Introduction: Understanding Bank Loan Rejections

Behind every funding decision lies a deeper logic, one that most SME owners never see. Banks and fintech lenders alike are decoding your leadership signals, assessing behavioural patterns, and drawing risk conclusions long before you walk through the door, send an email or pick up the phone. This article reveals the visible part of that process, and if you want the unfiltered version, the download at the end reveals more about SME loan rejections and successes.

If you’re an SME owner, you’ve probably felt the frustration of a bank turning you down for a loan. Maybe you had strong sales, a clear vision, and a solid business plan, and still, the answer was no. You’re not alone: the success rate for SME loan applications has fallen from 80% in 2018 to around 50% in 2023.

It’s easy to take rejection personally, but bank decisions are rarely emotional. They’re based on strict lending criteria and risk models. This article explains why banks say no, and what you can do to turn that into a yes.

Why SME Funding Matters

SMEs are the engine of the UK economy. They make up 99.9% of private sector businesses, employ 16.6 million people, and generate £2.8 trillion in annual turnover. When these businesses can’t access the capital they need, the ripple effects hit innovation, job creation, and growth.

Overcoming funding barriers isn’t just about individual businesses, it’s vital to the wider economy.

SME Loan Rejections: Why Banks Say No

Many SME owners assume rejection means something personal: “They didn’t like me,” or “They don’t understand small businesses.” In reality, banks want to lend, it’s how they make money. But they also have to manage risk.

Banks use objective criteria to assess loan applications:

• Cash flow and financial resilience
• Value and reliability of collateral
• Financial history and creditworthiness
• Leadership team experience
• Sector and macroeconomic risk

Understanding these filters helps you prepare a more credible, compelling funding case.

SME Funding Criteria: What Banks Are Really Looking For

Based on years advising SMEs, here are the five areas banks focus on:

1. Cash Flow Management: Clear ability to service debt from operations.
2. Collateral Quality: Reliable assets, sensibly valued.
3. Track Record: Financial history that inspires confidence.
4. Management Credibility: A capable, experienced leadership team.
5. Risk Mitigation: Awareness of the risks and a plan to manage them.

Weakness in just one of these areas can derail an application. Strengthening them all puts you on a much stronger footing.

How to Overcome SME Loan Rejections: Five Practical Steps

As a Fractional CFO, I’ve worked with many SMEs to improve funding outcomes. Here’s a five-step action plan to help you become more “bankable”:

1. Build a Reliable Cash Flow Forecast: It should be clear, detailed, and stress-tested.
2. Secure Your Assets: Use realistic valuations and professional assessments.
3. Show Your Track Record: Use clean financial statements to tell a story of reliability.
4. Strengthen the Team: Appoint experienced advisors or NEDs if needed.
5. Demonstrate Risk Awareness: Show that you’ve identified risks and planned for them.

SME Loan Rejection vs Approval: A Case Study

Consider two tech SMEs, both seeking £500,000 in growth capital:

• SME A had a strong product but a vague business case. Their cash flow projections lacked detail, they had no collateral, and their team was light on experience. During the loan interview, the founder focused on vision but couldn’t explain the numbers.
• SME B took a different approach. They brought in a Fractional CFO (me), built a professional cash flow forecast, secured the loan against equipment, and added a non-executive director with sector experience. Their pitch focused on risk, return, and financial control. They got the loan.

The difference wasn’t product quality, it was presentation, credibility, and preparation.

Beyond Banks: The Rise of Fintech and Embedded Finance

The SME funding world has changed. Modern fintech lenders and embedded finance platforms have created new paths to capital.

Companies like Capchase, Funding Circle, and embedded providers like Shopify Capital and Amazon Lending tap directly into your business systems, from accounting software to payment processors, to assess eligibility. These platforms use real-time data and algorithms to assess revenue quality, churn, seasonality, and growth potential.

What they offer:

• Loan sizes from £10,000 to £5 million, based on real-time performance
• Security structures from unsecured advances to revenue-share models or personal guarantees
• Interest rates that vary based on business health, market rates, and risk profile
• Repayment terms tailored to cash flow: flat fees, revenue-based, or fixed-term

The speed is often faster than banks, but so is the scrutiny. Clean systems, up-to-date metrics, and transparent data are essential to avoid SME loan rejections.

These fintech options won’t suit every business. But for digital-first SMEs, growth-stage startups, or those with non-traditional revenue patterns, they can be a compelling alternative.

Other Alternatives: Know Your Options

• Private Lenders: More flexible but typically more expensive
• Equity Investors: Strategic backing, but with ownership dilution
• Government-Backed Schemes: Help bridge gaps with guarantees
• Invoice or Asset Finance: Useful for short-term cash flow or equipment purchases

Each has its trade-offs. The key is matching the right funding type to your business stage and goals.

Final Word: Don’t Let a ‘No’ Stop You

Much of what shapes funding success isn’t written in the criteria, it’s inferred in how you operate, how you respond, and how much control you demonstrate. Banks rarely say this out loud, but those of us who’ve sat across the table know the unspoken rules. If you want to understand the hidden levers that influence decisions, from pre-application scoring signals to the behavioural cues that shape your interest rate, the full download is designed to give you that edge.

Rejection isn’t the end. It’s a prompt to reassess and reposition. Focus on building financial clarity, credible leadership, and a realistic view of your risk and growth plan.

Explore all your options, from traditional banks to fintech lenders, and prepare thoroughly. Great businesses get funded when they speak the language of lenders and face less SME loan rejections.

If you need help shaping your funding strategy, give me a call. We’ll explore your goals, assess your options, and create a clear plan.

Download my free guide, “5 Steps to Funding Success for SMEs” to get started today.

Other resources: https://www.british-business-bank.co.uk/business-guidance

The post SME Loan Rejections: Why Banks Say No and How to Get Funded appeared first on Accural Limited.

As AI continues to reshape the business landscape, it is clear that traditional approaches to Learning and Development (L&D) require a fundamental overhaul. AI is not just another tool; it is a transformative force that is redefining how businesses operate, make decisions, and engage with stakeholders. For UK SMEs, a robust and fit-for-purpose AI training strategy is no longer optional. It is essential. This strategy must address the specific challenges and opportunities that SMEs encounter in an AI-driven world.

What Makes AI Training for SMEs Unique and Challenging?

A well-designed AI training for SMEs must account for rapid change, varied applications, and ethical challenges.

A Rapidly Evolving Landscape

The world of AI moves quickly. What is relevant today may be outdated tomorrow. Training must be designed to adapt, with regular updates that ensure continued relevance.

Diverse Applications

AI is not a single technology. From chatbots to predictive analytics to image recognition, it encompasses a wide range of applications. Training should be tailored to these specific use cases.

Ethical Imperatives

AI introduces complex ethical questions such as data privacy, algorithmic bias, and transparency. Training should prepare staff to use AI responsibly and with awareness of these challenges.

The Complexity of Completeness

No individual can understand the entire AI landscape alone. A strong AI strategy depends on shared learning and collaborative validation of both ideas and execution. This ensures that the organisation remains on the right path.

Collaborative Approaches to AI Training for SMEs

Ideally, a CTO and a dedicated L&D specialist can lead AI adoption. However, integrating AI is rarely simple, regardless of in-house expertise. Barriers such as resistance to change, rigid workflows, or misinformation can arise from unexpected parts of the organisation.

To overcome these, a more distributed approach to training is required. This should reflect an insights everywhere mindset:

• Involve All Levels
  Invite input from employees across all departments. Their lived experience reveals challenges and opportunities that leadership alone might not see.
• Collaborate with Experts
  In-house AI experts can help ensure training is practical and aligned with evolving best practices.
• Make Training Role-Specific
  Different teams will use AI in different ways. Tailor training to reflect the tools and decisions relevant to each role.
• Build a Feedback Culture
  Encourage regular sharing of experiences and insights. Collaboration across departments improves understanding and breaks down silos.
• Adopt an Iterative Approach
  Replace fixed training playbooks with dynamic versions that evolve based on feedback and results.
• Prioritise Practical Application
  Theory is important, but hands-on experience is essential. Real-world use of AI tools should be a core part of the learning journey.
• Anticipate Concerns
  Proactively address doubts or fears about AI. Clarify misconceptions, share success stories, and provide reassurance through ongoing support.
• Celebrate Change Leaders
  Identify early adopters, encourage their momentum, and make space for them to lead by example.

While the presence of a CTO or L&D expert is valuable, successful AI integration requires a broader cultural shift. Training should be inclusive, collaborative, and grounded in continuous improvement.

Improving SME AI Training Through Experience Strategy and Feedback

The introduction of AI will trigger reactions from both employees and customers. Many previous tech rollouts created pressure for constant availability and unsustainable productivity. Will AI repeat this pattern? Possibly. To avoid this, businesses must establish active feedback systems that allow early course correction.

• Balance Performance with Wellbeing
  As AI improves efficiency, ensure it also supports work-life balance and positive customer interactions. Monitor and adjust accordingly.

Embedding Continuous Learning in AI Training for SMEs

• Provide Regular Updates
  Training materials must be reviewed and refreshed regularly to keep pace with new developments.
• Create Peer Learning Communities
  Internal groups can share challenges and successes, supporting each other in applying AI effectively.
• Bring in Outside Perspectives
  Collaborating with external advisors or training partners can offer new insights and fresh ideas.
• Resources
  • https://www.cipd.org/uk/topics/artificial-intelligence-workplace/
  • https://www.oecd.org/en/publications/ai-and-the-future-of-skills-volume-1_5ee71f34-en.html
  • https://www.gov.uk/government/publications/implementing-the-uks-ai-regulatory-principles-initial-guidance-for-regulators/implementing-the-uks-ai-regulatory-principles-initial-guidance-for-regulators

AI training should not be viewed as a one-off requirement. It is a strategic investment in your people. By focusing on capability building rather than replacement, and by ensuring robust learning and feedback systems, UK SMEs can position themselves to fully realise AI’s potential. This includes improved performance, greater innovation, and increased job satisfaction.

With the right approach to AI training for SMEs, businesses can build resilience, creativity, and competitive advantage.

This article is part of a wider thought series exploring AI adoption in UK SMEs. https://accural.co.uk/ai-integration-uk-smes-roadmap/

The post How To Master AI Training For SMEs And Transform Your Business appeared first on Accural Limited.

Imagine during the onset of emails in the workplace if each individual used their own accounts, or multiple accounts for different areas of the business. I believe this is what is happening in UK’s SMEs, individuals are using AI in their own workflows with a hands-off hush in most businesses (OK so this is belief and somewhat anecdotal, not evidenced-based but I do believe representative of reality).

The Urgent Need for an AI Roadmap

Some team members will be harnessing AI tools, it could be anywhere. Finance, HR, legal, customer service, etc. Others may remain untouched or hesitant to try or to understand. I’m convinced that if we were to plot productivity and quality metrics on a graph, a chasm would emerge between these approaches in the short to medium term. This isn’t something that should be left to chance, to harness the collective potential of AI, a cohesive roadmap is needed.

• Objective Assessment: Begin with a clear, unbiased assessment of current operations. Identify areas ripe for AI integration, not just in terms of tasks but in terms of strategic objectives.
• Pilot Programs: Before a full-scale rollout, initiate pilot programs. These serve as testbeds, providing invaluable insights into the AI tools’ efficacy and areas of improvement.
• Data Infrastructure: AI thrives on data. Establish robust data collection, storage, and processing systems. Ensure data quality and integrity, as the bedrock of AI initiatives.
• Training and Development: Equip your workforce with the necessary skills to work alongside AI tools. This includes not just technical training but also fostering a culture of adaptability and continuous learning. I am going to talk more about this later, as it’s so important not to just slide in alongside the other internal training methods.
• Governance and Ethics: Lay down clear guidelines on the ethical use of AI to address data privacy considerations, fairness in AI decisions, and transparency in algorithms from a macro perspective, but also from an organisational pathway perspective.
• Feedback Mechanisms: Create channels for continuous feedback. As AI tools are deployed, gather insights from end-users, be it employees or customers, and iterate based on this feedback.

Holistic Growth

The ultimate objective of AI integration in UK SMEs isn’t just individual augmentation but achieving a holistic competitive advantage for the organisation.

Departmental Synergy: Ensure AI tools used across different departments “talk” to each other. For instance, insights from a sales AI tool should inform inventory management systems or customer experience tools.

Strategic Re-allocation: As AI takes over certain tasks and frees up human resources for more strategic, creative roles. This doesn’t mean downsizing but upskilling and redirecting talent to areas where human intuition and creativity shine.

Scalability and Future-Proofing: Design the AI roadmap with an eye on the future. As the business grows, the AI systems should scale seamlessly. Moreover, remain agile to incorporate newer AI innovations as they emerge. I will be discussing a little more the flux internally and externally that is coming as innovation leads to consolidation and niching

I conclude this piece by considering that the journey of AI integration is not a sprint but a marathon. It requires foresight, strategy, and the deliberate crafting of an architecture that facilitates not just the adoption of AI tools, but the complete transformation of the organisation. The end goal is clear: an organisation that’s not just more productive but more resilient, adaptive, and poised for future growth while the world changes around and within.

This article forms part of wider thought piece in relation to AI adoption guide for UK SMEs.

The post Crafting the Roadmap for Organisational Transformation: Deliberate AI appeared first on Accural Limited.

How does AI fit into this picture? At the moment, AI seems to be an impending mainstream force, lurking just beneath the surface. Early adopters are actively integrating it into their workflows, while laggards, reminiscent of those slow to adapt during the internet’s infancy, are repeating the same misjudgements about the inevitable change.

We have entered a phase where individuals and organisations are beginning to understand the power of AI, but there is such an incredibly wide scope. Imagine standing at the edge of the Grand Canyon for the first time. Your toes touch the precipice, and as you look out, the sheer magnitude takes your breath away. It’s a blend of awe, a twinge of fear, and an overwhelming realisation of the vastness before you. This is the scale at which we should visualize the emergence of AI in the business world, especially for UK SMEs.

AI isn’t just a new tool or software; it’s akin to the discovery of electricity or the invention of the internet. It’s a fundamental shift in how businesses operate, make decisions, and engage with their customers. The possibilities are as expansive as they are intricate. From automating mundane tasks, and predicting market trends years in advance, to personalising customer experiences at an unprecedented level, AI is redefining the rulebook. It’s impossible to overstate the magnitude in front of us. The impact of AI Adoption in UK SMEs is significant.

For the adventurous UK SMEs ready to dive deep, the promise is immense. Efficiency gains, new revenue streams, and the chance to leapfrog competitors. But, with great promise comes great responsibility. Those who hesitate, misunderstand, or underestimate this force risk not just lagging but potentially fading into obscurity. It evokes memories of businesses that dismissed the internet as a passing fad, only to be left in its digital wake.

But it’s not just about keeping up. It’s about understanding the depth and breadth of what AI offers. For every SME, the AI journey will be unique. What works for a tech start-up in Manchester might not resonate with a family-run bakery in Cornwall. Tailoring AI to individual needs, aspirations, and challenges is the order of the day.

This is a watershed moment. A time of unparalleled opportunity, but also a period that demands caution, foresight, and a touch of reverence for the transformative power at our fingertips. As UK SMEs, the choice is clear: harness this leviathan’s potential or watch from the sidelines. Either way, the landscape of business is changing, and it promises to be a thrilling ride.

Much like the tantalising allure that performance-enhancing substances once held for athletes, AI presents a world of untapped potential for businesses. However, with these immense possibilities comes the imperative for oversight. Just as sports needed clear guidelines and regulations around substance use, AI requires robust regulation and thoughtful corporate policies to ensure its power is harnessed ethically, responsibly, and for the collective good.

This article forms part of wider thought piece in relation to AI adoption guide for UK SMEs.

The post The New Competitive Edge appeared first on Accural Limited.

In sports, athletes have always sought that extra edge, the tiniest advantage to outpace, outlast or outplay the competition. Modern athletes have turned to sports psychologists, advanced equipment, and sometimes, controversially, to performance-enhancing substances. AI Adoption in UK SMEs can use this as a metaphor.

How does AI fit into this picture? At the moment, AI seems to be an impending mainstream force, lurking just beneath the surface. Early adopters are actively integrating it into their workflows, while laggards, reminiscent of those slow to adapt during the internet’s infancy, are repeating the same misjudgements about the inevitable change.

We have entered a phase where individuals and organisations are beginning to understand the power of AI, but there is such an incredibly wide scope. Imagine standing at the edge of the Grand Canyon for the first time. Your toes touch the precipice, and as you look out, the sheer magnitude takes your breath away. It’s a blend of awe, a twinge of fear, and an overwhelming realisation of the vastness before you. This is the scale at which we should visualize the emergence of AI in the business world, especially for UK SMEs.

AI isn’t just a new tool or software; it’s akin to the discovery of electricity or the invention of the internet. It’s a fundamental shift in how businesses operate, make decisions, and engage with their customers. The possibilities are as expansive as they are intricate. From automating mundane tasks, and predicting market trends years in advance, to personalising customer experiences at an unprecedented level, AI is redefining the rulebook. It’s impossible to overstate the magnitude in front of us. The impact of AI Adoption in UK SMEs is significant.

For the adventurous UK SMEs ready to dive deep, the promise is immense. Efficiency gains, new revenue streams, and the chance to leapfrog competitors. But, with great promise comes great responsibility. Those who hesitate, misunderstand, or underestimate this force risk not just lagging but potentially fading into obscurity. It evokes memories of businesses that dismissed the internet as a passing fad, only to be left in its digital wake.

But it’s not just about keeping up. It’s about understanding the depth and breadth of what AI offers. For every SME, the AI journey will be unique. What works for a tech startup in Manchester might not resonate with a family-run bakery in Cornwall. Tailoring AI to individual needs, aspirations, and challenges is the order of the day.

This is a watershed moment. A time of unparalleled opportunity, but also a period that demands caution, foresight, and a touch of reverence for the transformative power at our fingertips. As UK SMEs, the choice is clear: harness this leviathan’s potential or watch from the sidelines. Either way, the landscape of business is changing, and it promises to be a thrilling ride.

Much like the tantalising allure that performance-enhancing substances once held for athletes, AI presents a world of untapped potential for businesses. However, with these immense possibilities comes the imperative for oversight. Just as sports needed clear guidelines and regulations around substance use, AI requires robust regulation and thoughtful corporate policies to ensure its power is harnessed ethically, responsibly, and for the collective good.

Crafting the Roadmap for Organisational Transformation: Deliberate AI

As businesses stand at the crossroads of the AI revolution, the question shouldn’t just be about whether to adopt AI, but how to do so deliberately with intention and strategy. The ad-hoc adoption of AI tools might yield sporadic benefits, but a systematic, deliberate integration is required to deliver transformative results for the AI Adoption in UK SMEs

Imagine during the onset of emails in the workplace if each individual used their own accounts, or multiple accounts for different areas of the business. I believe this is what is happening in UK’s SMEs, individuals are using AI in their own workflows with a hands-off hush in most businesses (OK so this is belief and somewhat anecdotal, not evidenced-based but I do believe representative of reality).

The Urgent Need for an AI Roadmap

Some team members will be harnessing AI tools, it could be anywhere. Finance, HR, legal, customer service, etc. Others may remain untouched or hesitant to try or to understand. I’m convinced that if we were to plot productivity and quality metrics on a graph, a chasm would emerge between these approaches in the short to medium term. This isn’t something that should be left to chance, to harness the collective potential of AI, a cohesive roadmap is needed.

• Objective Assessment: Begin with a clear, unbiased assessment of current operations. Identify areas ripe for AI integration, not just in terms of tasks but in terms of strategic objectives.
• Pilot Programmes: Before a full-scale rollout, initiate pilot programmes. These serve as testbeds, providing invaluable insights into the AI tools’ efficacy and areas of improvement.
• Data Infrastructure: AI thrives on data. Establish robust data collection, storage, and processing systems. Ensure data quality and integrity, as the bedrock of AI initiatives.
• Training and Development: Equip your workforce with the necessary skills to work alongside AI tools. This includes not just technical training but also fostering a culture of adaptability and continuous learning. I am going to talk more about this later, as it’s so important not to just slide in alongside the other internal training methods.
• Governance and Ethics: Lay down clear guidelines on the ethical use of AI to address data privacy considerations, fairness in AI decisions, and transparency in algorithms from a macro perspective, but also from an organisational pathway perspective.
• Feedback Mechanisms: Create channels for continuous feedback. As AI tools are deployed, gather insights from end-users, be it employees or customers, and iterate based on this feedback.

Holistic Growth

The goal of AI integration isn’t just to augment individual capacity but to elevate (or maintain in relation to the competition) the organisation’s competitive advantage holistically.

Departmental Synergy: Ensure AI tools used across different departments “talk” to each other. For instance, insights from a sales AI tool should inform inventory management systems or customer experience tools.

Strategic Re-allocation: As AI takes over certain tasks and frees up human resources for more strategic, creative roles. This doesn’t mean downsizing but upskilling and redirecting talent to areas where human intuition and creativity shine.

Scalability and Future-Proofing: Design the AI roadmap with an eye on the future. As the business grows, the AI systems should scale seamlessly. Moreover, remain agile to incorporate newer AI innovations as they emerge. I will be discussing a little more the flux internally and externally that is coming as innovation leads to consolidation and niching

I conclude this section by considering that the journey of AI integration is not a sprint but a marathon. It requires foresight, strategy, and the deliberate crafting of an architecture that facilitates not just the adoption of AI tools, but the complete transformation of the organisation. The end goal is clear: an organisation that’s not just more productive but more resilient, adaptive, and poised for future growth while the world changes around and within.

AI Training: Beyond Traditional L&D

As AI permeates the business landscape, the traditional approach to Learning & Development (L&D) must be reimagined. AI isn’t just another tool; it’s a transformative force that reshapes how businesses operate, make decisions, and engage with stakeholders. Training for AI, therefore, demands a unique approach.

The Uniqueness of AI Training

• Dynamic Landscape: The world of AI is continuously evolving. What’s relevant today might be obsolete tomorrow. Training programmes must be agile, with regular updates to stay current.
• Multiplicity of Applications: AI isn’t monolithic. From chatbots to predictive analytics to image recognition, AI has a diverse range of applications. Training must be tailored to these specific use cases.
• Ethical Considerations: Using AI brings about a host of ethical considerations, from data privacy to algorithmic fairness. Training should address these nuances, ensuring that employees use AI responsibly and accurately.
• The Complexity of Completeness: Nobody is capable of knowing everything, so ensuring the landscape relevant to your business is maintained will need to be a team effort with a hopper of validation (ideas and execution) required to ensure the organisation moves in the right direction.

Designing AI Training: A Collaborative Endeavour

In an ideal scenario, organisations equipped with a Chief Technology Officer (CTO) and a dedicated learning and development (L&D) expert are well-poised to spearhead AI Adoption in UK SMEs. One might naturally assume these roles to be at the epicentre of the transformative. However, the path to such a complex integration as AI isn’t always straightforward, regardless of the expertise on hand.

While the CTO and L&D expert bring invaluable technical and training insights, roadblocks can emerge from the most unexpected corners of an organisation. Resistance to change, entrenched processes, or even misinformation will impede AI adoption.

Given this landscape, it’s imperative for the L&D strategy to adopt an ‘insights everywhere’ approach. Rather than being solely top-down, the strategy should:

• Solicit Feedback from All Levels: Engage employees across hierarchies and departments. Their on-the-ground insights can offer a more holistic view of the actual challenges and opportunities and highlight concerns.
• Co-pilot with Experts: Drawing upon in-house AI experts can ensure training remains pragmatic and aligned with best practices.
• Individually Tailoring: Reflecting the varied AI applications across roles, training modules should be role-specific, ensuring direct relevance.
• Foster a Collaborative Culture: Encourage a culture where feedback isn’t just sought, but is actively shared. Break down silos, and promote inter-departmental collaboration.
• Iterate an Approach: Instead of a fixed playbook, consider a dynamic, evolving one. As AI tools are integrated and feedback is gathered, the playbook should be refined accordingly.
• Contain Hands-on Experience: Beyond theory, practical exercises and real-world scenarios should be integral, allowing employees direct interaction with AI tools.
• Address Concerns Proactively: Before resistance becomes a significant roadblock, proactively address concerns. This might involve dispelling AI myths, highlighting success stories, or even providing additional resources and training.
• Utilise Change Champions: Seek out and celebrate early adopters, incentivise and promote knowledge and innovation sharing.

In essence, while the expertise of a CTO and L&D specialist is undeniably crucial (where available), a successful AI adoption strategy transcends these roles. By embracing an ‘insights everywhere’ perspective, organizations can craft a more inclusive, adaptable, and effective roadmap for AI integration.

Feedback Mechanisms and Experience Strategy

The introduction of AI will inevitably lead to a spectrum of feedback from both employees and customers. To navigate this, a robust customer and employee experience strategy is essential. We’ve witnessed with past technological implementations an expectation of heightened productivity. However, the reality was the birth of an ‘always-on’ culture, the ramifications of which we still grapple with today. The question looms: will AI follow a similar trajectory? To mitigate such unforeseen consequences, feedback mechanisms must be proactive, comprehensive, and agile, allowing for real-time adjustments and refinements.

A Continuous Learning Ethos

• Regular Refreshers: The dynamic AI landscape demands periodic updates to training content.
• Communities of Practice: Peer-to-peer learning forums can facilitate dialogue, allowing employees to share AI challenges and insights.
• External Collaborations: Tapping into external expertise can offer fresh insights, enriching the overall training experience.

AI training is not a mere procedural step; it’s a strategic pivot. The focus should be on enhancing individual and team capabilities, not replacing them. By embracing this perspective and ensuring robust feedback mechanisms, businesses can truly harness AI’s potential, yielding not only economic benefits but also fostering innovation, creativity, and enhanced job satisfaction.

Navigating the Flux: Future-Proofing in an Ever-Changing AI Landscape

Imagine a supply chain in complete flux. Goods, processes, and routes constantly evolving, with components interchanging and systems reconfiguring. This vivid scenario mirrors the current state of AI technology. The tools and methodologies that seem groundbreaking today could be passé tomorrow, and today’s hard work might be rendered obsolete sooner than anticipated.

How do we measure the ROI with constantly shifting sands amidst a backdrop of fear and an already complex economic reality?

The Constantly Shifting Sands of AI
AI’s rapid evolution, driven by relentless research, innovation, and competition, means that new tools emerge continually, while existing ones consolidate, fragment, or even disappear. This dynamism poses a challenge: How can businesses invest time, resources, and money into AI implementations when the landscape is so volatile? How can we drive AI Adoption in UK SMEs?

Staying True Amidst the Flux

While the temptation might be to chase the latest AI trends, it’s crucial to remain anchored to the core business model. The true north for any organisation will remain its mission, values, and the value it offers to its customers. However, this constancy must be balanced against the changing expectations and needs of customers, who themselves are grappling with a rapidly changing delivery landscape.

I’m going to draw an analogy with another area of technology, the API or Application Programming Interfaces. At a basic level, APIs allow different software applications to communicate with each other. In the context of AI and the broader tech landscape, APIs represent a philosophy of flexibility and interconnectivity.

• Adaptability: Just as a supply chain must adjust to new goods or routes, APIs allow businesses to quickly integrate new tools or technologies without overhauling their entire system.
• Interoperability: In a world where tools consolidate and fracture, APIs ensure that different systems, old and new, can work together seamlessly.
• Customer-Centricity: As customer needs evolve, APIs allow businesses to quickly pivot, adding new features or integrations that enhance the customer experience.
• Supplier Synergy: On the flip side, businesses can also adjust to changes from suppliers or partners, ensuring that backend processes remain smooth and efficient.

In conclusion, the AI landscape, much like our earlier example of a supply chain in flux, demands agility, foresight, and adaptability. However, by leveraging the API approach and remaining anchored to their core mission, businesses can navigate this dynamic terrain. The goal is to be fluid enough to harness the latest AI innovations, mature enough to understand the direction of travel, yet stable enough to offer consistent value to customers, irrespective of the technological churn.

Rigorous Quality Assurance (QA) in AI Integration

In my opinion, the short-term impact of AIvolution in the workspace is going to require more focus on QA in the workplace. This is not merely a traditional testing phase but a foundational step to ensure that the AI operates as expected and brings tangible benefits to the business. As guardians of the integrative landscape and use cases, QA is crucial to safeguarding quality and ROI.

The Essence of QA in AI

• Predictability and Reliability: Unlike traditional software that can be tested with defined inputs and expected outputs, AI often operates in the realm of probabilities. QA ensures that the AI’s predictions and actions are consistent and reliable within acceptable bounds (ethics, quality and in line with the overall strategic direction).
• Handling Variability: AI models are trained on vast datasets and are expected to handle diverse inputs in real-world scenarios. Through rigorous QA, we can ensure that the AI performs well across a spectrum of cases, including edge cases that might not have been explicitly trained on.
• Ethical Considerations: AI models can unintentionally perpetuate biases present in their training data. A thorough QA process will test for and mitigate these biases, ensuring that the AI’s decisions are fair and unbiased.

Tailoring AI to Business Needs

Every business, particularly SMEs, grapples with its distinct set of challenges and requirements. Quality Assurance (QA) in the realm of AI goes beyond mere functionality checks. It’s about tailoring AI solutions to align with the unique nuances of each business. For AI to truly drive performance enhancements, it must meld seamlessly with a business’s pre-existing systems and operational workflows. Strong QA leadership is pivotal in guaranteeing this harmonious integration.

In the rapidly evolving landscape of AI, we’re witnessing groundbreaking intersections between departments, leading to innovation and challenge in equal measure. For instance, I’ve had first-hand experience with use cases where an interim CFO, traditionally not a tech role, leveraged Python to craft a bespoke software tool, streamlining processes within the finance team. In another instance, sales and marketing personnel engaged with ChatGPT to navigate contract clauses, distilling the essentials before routing them to external legal entities for final approvals.

Such innovative applications of AI underscore a transformative shift: AI tools, when wielded effectively, can channel efforts towards laser-focused objectives. The result? A magnified return on investment and an acceleration in decision-making processes.

Feedback Loops and Continuous Improvement

Iterative Refinement: AI is not a one-off solution. It learns and improves. A robust QA phase sets the foundation for continuous feedback loops where the AI is constantly refined based on real-world performance and feedback.

• Stakeholder Trust: Before an AI system can be fully deployed and relied upon, stakeholders (from employees to management) need to trust its outputs. A rigorous QA phase, where the AI is tested and its decisions validated, is crucial to building this trust.
• Adaptability: The business landscape is ever-evolving, and so are the challenges SMEs face. Through QA, businesses can ensure that their AI solutions are adaptable and can evolve with changing needs.
• Scalability: As businesses grow, their AI solutions need to scale with them. QA tests for scalability, ensuring that the AI can handle increased loads and complexities as the business expands.

In essence, while the allure of AI is undeniable, diving headfirst without a rigorous QA phase is akin to sailing turbulent seas without a compass. Although this section is not about quality assurance ensuring AI not only promises but delivers, acting as the bridge between potential and realised benefits for SMEs it is fundamentally about the world with AI and how human interfaces are going to optimise ROI in the short term. Active monitoring and engagement with the data flow and use cases (currently often determined at an individual level) is going to be a key factor in organisational success here.

The Human Heartbeat in the AI Revolution

Why AI is the SMEs’ New Power Tool

At its core, AI isn’t just about algorithms and data; it’s about augmenting our capabilities to unlock unprecedented potential. For SMEs, which often operate with limited resources, AI offers a level playing field, enabling them to compete with larger enterprises and each other in novel ways. There are many case studies from the last few years illuminating businesses harnessing AI for operational efficiency. But it’s not just about efficiency; it’s about reimagining business models, forging deeper customer relationships, and innovating at a pace previously thought impossible.

We are so used to incorporating IT into our organisations, that maybe we aren’t generally able to conceptualise the radical difference that the AI copilot can make. As with any transformative journey, challenges abound. SMEs often grapple with concerns like data privacy, the steep learning curve of AI tools, or even resistance from traditionalist factions within their teams. The key lies in viewing these not as insurmountable barriers but as waypoints. By harnessing a culture of continuous learning and challenge, promoting cross-departmental collaborations, and staying updated with the evolving AI landscape, these challenges can be transformed into growth levers.

This isn’t just about content writing, image development, and presentation creation. It’s not just about meeting schedules, email organisation or email response prompting. From a CFO it is also not just about efficiencies, accounts prep, due diligence and shareholder communications.

Emerging trends hint at even more personalised customer experiences, AI-driven business strategies, and tools that can predict market shifts with uncanny accuracy. For the naysayers and sceptics, it’s essential to remember: just as businesses that sidestepped the internet revolution found themselves sidelined, ignoring the AI wave could have similar repercussions. However, this isn’t a narrative of fear but one of immense promise.

Your Personal Guide in the AI Jungle

In my own journey, I’ve witnessed the transformative power of AI firsthand. From automating mundane tasks to unlocking insights from heaps of data, the potential is amazing. But it isn’t perfect, it’s a reflection of the humanity which created the large language models (LLMs) like ChatGPT, so mistakes and errors are bound. We are only human, and this tool is based on human knowledge. But more than the tools, it’s the stories that resonate. Like when a team, initially resistant to AI, experienced the magic of predictive analytics and became its strongest advocate. Or when a small business, on the brink of closure, turned its fortunes around by leveraging AI-driven market insights.

The Invitation

This is more than just a technological shift; it’s a cultural revolution. As we stand at this crossroads, I invite you to engage, discuss, and delve deeper. Whether you’re an AI novice or a tech-savvy entrepreneur, there’s a world of discovery awaiting. Let’s embark on this journey together, demystifying AI, debunking myths, and most importantly, unlocking the immense potential that lies at the intersection of technology and human ingenuity.

In my next thought pieces around AI and adoption in SMEs, I will be considering return on investment (ROI) and some more thoughts focussed on the quality assurance piece, ethics and tools to leverage. I think it would be interesting to focus on some roles too. In this next phase, I believe this is critical.

Finally, I conclude with my thoughts on a plan to adopt AI into SMEs.

Ten-Point Plan for AI Adoption in SMEs

Of course, a thought piece like this has to end with a point beyond the call to action to grasp the emerging technology capabilities. We need a roadmap. Just as in sports, while the tools (or ‘performance enhancers’) can give an edge, it’s the strategy, direction and leadership that determine success. So here is Accural’s ten-point plan for AI Adoption in UK SMEs.

1. Assessment & Awareness
   • Conduct a comprehensive assessment of current business operations to identify areas ripe for AI integration.
   • Raise awareness about the potential of AI through workshops and seminars, addressing myths and misconceptions.
2. Strategic Roadmap Development
   • Craft a clear AI roadmap tailored to the unique needs and objectives of the business.
   • Ensure the roadmap is flexible to accommodate the rapid evolution of AI technologies.
   • Ensure AI tools across departments are interoperable and can “talk” to each other.
   • Encourage collaboration between departments to share insights and best practices related to AI.
3. Data Infrastructure & Management
   • Establish robust systems for data collection, storage, and processing.
   • Prioritise data quality, integrity, and security, understanding that data is the bedrock of AI.
4. AI Tool Selection and QA Validation
   • Research and select AI tools that align with business needs.
   • QA teams test these tools in controlled environments to validate their effectiveness and reliability.
5. Pilot Programmes & Iteration
   • Launch pilot AI programmes in selected areas to test and refine solutions.
   • Use feedback from these pilots to iterate and improve before broader rollouts.
6. Holistic Training & Development
   • Develop a comprehensive training programme that goes beyond traditional L&D, tailored to the specific AI tools being used.
   • Foster a culture of continuous learning and adaptability.
   • QA oversees integration, ensuring smooth data flow and decision-making across departments.
7. Ethics & Governance
   • Establish clear guidelines for the ethical use of AI, addressing concerns like data privacy and algorithmic fairness.
   • Set up an AI governance body or committee to oversee AI implementations and ensure alignment with ethical standards.
   • QA teams test and audit AI decisions for bias, ensuring regulatory and ethical compliance.
8. Feedback Mechanisms
   • Create channels for continuous feedback from both employees and customers.
   • Use this feedback to make real-time adjustments and refinements to AI tools and strategies.
   • Recognise and celebrate early successes and milestones in the AI journey to build momentum and foster a positive AI culture.
   • Continuously monitor the AI landscape, adapting the strategy as needed, and ensuring the business remains agile in its AI approach.
9. Future-Proofing & Scalability
   • Design AI systems to be scalable, ensuring they can handle growth and increased complexity.
   • Stay updated with emerging AI trends to continually refine and adapt the AI strategy.
10. ROI Measurement and QA-Backed Reporting:
    • Measure the expected and actual return on investment (ROI) of AI integrations.
    • QA aids in collecting accurate data, processing it, and generating reliable reports showcasing the value addition from AI.

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Unlocking Profit Potential: The Power of Salesforce Incentivisation in Complex Business Ecosystems

Introduction

As an experienced UK CFO, I’ve consistently observed that sales teams are the heartbeat of any organisation, acting as the direct link between a business and its customers. Navigating a complicated business landscape where complexity is the driving force requires a nuanced approach to salesforce incentivisation. This is a dynamic area where best practices and principles are of paramount importance.

The Complexity Factor

Before delving into incentivisation, it’s crucial to consider that complexity should steer our focus. Sales teams are grappling with fast-paced technological changes, shifting customer behaviours, and evolving business models. This complexity calls for a flexible and adaptable approach to incentivisation—one that captures a diverse set of key performance indicators (KPIs).

KPIs: More Than Just Numbers

Traditionally, businesses have prioritised market penetration and volume-based targets, often overlooking qualitative measures like customer satisfaction and long-term engagement. KPIs should be tailored to suit specific organisational needs. For some, market penetration—measured by the number of sign-ups—might be a key metric, whereas others may put more weight on customer lifetime value or brand advocacy.

The Incentive Framework

A multi-layered incentive scheme can address various aspects of sales performance. These layers could include:

• Individual performance bonuses
• Team-based incentives
• Long-term rewards tied to customer retention
• Bonuses linked to strategic organisational objectives

A hybrid approach often yields the best results, striking a balance between immediate gains and long-term growth, whilst considering both individual and collective contributions.

The Indirect Management Element

Sales teams commonly work in unsupervised settings, interfacing directly with clients. Indirect management methods, such as leaderboards, weekly check-ins, and performance analytics, can instil a self-regulating work ethic and maintain accountability within the team.

Fine-Tuning for Maximum Impact

Ongoing assessment is imperative. Sales data should be scrutinised both quantitatively and qualitatively to understand trends, spot bottlenecks, and adjust the incentive structure accordingly. Advanced data analytics tools can be invaluable here, enabling predictive modelling and enhanced strategic alignment.

Conclusion

How sales teams are remunerated significantly impacts an organisation’s financial health. Informed by the principle that complexity is the driving force, and supported by best practice principles, salesforce incentivisation can become a powerful tool for businesses looking to maximise profit and performance. As CFOs, we are in a unique position to develop and implement these robust incentive frameworks, ensuring that the salesforce is not just motivated but also strategically aligned with the complex and dynamic landscape they operate within.

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After setting the stage in our previous discussion on the significance of hidden data in business, let’s shift our focus to the actual techniques for uncovering these concealed gems. In a business ecosystem increasingly ruled by data, being oblivious to these hidden assets is a luxury organizations can ill-afford.

Data Audits: Your First Step

Initiating a comprehensive data audit can serve as a promising first step. This process involves scrutinizing your existing data repositories and identifying what data is actively used, what lies dormant, and what is completely forgotten. This is an effective way to highlight data that can be put to better use.

Breaking Down Silos

Isolated departments or ‘silos’ can stifle the flow of information within an organization. Implementing cross-departmental collaborations can facilitate the sharing of invaluable insights and skills, which would otherwise remain confined.

Employee Skill Inventory

An often overlooked reservoir of hidden data is the untapped talents within your workforce. Regularly conducting skills assessments can help identify new areas where your existing team can contribute, beyond their current roles.

Leveraging Machine Learning Algorithms

The power of machine learning algorithms in sifting through vast amounts of data to find patterns or insights cannot be understated. Advanced analytical tools can significantly reduce the time and resources required to find actionable information.

Continual Learning and Adaptation

To keep pace with the ever-changing business landscape, your strategies for uncovering hidden data should evolve as well. Adopting a culture of continual learning can help you stay ahead of the curve in identifying and leveraging new forms of hidden information.

Conclusion

The task of uncovering hidden business intelligence is neither straightforward nor a one-time effort. It demands a culture of persistent inquiry, regular audits, and an openness to adapt and learn. By following these techniques, you are well on your way to transforming hidden data into strategic assets.

The post Unknown Knowns: Unlocking Hidden Potential appeared first on Accural Limited.

Where is the Hidden Data?

Uncovering the hidden data in business is vital. Throughout my career, I have been excited by the hidden data and knowledge within businesses. Seeking them out to challenge and verify business ‘knowns’. The analysis often generates strategic and operational insights.

In a series of articles, I am going to present approaches to understanding and accessing the hidden depths of business that often lie undisturbed and underutilised. The knowledge can be in many forms including, ignored or inaccessible data, valuable insights trapped in siloed individuals and departments, or untapped skills and talent in the workforce.

Known, unknowns and the Johari Window

I’m a fan of matrix representations! Significant examples include the SWOT analysis and Ansoff’s Matrix. I am also a fan of the famous quote made by Donald Rumsfeld in 2002 who was the then US Secretary of State:

“…as we know, there are known knowns; there are things we know we know. We also know there are known unknowns; that is to say, we know there are some things we do not know. But there are also unknown unknowns—the ones we don’t know we don’t know” (Rumsfeld 2002).

(For some nerdy info read on, otherwise skip the next paragraph!)

The quote was formed using the principles developed in a tool by psychologists Luft and Ingham (1961) called The Johari Window. The model was designed to help individuals increase self-awareness. Fundamentally, the tool analyses things that are known (and not known) about oneself, against things that are known (and not known) by others about that same individual. Using a 4×4 matrix, it groups those things into four quadrants, as shown below:

 

Understanding and Accessing the Hidden Data

 

Reflecting on the quote from Donald Rumsfeld and combing with the Johari window, I realised that another opportunity or interpretation was to use the 4×4 matrix to explore the facets of awareness and knowledge. Taking the famous quote in turn:

1. “…known knowns” are the things we know we know.  We are aware that we have knowledge about these items. Examples in business could be:
   • existing customer base
   • key suppliers
   • internal metrics (staff numbers, EBITDA, projections)
2. “…known unknowns” are the things we know we don’t know. We are aware of the things we don’t know. Examples include:
   • future political and other environmental changes
   • changes to inflation and interest rates
   • competitors strategic development pathways
3. The final element of Donald Rumsfeld’s quote is “there are unknown unknowns”. The things we don’t know we don’t know. We are not aware of the things we don’t know. These events have been defined as black swan events. Examples of black swan events we can state (with hindsight):
   • the global financial crisis of 2007-2008
   • Brexit
   • the rise of the Internet

Putting these together and presenting on a 4 x 4 matrix, it omits a quadrant (see below). The missing quadrant is unknown knowns: what we don’t know we know.

 

This article is about uncovering the hidden data and conceptualising different types of knowledge within our SMEs that we want to unleash. The next article will examine how we tap into the unknown knowns.

The post Uncovering the hidden data: the unknown, knowns. appeared first on Accural Limited.

On the eve of my graduation, this post is about my reflections on completing a doctorate. Years of dedication, toil and support from friends, family and colleagues have delivered me to this point.
What set out as my personal Everest is complete. However, I am now reflecting on why I started, how the process changed me, what I learnt, and what would I change?
Evidently, this is not a post about my thesis or my findings. Contact me if you would like to talk about family business and succession.

The Beginning

As a Charted Management Accountant involved in business leadership for over twenty years, the reasons for beginning a doctorate were threefold: personal, academic and commercial. But personal was always the most important.

So, I was relatively late to the concept of combining raw intellectual capability with focused academic industry. After qualifying as an accountant, I decided to utilise my professional work ethic in educational development.

Previously, I completed two degrees and then moved onto an MBA and the prospect of completing a Doctorate loomed. My Master’s thesis was a model based on the ‘best way’ of completing a succession in a family business. However, I wanted to test modes of ‘best ways’ and see what that meant to family businesses.

I had witnessed succession planning issues and a lack of focus on profitability during my career, which had led to awful problems from those businesses. The businesses had been successful for many years providing economic, technical and commercial benefits to society and then entered a phase of uncertainty, conflict and decline.

Determined to learn more about the succession event, I set off to and provide assistance to family businesses through their process of CEO change.

Consequently, this would be my personal Everest, achieving the highest academic level. Unquestionably It would be difficult, complicated, but ultimately worthwhile.

Initial Stages

It was clear from the outset that deep thinking, organisation and reflection were going to be essential parts of the journey. The changes that I was anticipating were developments in knowledge and understanding, intellectual skills, subject-specific skills and some exciting new transferable skills.

I engaged my passion for learning and personal development!

As a naturally analytical and reflexive person, I knew from the start that the process of completing the thesis would involve a deconstruction and reconstruction of thoughts and knowledge within a backdrop of my career, personal life and as a student researcher.

This deconstruction and reconstruction also happened during the completion of my accounting exams. I have often spoken to students I have coached through the years about this process. During my reflection on the Doctorate process, I discovered interesting work in this phenomenon of change decades earlier by Van Ganeep (1960) and Turner (1969, p. 126) as liminal phases.

Van Ganeep (1960) described liminal phases as the in-between periods when a transition is taking place. Turner (1969, p. 126) explaining that the liminal period was one of uncertainty, which “…has few or none of the attributes of the past or coming state”.

In my case, the oscillations of change were between senior roles in business (Chair and CFO), being a new parent and as a relative novice in the field of doctoral research.

Being able to perform at a good level in all these areas, was at the heart of the doctorate challenge.

Working Methods

I prepared for the doctoral process, but the reality contained seismic shifts in perception, self-awareness and confidence. There were times throughout when the doctorate felt like a black hole from which no time, space or information escaped. Thankfully, there was assistance from research supervisors, my colleagues and family in diverse ways.

I kept detailed work schedules and personal journals as an aide-memoire and metronomic support. It became clear that despite the proximity of valued support, the completion of the doctoral process would ultimately be very personal and would require significant internal discipline.

It became essential to withdraw into periods of ‘bracketing’ whereby I could “deliberately and temporarily suspend…certain aspects of the self” (Hay & Samra-Fredericks, 2016, p. 2). I alternated between family, work-life and research.

I realise that I was incredibly fortunate, like Barack Obama, to have “…taken the chaos of [my] unfinished [thesis] and…do battle with it” (Obama, 2018, p. 171). The many locations of the private study included Preston, Wigan, Warwick, London, Derby, Leicester, Keele, Brussels, Chicago, Liverpool and Manchester but, unlike Barack, never Bali.

The Family Business Field

My interest in family business began as a practitioner providing advice and support on their strategic selection and financial and operational management.

Engagement with the academic field started by attending conferences and then playing a small role in establishing a forum in London where leading experts gave lectures.

The established and developing researchers in family business are incredibly passionate. Over the years, I witnessed the progression and insights within the field with great interest. For such a well-established organisational structure, family business research feels fresh and continuously improving in quality and impact. The leading authors and journals provided useful foundations for research along the way.

This freshness meant there were areas which were ripe for focus and development, particularly with a practitioner emphasis and opportunities to make things better for the family businesses that wanted to embark on a succession process. I passionately believe that the findings in my study will help family businesses.

What did I learn?

To draw upon the aspects of liminality once more, research development, like life, oscillates between the feeling of staying the same and forever changing. Ultimately the learning has been professional, personal and practical.

Practically, I have developed significant continued expertise with the tools shown in a separate blog post.

There are, of course, many free and paid alternatives for the systems above, but I found these to be perfect for my working methods.

From a personal perspective, I have always been a confident person in terms of professional delivery. Initial concerns were whether these aptitudes would translate to an academic setting and whether new skills would develop appropriately.

The doctoral process has changed how I approach learning and development. I developed a holistic tool for continuous professional development that I use professionally and personally.

I also developed an evidence-based system of problem resolution in all areas of life. This was a key personal aim, to be able to utilise ideas, theories and concepts created within academia to professional and personal beneficial effect.

Resilience has also developed, along with an increased capacity for adaptability and change. Although, that may be due to the ageing process also!

What would I do differently?

My reflections on completing a doctorate would be incomplete if I didn’t consider this question. I learnt a great deal over the years that have passed while completing the study. In this case, it is difficult to say what I would do differently because the end product was incredibly satisfactory.

One area to consider would be an awareness of how long things can take in academic writing and research. In a professional context, I gauge the level of complexity of a task and manage hard and unbreakable deadlines. Academically, the pace of progress was slower.

However, those more extended periods of academic reflection provided time for new helpful research to emerge. Ultimately there was no guarantee of satisfaction. Changing things would seem a risk not worth taking.

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