story/craftsmanship]
[Desire: Evoke feeling/status]
- [Feature 1]: [Benefit 1]
- [Feature 2]: [Benefit 2]
[Action: Confident call to purchase]

Template 5: The FAQ Generator Objective: Proactively address customer questions to reduce inquiries and build trust.

**GOAL:** Create an FAQ for my website.
**TASK:** Write some frequently asked questions for my online course on sourdough baking.
**CONTEXT:** It's for complete beginners.

**ACTION:** Generate a comprehensive and user-friendly FAQ section for a product landing page.
**STEPS:**
1. Anticipate and formulate 5-7 of the most likely questions a potential customer would have.
2. Categorize the questions into logical groups (e.g., "Course Content," "Pricing").
3. Write a clear, concise, and reassuring answer for each question.
4. Frame answers to overcome objections and build confidence.
5. Include a final "catch-all" question.
**PERSONA:** Act as a Customer Success Manager who is an expert in user onboarding. Your tone is patient, clear, and encouraging.
**EXAMPLES:**
- Good Q/A: "Q: What if I've never baked before? A: This course is designed specifically for you! We start with the absolute basics."
- Bad Q/A: "Q: Is it for beginners? A: Yes."
**CONSTRAINTS:**
- Each answer should be 2-3 sentences max.
- Simple language, no technical jargon.
- Product: Online course on sourdough baking for beginners.
**TEMPLATE:**
Frequently Asked Questions
[Category 1: e.g., Course Content]
Q: [Question 1]?
A: [Answer]
[...etc...]

Phase 1: Sensing & Planning (From Audit to Action)

Goal: Move from a "quick win" to a strategic plan. This phase walks you through a 5-step process to identify your single highest-value opportunity and commit resources to it.

Step 1: The Internal 80/20 Audit

List your top 5 most frustrating or time-consuming tasks. Rate them. This helps you find your "20%" target.

Task Description	Time Investment (1-5)	Value Creation (1-5)	AI Suitability (1-5)

Step 2: The External Opportunity Audit

Briefly research what others are doing. Use your AI tool to speed this up! Ask it: "How are businesses like [YOUR BUSINESS TYPE] using AI for [YOUR TASK FROM STEP 1]?"

Step 3: The "One-Task" Decision

You must pick only one task to focus on. Resisting the urge to multitask is key. Choose your single, highest-leverage task to pilot.

My "One-Task" is:

Step 4: Identify Your Tool Category

Is your task...
- General Text/Content? (e.g., ChatGPT, Claude)
- Multimodal Content? (e.g., DALL-E, Midjourney)
- Structured Reasoning/Logic? (e.g., Claude for coding, GPT-4 for analysis)

Step 5: Resource Allocation Plan

Commit to it. A plan turns a vague idea into a real project.

My Goal is:

I will commit:

Success looks like:

Phase 2: Seizing & Learning (Selecting & Learning)

Goal: To make a strategic choice and acquire the minimum necessary skills to act on your plan from Phase 1.

Step 1: The 'CATS' Matrix (Tool Selection)

You've chosen your category (e.g., "General Text"). Now, compare specific tools in that category (e.g., ChatGPT vs. Claude). Score 1 (Low) to 5 (High).

Tool	C (Cost)	A (Accessibility)	T (Time to Value)	S (Skills Required)	Total
					18
					18

Decision: Choose the tool with the highest score (>= 15).

Step 2: Targeted Micro-Learning

Don't become an "AI expert." Learn only what you need for your "One-Task." This is "Just-in-Time" learning.

Identify Core Skill: e.g., "Writing a prompt for Instagram captions."
Find Resource: Search for a 10-minute YouTube video or blog post on that exact skill.
Time-box: Allocate 30-60 minutes for this. No more.
Practice Immediately: Apply what you learned to your "One-Task" right away.

Phase 3: Reconfiguring & Adapting (Building Your SOP)

Goal: To make your successful pilot permanent. This phase turns your individual "win" into a simple, repeatable Standard Operating Procedure (SOP) for your business. This documented process is your new, reconfigured capability.

Step 1: Create Your 'Minimal Viable Routine' (SOP)

Document your new process. This turns it from a personal habit into a business asset.

SOP-MKT-001: Weekly Instagram Content Generation

**SOP Title:** SOP-MKT-001: Weekly Instagram Content Generation
**OBJECTIVE:** To generate one week of social media content in under 30 minutes.
**TOOL(S) USED:** Google Gemini

**MASTER PROMPT (ASPECT):**
- ACTION: Generate 5 Instagram posts.
- STEPS: 1. Create a theme for each post. 2. Write a 50-word caption. 3. Suggest a visual.
- PERSONA: Act as a social media expert for graphic designers.
- EXAMPLES: [Your own best example]
- CONSTRAINTS: Tone must be "creative, professional, insightful."
- TEMPLATE: "Day 1: [Caption]..."

**STEP-BY-STEP PROCESS:**
1. Open Google Gemini.
2. Paste the MASTER PROMPT.
3. Review outputs for tone, accuracy.
4. Schedule posts in [Your Tool].

**SUCCESS METRIC:** Time spent per week < 30 minutes.
**VERSION HISTORY:** v1.0 (06/11/2025) - Initial creation.

Step 2: Understand the Automation Gradient

Your new SOP is just the start. You must make a strategic choice about how far to automate it.

Level 1: Manual MVR (You are here): You copy/paste the prompt. (Low effort, good value)
Level 2: Semi-Automated: You use a "no-code" tool like Zapier to connect your AI to another app (e.g., auto-draft a post from a blog RSS feed). (Medium effort, great value)
Level 3: Fully Autonomous: An "agent" does the whole process for you. (High effort, highest value)

Key insight: You don't need Level 3. Level 1 might be "good enough" and the right strategic choice for your business right now.

Step 3: Continuous Adaptation (PDCA / Review)

Your new capability must adapt or it will become obsolete. Use two feedback loops:

PDCA Cycle (Operational): On a weekly basis, ask "Can I improve the prompt?" (Plan-Do-Check-Act). This refines your current SOP.
Quarterly Review (Strategic): On a quarterly basis, ask "Is this SOP still the right process?" or "Is it time to move to Level 2 automation?" This challenges the whole SOP.

Research & Academic Context

The following sections provide the academic and practical context underpinning the ALeM framework. Click each title to expand the content.

The "Golden Thread": Academic Rigor

The ALeM's practical steps are a direct operationalization of academic theory. This "golden thread" connects each phase to a core DCT capability.

ALeM Practical Phase	Core Mechanism	DCT Capability Operationalised
Phase 0: Quick Wins	Bootstrap Paradox Mitigation	A pre-capability "nudge" that enables Sensing by providing initial motivation.
Phase 1: Sensing & Planning	80/20 Audit & Resource Plan	Sensing: A practical tool to analyse internal processes, not external hype.
Phase 2: Seizing & Learning	CATS Matrix & Micro-Learning	Seizing: A resource-allocation mechanism to commit scarce resources to high-ROI tasks.
Phase 3: Reconfiguring	MVR / SOP & PDCA Cycle	Reconfiguring: The engine to move from ad-hoc use to systemic, learned adaptation.

Practical Challenges & Realities

This framework is practical, not magic. You will face real-world challenges. Here is how ALeM is designed to handle them.

Digital Divide & Infrastructure

Challenge: The ALeM assumes you have reliable internet and a modern computer.
Mitigation: This is a hard prerequisite. The TSF in Phase 0 guides users to low-bandwidth or even local/on-premise tools (like Ollama) if data security or poor connectivity is a major issue.

Failure Management (e.g., Bad Outputs)

Challenge: Your AI pilot might fail, or the AI will "hallucinate" (make things up). This is demoralizing.
Mitigation: ALeM reframes this as a learning event, not a personal failure. We use a Failure Taxonomy:
- Was it a Prompt Failure? (Go back and refine your ASPECT prompt)
- Was it a Tool Failure? (Go back to the CATS matrix and try a different tool)
- Was it a Strategic Failure? (Go back to the 80/20 Audit; you picked the wrong task to automate)

Ethical & Legal Risks (GDPR, Copyright)

Challenge: As a business owner, you are legally responsible for your AI's output, including data privacy (GDPR) and copyright.
Mitigation: This is built directly into the framework:
- TSF (Phase 0): The first filter is Data Governance, forcing you to think about security before you start.
- ASPECT (Phase 0): The "C" (Constraints) in the advanced prompt is where you build in rules like "Do not use personal data" or "Ensure output is original."
- MVR (Phase 3): The "Human Review" checkpoint is a mandatory step for all customer-facing content to check for accuracy, bias, and brand safety.

AI Literacy Barriers

Challenge: Users may not know how to use AI, evaluate outputs, or understand what it's doing.
Mitigation: The framework is a "scaffolded complexity" system.
- Phase 0 uses simple templates (GTC) to build confidence.
- Phase 2 introduces "Just-in-Time" Micro-Learning.
- ASPECT framework is offered as an advanced option for users who are ready for it.

Limitations of this Framework

To be a rigorous artifact, ALeM must be transparent about its own limitations and boundary conditions.

The Time and Resource Paradox

Limitation: The framework requires an upfront investment of time and cognitive energy, the very resources a stressed owner-manager lacks.
Built-in Mitigation: Phase 0 (Quick Win) is designed specifically to solve this. It delivers a tangible ROI (e.g., 15 minutes saved) before asking for the 1-hour commitment for Phase 1. It creates motivational capital.

The Cognitive & Metacognitive Burden

Limitation: The model's structure can feel analytical or "academic" to an intuitive, action-oriented entrepreneur.
Built-in Mitigation: Scaffolded Complexity. The user starts with the simple GTC prompt framework. Only when they are ready for more power do they "unlock" the advanced ASPECT framework. This progressive disclosure respects the user's learning curve.

Boundary Conditions (Who is this for?)

Limitation: ALeM is not a universal solution.
Exclusion Criteria: This framework is not for:
- Businesses in acute financial crisis (it's not a rescue plan).
- Users who lack foundational digital literacy (e.g., cannot use a web browser).
- Users who are unwilling to commit 2-3 hours over a few weeks to learn.

Sector-Specific Limitations (e.g., Healthcare, Legal)

Limitation: Businesses in highly regulated sectors (healthcare, legal, finance) face data security risks that make public cloud AI tools (like ChatGPT) inappropriate for core tasks.
Built-in Mitigation: The TSF (Phase 0, Step 1) makes Data Governance the first decision. It explicitly directs these users to Local LLM solutions (Ollama, LM Studio) that run on their own hardware, ensuring no sensitive data ever leaves their control.

References

This artifact is built on established academic theory and contemporary market research. Key sources are listed below.

Academic Journal Articles & Books

Baker, T. and Nelson, R. E. (2005) 'Creating something from nothing: Resource construction through entrepreneurial bricolage', Administrative Science Quarterly, 50(3), pp. 329-366.
Brownlee, J. (2020) How to Choose a Machine Learning Model. Machine Learning Mastery.
Cohen, W. M. and Levinthal, D. A. (1990) 'Absorptive Capacity: A New Perspective on Learning and Innovation', Administrative Science Quarterly, 35(1), pp. 128-152.
D'Aveni, R. A. (1994) Hypercompetition: Managing the dynamics of strategic maneuvering. New York: Free Press.
Davenport, T. H. and Ronanki, R. (2018) 'Artificial intelligence for the real world', Harvard Business Review, 96(1), pp. 108-116.
Davis, F. D. (1989) 'Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology', MIS Quarterly, 13(3), pp. 319-340.
Deming, W. E. (1986) Out of the Crisis. Cambridge, MA: MIT Press.
Eisenhardt, K. M. and Martin, J. A. (2000) 'Dynamic capabilities: what are they?', Strategic Management Journal, 21(10‐11), pp. 1105-1121.
Eloundou, T., Manning, S., Mishkin, P. and Rock, D. (2023) 'GPTs are GPTs: An early look at the labor market impact potential of large language models', arXiv preprint arXiv:2303.10130.
Flavell, J. H. (1979) 'Metacognition and cognitive monitoring: A new area of cognitive–developmental inquiry', American Psychologist, 34(10), pp. 906–911.
Ghobadian, A. and O'Regan, N. (2006) 'The impact of ownership on small firm behaviour and performance', International Small Business Journal, 24(6), pp. 555-586.
Goswami, S. (2025) Choosing the Right LLM for Your Use Case. Towards Data Science.
Gregor, S. and Hevner, A. R. (2013) 'Positioning and presenting design science research for maximum impact', MIS Quarterly, 37(2), pp. 337-355.
Hevner, A. R., March, S. T., Park, J. and Ram, S. (2004) 'Design science in information systems research', MIS Quarterly, 28(1), pp. 75-105.
Kerr, M. S. (1991) 'Leveraging human assets in law firms: Human resource management and just-in-time training', Law Practice Management, 17(4), pp. 26-31.
Kevill, A. (2014) How are dynamic capabilities enacted in micro-enterprises and what role do different parties and managerial time allocation play in this enactment? White Rose eTheses Online.
Koch, R. (1998) The 80/20 principle: The secret of achieving more with less. London: Nicholas Brealey Publishing.
Kotter, J. P. (1996) Leading change. Boston, MA: Harvard Business Press.
Mitra, A. (2025) A Guide to Choosing the Right LLM for Your Enterprise. Analytics India Magazine.
Noy, S. and Zhang, W. (2023) 'Experimental evidence on the productivity effects of generative artificial intelligence', Science, 381(6654), pp. 187-192.
Peffers, K., Tuunanen, T., Rothenberger, M. A. and Chatterjee, S. (2007) 'A design science research methodology for information systems research', Journal of Management Information Systems, 24(3), pp. 45-77.
Rogers, E. M. (2003) Diffusion of Innovations (5th ed.). New York: Free Press.
Simon, H. A. (1972) 'Theories of Bounded Rationality', in McGuire, C. B. and Radner, R. (eds.) Decision and Organization. Amsterdam: North-Holland Publishing Company, pp. 161-176.
Teece, D. J. (2007) 'Explicating dynamic capabilities: the nature and microfoundations of (sustainable) enterprise performance', Strategic Management Journal, 28(13), pp. 1319-1350.
Teece, D. J. (2014) 'The foundations of enterprise performance: Dynamic and ordinary capabilities in an (economic) theory of firms', Academy of Management Perspectives, 28(4), pp. 328-352.
Teece, D. J., Pisano, G. and Shuen, A. (1997) 'Dynamic capabilities and strategic management', Strategic Management Journal, 18(7), pp. 509-533.
Tidestrom, A. (2022) 'Mental health in small business: A review and research agenda', International Small Business Journal, 40(4), pp. 497-513.
Ucbasaran, D., Westhead, P. and Wright, M. (2010) 'The Nature of Entrepreneurial Experience, Business Failure and Comparative Optimism', Journal of Business Venturing, 25(6), pp. 541-555.
Venkatesh, V. and Davis, F. D. (2000) 'A theoretical extension of the technology acceptance model: Four longitudinal field studies', Management Science, 46(2), pp. 186-204.
Venkatesh, V., Morris, M. G., Davis, G. B. and Davis, F. D. (2003) 'User Acceptance of Information Technology: Toward a Unified View', MIS Quarterly, 27(3), pp. 425-478.
Walsh, J. P. and Ungson, G. R. (1991) 'Organizational memory', Academy of Management Review, 16(1), pp. 57-91.
Weick, K. E. (1984) 'Small wins: Redefining the scale of social problems', American Psychologist, 39(1), pp. 40-49.
Welsh, J. A. and White, J. F. (1981) 'A small business is not a little big business', Harvard Business Review, 59(4), pp. 18-32.

Industry Reports & Grey Literature

British Chambers of Commerce (2024). Most SMEs still struggling to embrace AI. Available at: https://www.britishchambers.org.uk (Accessed: 24 July 2024).
Department for Digital, Culture, Media & Sport (2022). AI activity in UK businesses: Executive summary. London: GOV.UK. Available at: https://www.gov.uk/government/publications/ai-activity-in-uk-businesses/ai-activity-in-uk-businesses-executive-summary (Accessed: 11 January 2022).
Hu, K. (2023) ChatGPT sets record for fastest-growing user base. Reuters. Available at: https://www.reuters.com/technology/chatgpt-sets-record-fastest-growing-user-base-analyst-note-2023-02-01/ (Accessed: 18 October 2025).
McKinsey & Company (2023) The economic potential of generative AI: The next productivity frontier. McKinsey Global Institute.
Money.co.uk (2025). UK business statistics 2025 - business facts and stats report. Available at: https://www.money.co.uk (Accessed: 8 July 2025).
Moneypenny (2025). The state of AI adoption in UK businesses | 2025 trends & insights. Available at: https://www.moneypenny.com (Accessed: 1 September 2025).
Ofcom (2024) Connected Nations 2024. Office of Communications. Available at: https://www.ofcom.org.uk/research-and-data/multi-sector-research/infrastructure-research/connected-nations-2024 (Accessed: 18 October 2025).
University of Brighton (2025) Developing absorptive capacity in SMEs. Available at: https://research.brighton.ac.uk/files/208088/Getting_the_Tail_to_Wag.pdf (Accessed: 18 October 2025).

AI Platform & Tool Documentation

Alibaba Cloud (2025) Qwen. Available at: https://qwen.ai/ (Accessed: 18 October 2025).
Apple (2025) Use ChatGPT with Apple Intelligence. Available at: https://support.apple.com/guide/mac-help/use-chatgpt-with-apple-intelligence-mchlfc5cf131/mac (Accessed: 18 October 2025).
LangChain (2025) Local LLMs. Available at: https://python.langchain.com/docs/how_to/local_llms/ (Accessed: 18 October 2025).
Manus (2025) Manus: Hands On AI. Available at: https://manus.im/home (Accessed: 18 October 2025).
Meta AI (2025) Meta AI. Available at: https://en.wikipedia.org/wiki/Meta_AI (Accessed: 18 October 2025).
Mistral AI (2025) Le Chat enterprise AI assistant. Available at: https://mistral.ai/products/le-chat (Accessed: 18 October 2025).
Moonshot AI (2025) Kimi (chatbot). Available at: https://en.wikipedia.org/wiki/Kimi_(chatbot) (Accessed: 18 October 2025).
OpenAI (2025) How your data is used to improve model performance. Available at: https://help.openai.com/en/articles/5722486-how-your-data-is-used-to-improve-model-performance (Accessed: 18 October 2025).
xAI (2025) Grok. Available at: https://x.ai/grok (Accessed: 18 October 2025).

Technical Blogs & Industry Analysis

Arsturn (2025) Claude vs. Local LLMs: Choosing the Right AI for Your Business. Available at: https://www.arsturn.com/blog/claude-vs-local-llms-choosing-the-right-ai-for-your-business (Accessed: 18 October 2025).
Binadox (2025) Best Local LLMs for Cost-Effective AI Development in 2025. Available at: https://www.binadox.com/blog/best-local-llms-for-cost-effective-ai-development-in-2025/ (Accessed: 18 October 2025).
Built In (2025) 5 Aspects of Data Privacy to Consider in AI Adoption. Available at: https://builtin.com/articles/ai-adoption-data-privacy (Accessed: 18 October 2025).
CamoCopy (2025) AI Assistants Privacy Report. Available at: https://www.camocopy.com/ai-assistants-privacy/ (Accessed: 18 October 2025).
CreativeTechs (2025) Keep Sensitive Data Private by Disabling AI Training Options. Available at: https://www.creativetechs.com/2025/09/05/keep-sensitive-data-private-by-disabling-ai-training-options/ (Accessed: 18 October 2025).
ema.co (2025) Best Open-Source LLMs for Enterprises in 2025. Available at: https://www.ema.co/additional-blogs/addition-blogs/best-open-source-llms (Accessed: 18 October 2025).
F11Photo (2025) Keep Sensitive Data Private by Disabling AI Training Options. Available at: https://f11photo.com/2025/09/05/keep-sensitive-data-private-by-disabling-ai-training-options/ (Accessed: 18 October 2025).
Genspark (2025) Genspark: Super AI Agent. Available at: https://play.google.com/store/apps/details?id=ai.mainfunc.genspark (Accessed: 18 October 2025).
Ignesa (2025) The Truth About Local LLMs: When You Actually Need Them. Available at: https://ignesa.com/insights/the-truth-about-local-llms-when-you-actually-need-them/ (Accessed: 18 October 2025).
Medium (2025) The Great AI Privacy Divide: Claude, ChatGPT, Gemini, and Copilot. Available at: https://medium.com/@michael_79773/the-great-ai-privacy-divide-one-year-later-two-worlds-apart-e74ce6187f1f (Accessed: 18 October 2025).
n8n (2025) How to run local LLMs for private, secure, and cost-effective AI automation. Available at: https://blog.n8n.io/local-llm/ (Accessed: 18 October 2025).
NetFriends (2025) AI Privacy Policy Evaluation: ChatGPT vs Gemini vs Claude. Available at: https://www.netfriends.com/blog-posts/ai-privacy-policy-evaluation-chatgpt-vs-gemini-vs-claude (Accessed: 18 October 2025).
SBTDC (2025) Protecting Your Data: Responsible AI Practices for Small Businesses. Available at: https://sbtdc.org/blog/protecting-your-data-responsible-ai-practices-for-small-businesses (Accessed: 18 October 2025).
Sentisight (2025) Open Source LLMs You Can Actually Deploy in 2025. Available at: https://www.sentisight.ai/open-source-llms-you-can-actually-deploy/ (Accessed: 18 October 2025).
Signity Solutions (2025) On Premise vs Cloud Based LLM: Which Is Right for Your Industry? Available at: https://www.signitysolutions.com/blog/on-premise-vs-cloud-based-llm (Accessed: 18 October 2025).
Skywork AI (2025) How to Keep ChatGPT Data Private: A 2025 Guide to Enterprise AI Security. Available at: https://skywork.ai/skypage/en/How-to-Keep-ChatGPT-Data-Private:-A-2025-Guide-to-Enterprise-AI-Security/1976537200450400256 (Accessed: 18 October 2025).

Your Role: The Expert Evaluation

Candid feedback is sought on this artifact across four key areas:

Theoretical Coherence

Is the link between ALeM and DCT sound?

Practical Utility

Is this framework genuinely useful and simple to implement?

Completeness

What critical gaps, assumptions, or blind spots have I missed?

Contextual Appropriateness

Does this reflect the operational reality of UK micro-businesses?

Have Feedback?

Your insights as an expert reviewer are crucial for this research. Please share your thoughts, critiques, or suggestions directly with the researcher.

Send Feedback via Email Book a 15-Minute Call

Choose to send a pre-filled email or book a guided walkthrough call.