Fraxses is powering data-driven audit transformation at a state owned utility.

For more then 10 years Fraxses has proved its versatility across industries, solving data challenges regardless of scale, complexity or how messy and scattered the underlying information may be. The platform has been tested across a wide range of use cases and has consistently delivered the solutions customers need.

Alongside its versatility, Fraxses has shown the potential to transform specific domains, one of the most promising of these being the world of audit. Since 2022 we argued Fraxses could be a game-changer for the audit profession, publishing a white paper The Transformation of Internal Audit and an accompanying article to outline that vision.

In the intervening three years, the Fraxses platform has advanced considerably. The recent addition of the Data Intelligence module, with its ontology-driven common data models and embedded rules, has made Fraxses an even more powerful audit tool. And importantly, we have a series of compelling real-world use cases that demonstrate our long-held contention that Fraxses presents the audit profession with a solution like no other.

Fraxses is being used to great effect in several major engagements across the world, including key public sector audits. In this articel, the first in a two-part series, we turn the spotlight on one of the most significant of these engagements, now in its fourth year: the audit of a national utility.

Partnering for a New Approach

In 2022, a global professional services firm, having recognised the platform’s potential in the audit space, partnered with Intenda to integrate Fraxses into its audit strategy for the utility. The project centres on recalculating usage charges and addresses the long-standing challenge of validating billing accuracy by reconciling financial data from SAP with readings from multiple operational systems.

The scale of this audit is extraordinary: more than eight billion records ingested, recalculated and validated by a core team of just three people. Traditional methods could not handle this volume of data, nor could they provide the confidence or meet the required timeframes for such a critical audit. With the implementation of Fraxses, a data challenge that previously seemed insurmountable was swiftly overcome.

By virtualising access to the utility’s financial and operational datasets, Fraxses enabled Audit teams to run full-population recalculations in place without copying or moving data. Automated substantive tests and reusable control frameworks reduced duplicated effort, while cross-functional access ensured that audit requirements were met from a single source of truth.

Efficiency, Cost and Scale

In year one the recalculation ran 40% over schedule due to sheer data complexity, however, after the first year of the audit Fraxses quickly began to deliver compounding benefits. In the second year the full audit was completed 15-20% ahead of projections, and in year three quarterly runs were completed in just two to four weeks.

These improvements translated into full-population coverage that gave auditors and stakeholders high levels of assurance, along with significant cost savings through reuse of prior-year work and faster onboarding for new team members and clients. Cross-source reconciliation delivered consistent, reliable results, while the professional services firm’s Audit team gained quicker access to the data than through the utility itself.

As demonstrated by these remarkable gains, Fraxses delivers improved efficiency and raises the bar for audit, enabling large-scale, data-driven engagements that combine speed, assurance and accountability with reduced cost and effort.

Laying the Foundation for the Future

With this engagement now in its fourth cycle, Fraxses has demonstrated its capacity to transform not only public sector audits but the audit profession at large. Planned next steps include expanding the use of Fraxses within the professional services firm, standardising it as the preferred platform for complex large-enterprise audits, building AI models on top of the Data Intelligence module to automate compliance and regulatory reporting, and developing audit solution templates with reusable control libraries and automation workflows.

Together, these advances highlight how Fraxses equips the audit profession with the technology and tools to move beyond sampling and manual reconciliation, towards a digital future where accuracy, assurance and scale come as standard. 

Kaleidocode's CEO, Rory Clarke, will be joining a panel discussion at Emeris’ (Varsity College) 2025 Client Connect November event titled “Supporting Students and Graduates in the AI Era: Staying Relevant in a Rapidly Evolving Workplace” — exploring how education, industry, and employers can better prepare young professionals for a world being reshaped by AI.

At Kaleidocode, we see firsthand how practical, experience-based learning helps bridge the gap between academic study and the demands of modern software development. Looking forward to sharing insights and learning from others doing incredible work in this space.

📅 November 2025

#AI #FutureOfWork #SkillsDevelopment #Apprenticeships #DigitalTransformation

Artificial Intelligence (AI) is no longer a fringe technology, it’s quickly moving into the heart of enterprise systems. But for many corporate leaders, the question isn’t simply “Should we adopt AI?” but rather “How can we adopt AI in a way that delivers real value?”

A recent MIT-led study of enterprise generative-AI pilots reported that only approximately 5% of initiatives delivered measurable business or profit-and-loss impact. In other words: roughly 95% of pilots failed to move beyond experimentation into genuine business benefit. 

This stark finding underscores that AI isn’t a plug-and-play silver bullet. It demands discipline, effective integration, clear focus and business alignment. The good news is that those companies that treat AI as a systems integration and change-management exercise rather than simply a model build project can see meaningful outcomes.

Lets explore some realistic strategic benefits of corporate AI systems and then set out five critical keys for leaders to keep in mind when evaluating AI implementation projects.

The Strategic Value of Intelligent Systems

Despite the cautionary findings, AI and intelligent systems still hold significant strategic potential when approached correctly.

Enhanced Decision-Making. AI systems now analyse large, diverse data sets and surface actionable insight faster than conventional BI tools. Whether forecasting demand, identifying customer churn or optimising resource allocation, the advantage lies in accelerating decision-making and not just automating it.

Intelligent Automation. Beyond rule-based automation, AI enables workflows that adapt, learn and respond intelligently, e.g. natural language processing, document understanding, image recognition and human-in-the-loop decision support. This enables operational scale-up without proportionate cost increases.

Predictive & Analytical Systems. Predictive models shift the enterprise from reactive to proactive: anticipating anomalies, optimising supply-chains, identifying risks before they materialise. In industries such as finance, logistics and manufacturing, this repositions systems from “data record repositories” to “insight engines”.

Scalable, Learn-able Architecture. The most compelling intelligent systems do more than “run once” repeatedly, rather they evolve. Incorporating feedback loops, continuously refined models and architecture, a business can embed intelligence as part of the business. This is where many AI pilots fall short (as the MIT study shows), systems stall when they fail to adapt or embed iteratively into business workflows.  

Five Critical Considerations for Corporate IT Executives

So, if there is so much value on offer, what should leaders be aware of when evaluating AI-enabled systems. As a primer, here are five considerations to guide strategy and decision-making.

1. Integration with Existing Systems
One of the major lessons from the MIT research is that failure is rarely due to the AI model itself, more often it’s due to poor integration into enterprise workflows and legacy systems. 
What should I be considering:

• Conduct an architectural assessment to understand how new AI components will interact with existing ERP, CRM or data-warehouse systems.
• Define clear APIs, data pipelines and interfaces to these systems rather than creating standalone bolt-on pilots disconnected from core systems.
• Treat AI as an extension of your current platform, not a separate experiment, service or dataset entirely.

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2. Data Readiness and Governance
AI’s value is only as strong as the data it learns from. Many companies underestimate the work required to bring structured, clean, contextualised data into play.
What should I be considering:

• Build data-governance frameworks (data lineage, quality, anonymisation, consent) early.
• Catalogue and inventory data sources, identify siloes, establish integration points.
• Recognise that data preparation often takes longer and costs more than the model build itself.

3. Security, Compliance and Ethical Use
As pilots move into production new risks emerge, e.g. model bias, “black box” decision making, regulatory non-compliance, sensitive data leakage. The MIT study found that many generative-AI efforts can stumble on governance rather than technology. 
What should I be considering:

• Make sure your AI systems can explain how they make decisions, keep clear records of what they do, and include people in the process to review or approve important outcomes.
• Align data and AI use with privacy laws and sector regulation (POPIA, GDPR, etc.).
• Involve risk, legal and compliance teams from the start.

4. Skills, Change Management and Organisational Culture
AI initiatives fail or under achieve not because of technology alone but because organisations aren’t ready for the shift. The MIT research calls out a “learning gap” where the enterprise struggles to adapt existing tools and workflows to AI-driven ways of working. 
What should I be considering:

• Create cross-functional teams combining business domain experts, data scientists, engineers and business owners.
• Invest in internal literacy, invest in educating business users on AI capabilities and limitations.
• Define roles and governance that embeds AI into business processes, and not silo it in a lab or POC process.

5. Measuring Business Value and Defining Success
Too many AI pilots never translate into measurable commercial impact. The reportedly high failure rate is in large part due to inadequate measurement frameworks or unclear business objectives. 
What should I be considering:

• Define KPIs or goals aligned with measurable business outcomes e.g. cost reduction, time-to-decision, error rate, revenue uplift.
• Set realistic timelines and scope: many successful AI programmes focus on one specific use-case, execute well and then scale gradually. 
• Use pilot results to decide whether to scale, pivot or stop. Don’t into the “pilot-forever” trap that dies off slowly.

Balancing Innovation with Governance
The MIT study serves as useful balance in the current environment; AI enthusiasm alone does not guarantee results. Leaders who treat AI as a strategic system change rather than a one-off project are far more likely to see sustained value. Successful approaches embed AI, data, people and processes in an integrated way, aligning with business goals, measuring outcomes, and evolving governance.

Choosing the right use-case/s connected to real commercial value and building organisation readiness are what distinguish the AI initiatives that succeed from the remainder.

The next phase of intelligent systems will be defined by those companies that see AI not as a novelty, but as a core part of their digital fabric. That’s where the real value lies.

Over the past decade, South Africa has quietly become one of the world’s most attractive destinations for software offshoring. Long known for its financial services and BPO sectors, the country’s technology industry has matured into a global player, offering high technical capability and professional business alignment.
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For clients in the UK, Europe, and the US, the advantages are compelling: skilled engineers, cultural familiarity, English fluency, and a working day that aligns neatly with European time zones. But beyond those obvious benefits, offshoring to South Africa represents a broader shift - one towards shared partnership, increased output and impact, and sustainable long term delivery models.

Why South Africa Stands Out
While traditional offshoring destinations have focused primarily on cost, South Africa offers a value-to-quality ratio that’s more balanced. The engineering community in South Africa is deeply rooted in enterprise software development, with strong exposure to global frameworks, agile practices, and cloud-native technologies.

Local companies have invested heavily in skills pipelines, often developing and mentoring their own junior engineers through academies and apprenticeship programs. This focus on continuous learning not only strengthens local talent, but also gives international clients access to teams that are growing, adaptive, and future-ready.

The result is a delivery ecosystem that combines technical sophistication with long-term stability - a key differentiator in a market often defined by rapid turnover.

Five Things to Look Out for When Selecting an Offshore Software Partner

When companies consider offshoring software development, typical concerns fall into the following areas: control, communication, quality, and security. Many fear losing oversight of delivery and progress, others worry about miscommunication or cultural disconnects. Ensuring consistent technical standards and maintaining code quality can also be challenging without shared tools and clear governance. Finally, data protection and compliance remain priorities, as clients need confidence that offshore partners meet global security and privacy standards. In light of this, here are 5 things to look for in a partner:

1. Cultural and Communication Fit - Effective collaboration depends on shared understanding, not just shared code. Look for teams that demonstrate strong communication skills, familiarity with Western business culture, and a working rhythm that complements your own time zone.

2. Proven Delivery Maturity - Ask how potential partners manage complexity — from DevOps pipelines to agile governance. Mature teams should demonstrate structured delivery methods, continuous integration practices, and the ability to scale without losing control.

3. Balance of Cost and Capability - Low hourly rates can be misleading. True value lies in productivity, quality, and the ability to deliver first-time-right solutions. Assess total cost of ownership over the lifecycle of your project, not just the initial quote.

4. Talent Development and Continuity - In offshoring, consistency matters as much as skill. Partners that invest in developing their people — through coaching, mentorship, and career progression — will provide more stable, committed teams over time.

5. Use of Modern Tooling and AI - Leading offshore partners increasingly use AI-assisted development, automated testing, and predictive DevOps to improve efficiency and reliability. Ask how your prospective partner integrates these tools into their workflow — and whether they can demonstrate measurable results.

A Balanced Approach to Global Delivery
Offshoring to South Africa offers something unique: proximity in mindset, excellence in engineering, and sustainability in talent growth. For companies seeking both technical depth and human connection in their software partnerships, it’s an option worth serious consideration. As the market evolves, success will belong to those who build not just teams, but ecosystems of learning, innovation, and trust — and South Africa is rapidly becoming one of the best places to do exactly that.

Across the world, companies are facing a shared problem, technology is moving faster than traditional education can keep up. Universities still play a critical role in shaping foundational knowledge — but employers increasingly find that graduates lack the hands-on skills and professional readiness needed to contribute meaningfully from day one. 

This gap between theory and practice is one of the biggest barriers to growth for technology-driven businesses. And it’s exactly why apprenticeships, structured, mentored, workplace learning programs, matter more than ever in 2025.

The Skills Gap Has Become a Structural Issue
In the software and digital industries, the combination of AI, cloud, automation, and cybersecurity has transformed the required skill set. But with every new technology wave, the lag between industry adoption and educational adaptation grows wider.
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Many companies now describe two simultaneous challenges:

• Short-term delivery pressure: Too few mid-level developers and testers to meet demand.
• Long-term sustainability: A lack of structured pathways for developing new talent with modern skills

An apprenticeship model directly addresses both, by growing capability internally, rather than perpetually buying it in the market.

What an Effective Apprenticeship Looks Like
The most successful apprenticeships combine formal learning with real project delivery and active mentorship. They don’t treat junior staff as “extra hands,” but as emerging professionals learning through guided participation.

In practice, strong programs include; a structured training curriculum aligned with current technologies, exposure to live systems and delivery teams, not just simulated projects, coaching on communication, teamwork and accountability, and clear performance feedback and progression milestones.

When well-designed, apprenticeships produce individuals who can deliver value within months — and grow into senior contributors measurably faster than traditional graduates.

Lessons from the Field
A good example comes from the South African tech sector, where several companies have built graduate and apprenticeship programs to address both local skills shortages and global offshoring opportunities.

One such program, run by Kaleidocode Pivot, integrates new graduates directly into enterprise software delivery teams through a structured coaching model. Apprentices start with foundational bootcamps in coding, testing, and DevOps, before moving into client-facing projects under senior mentorship.

In partnerships with firms like LexisNexis and Impro Technologies, apprentices have been embedded in testing automation and development teams — contributing to quality assurance, test frameworks, and and development while learning modern delivery practices. The result is a genuine talent pipeline: motivated, coached professionals who can move from apprentice to junior developer to full team member over a single year.

It’s a model that others are beginning to replicate, with measurable outcomes in productivity, retention, and cost efficiency.

The Broader Benefits
Beyond immediate skills development, apprenticeships bring systemic value. For businesses, they create a stable, loyal, and cost-effective talent pipeline, whilst for industries they align training with real market needs, raising the overall professional standard. And finally for the individuals themselves, they offer an accelerated route into employment and meaningful, high-value work. 
And in countries like South Africa, apprenticeships carry social impact — addressing youth unemployment and supporting transformation through sustainable employment and skills transfer.

Apprenticeships in the Age of AI
But what about the impact of AI on our workforces. Will we need to invest in young professionals if agentic AI can be applied to entry level work. This is a meaningful question that requires a nuanced response.
As generative AI reshapes how we build and test software, there is a growing need for professional and human guidance of the code that is being generated. Future engineers won’t need to code purely at a foundational level— they’ll need to understand how to collaborate with intelligent systems, apply judgment, and design responsibly.
Apprenticeships are uniquely positioned to cultivate those traits, because they’re rooted in mentorship, reflection, and context — the very qualities AI finds difficult to replace meaningfully.

Building the Future Workforce
Whether you’re a CIO trying to scale your teams, or a corporate Learning and Development specialists designing learning programs, the message is clear, apprenticeships are no longer an optional social initiative, rather they have become  a strategic imperative.
Apprenticeships create the bridge between our education systems and our industry development teams. If executed well, they don’t just fill roles, rather they build capacity and prepare our people for a future where learning and adaptability does not stop.