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How Technical Writing Skills Double Your Developer Productivity

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Did you know that investing in IT writing skills can double your effectiveness as a developer? While many programmers focus exclusively on improving their coding abilities, they often overlook the powerful productivity boost that comes from strong technical writing practices.

Technical documentation isn’t just busywork that slows you down. Rather, it’s a productivity multiplier that clarifies your thinking, prevents rework, enhances collaboration, and expands your professional influence. According to a study by GitHub, teams with comprehensive documentation resolve bugs 28% faster and experience 23% fewer deployment issues. Additionally, developers who regularly document their work report spending 35% less time answering repetitive questions.

Throughout this article, you’ll discover exactly how writing skills transform good developers into exceptional ones. From creating internal documentation that prevents the same problems from recurring to writing design docs that align stakeholders before coding begins, these skills directly impact your daily effectiveness and long-term career trajectory.

Writing to Clarify Your Own Thinking

The ability to write clearly often mirrors the clarity of your thinking. Many developers discover that the process of documenting code or explaining technical concepts through writing forces them to organize their thoughts more effectively. Let’s explore how IT writing serves as both a thinking tool and a productivity amplifier.

Breaking down complex ideas into simple steps

Complex programming problems can feel overwhelming when viewed as a whole. Skilled developers approach these challenges methodically by breaking them down into manageable pieces. Writing provides the perfect medium for this decomposition process.

One effective technique is to begin with plain English descriptions. Before writing a single line of code, document what you’re trying to accomplish in simple language. This approach helps you:

  • Define the problem clearly without getting lost in implementation details
  • Identify logical steps needed to reach the solution
  • Create a roadmap for implementation that keeps you focused
  • Spot potential issues before writing code

For example, instead of diving straight into coding a complex algorithm, first write comments describing what each part should do. This serves as pseudocode that clarifies your approach. As one developer notes, “When I come across functions which I expect to be difficult to write, I will describe what the function does using either plain language or pseudocode” [1].

Furthermore, this preparation saves significant time during implementation. Test-driven development practitioners often write out pseudocode steps before writing tests, “allowing you to design the unit at a higher level of abstraction before writing the implementation” [1].

How writing reveals gaps in understanding

Perhaps the most valuable aspect of technical writing is how it exposes what you don’t know. When you attempt to explain something clearly, knowledge gaps become immediately apparent.

Consider this: almost all developers (63%) spend between 30-120+ minutes daily searching for answers about their codebase [2]. Much of this time could be saved through better documentation that addresses these knowledge gaps proactively.

The documentation process naturally highlights these knowledge holes. As noted in technical writing resources, developing an outline “will not only acquaint you with the source material, but it will also help spotlight holes you hadn’t noticed” [3].

Many developers use simple yet effective methods to track these gaps:

“I write down what I have understood about the concept in my own words. Whenever I find there is a gap in my notes now I can go back to the site or book am learning from and try to figure out what I missed out” [4].

Others use more structured approaches like skills gap analysis, which helps “identify skill shortages” and “plan targeted training” [5]. This systematic identification of knowledge deficiencies through writing allows for focused improvement.

Consequently, the act of documenting your code or explaining concepts forces you to confront what you don’t fully understand. When you try to write a clear explanation but struggle, you’ve identified precisely where your knowledge needs strengthening.

In essence, writing becomes both a diagnostic tool and a learning method. The clearer your writing becomes, the clearer your thinking—and ultimately, your code.

Using Documentation to Reduce Rework

Poor documentation costs developer teams tremendously. According to surveys, inefficient documentation hinders 41% of developers [6], with 64% spending over 4 hours weekly searching for project information [7]. This wasted time translates to approximately $13,500 lost per employee annually [7]. Let’s examine how strategic documentation eliminates this costly rework.

Investing in modern software documentation solutions allows teams to maintain accurate, reusable knowledge while reducing redundant work. These solutions provide centralized repositories, templates, and version control, ensuring that developers can quickly find relevant information and avoid repeated mistakes. By integrating such solutions into daily workflows, teams prevent knowledge loss and significantly improve productivity.

Creating reusable knowledge with internal docs

Internal documentation transforms individual knowledge into organizational assets that prevent teams from “reinventing the wheel” [8]. Despite its importance, only 4% of companies consistently document their processes [9]. This gap represents a significant opportunity for productivity gains.

Strong internal documentation delivers concrete benefits:

  • Boosts engineer productivity by providing answers without hunting down colleagues
  • Prevents teams from solving the same problems repeatedly
  • Enables cross-team knowledge sharing about successes and failures
  • Creates inclusive environments where everyone has equal access to information [8]

Internal documentation should be treated as a living system, not static files. The most effective approach maintains documentation as “code” with version control, automated testing, and continuous updates [7]. This ensures content remains accurate as systems evolve.

Reducing support questions with clear READMEs

READMEs serve as the face of your project, introducing it to new users and contributors. A comprehensive README eliminates repetitive questions by addressing common inquiries upfront, essentially functioning as a self-service support center [10].

An effective README should include:

  1. Clear project description explaining what the software does
  2. Installation and setup instructions with required dependencies
  3. Usage examples showing common operations
  4. Troubleshooting guidance for typical issues
  5. Contribution guidelines for potential collaborators [10]

The payoff is substantial—projects with comprehensive documentation receive 47% more contributions [7] and reduce support tickets from internal teams by 41% [7]. Furthermore, teams report a 35% reduction in development costs through better knowledge sharing [7].

Avoiding repeated explanations in team chats

Nothing drains productivity like answering the same questions repeatedly in chat channels. Research shows software teams spend 15-20 hours weekly resolving documentation-related issues [7]—time that could be spent building features.

Documentation transforms these repetitive exchanges into reusable knowledge. Rather than explaining the same concept multiple times, point colleagues to documented resources. This approach particularly benefits remote teams working across time zones, where documentation enables asynchronous communication without waiting for colleagues to come online [8].

For maximum effectiveness, documentation should be:

  • Accessible: Centralized, searchable, and easy to find
  • Current: Regularly updated for accuracy
  • Comprehensive: Covering necessary details without overwhelming
  • Contextual: Providing rationale, not just instructions [11]

Notably, organizations implementing these practices see significant improvements: 60% faster onboarding for new team members and a 28% improvement in sprint velocity [7]. Beyond productivity gains, good documentation breaks down barriers between teams, encouraging collaboration across projects [8].

Writing as a Tool for Better Collaboration

Effective collaboration forms the backbone of successful software development, with writing serving as its primary enabler. Beyond personal productivity gains, IT writing skills dramatically enhance how teams work together—especially when they’re distributed across locations or time zones.

Improving async communication in remote teams

Remote work depends heavily on asynchronous communication—the exchange of information without requiring immediate responses. According to research, remote workers spend approximately 3 hours and 43 minutes daily communicating through various channels [12]. Asynchronous writing becomes critical as teams spread across different time zones with limited overlapping hours.

Strong written communication offers several advantages for distributed teams:

  1. Creates documentation automatically – When teams communicate primarily in writing, they automatically create a record of decisions and discussions that can be referenced later [13].
  2. Enables thoughtful responses – Async communication gives team members time to research and provide well-constructed, informed responses rather than immediate reactions [14].
  3. Levels the playing field – Written brainstorming allows traditionally quieter voices to contribute without interruption, diversifying the pool of ideas [12].

For maximum effectiveness, establish clear response time expectations (such as responses by the end of the next business day) and maintain a centralized knowledge hub where team members can find information independently [14].

Writing design docs to align stakeholders

Software design documents function as blueprints for projects, outlining what you’re building, how it works, and what it looks like. These documents significantly improve team alignment and prevent costly misunderstandings.

A well-crafted design document transforms abstract ideas into concrete plans by bridging the gap between what software should do and how it will be built [15]. Moreover, design docs facilitate communication among stakeholders who may have different perspectives and priorities.

Effective design documents should include:

  • Clear problem description before jumping to solutions [1]
  • Visual aids like diagrams and flowcharts to illustrate complex concepts [16]
  • Highlighted questions and key decision points [1]
  • Standardized format for consistency across projects [16]

The process of creating these documents often reveals misalignments in understanding before coding begins. As one expert notes, “By getting the problem written down in concrete-as-possible terms, and soliciting feedback, you can reveal and address differences before they become an issue” [1].

Using RFCs to document decisions

Request for Comments (RFCs) provide a structured approach to making and documenting technical decisions. Originally developed for internet standards, RFCs have become valuable tools for software teams making significant technical choices.

RFCs are relatively informal documents created before coding begins, documenting high-level implementation strategy and critical design decisions while emphasizing trade-offs considered at the time [17]. They serve multiple purposes within teams:

  • Allow individual contributors to participate in decisions for systems they’re responsible for
  • Enable domain experts to contribute even when not directly involved
  • Improve risk management for technical decisions
  • Create snapshots of context for future reference [17]

The commenting period for RFCs should have clear time limits so proposals don’t linger indefinitely waiting for feedback [18]. After an RFC is approved, it becomes part of the team’s decision record, providing valuable context for future team members who weren’t present when decisions were made [17].

Organizations implementing RFC processes typically create repositories to store these documents, with any team member able to write an RFC and open it for discussion [17]. This approach has proven especially valuable for cross-functional collaboration, as technical proposals usually have product and business implications [18].

Teaching Through Writing to Scale Your Impact

Your IT writing skills can extend well beyond personal productivity—they enable you to scale your technical knowledge across teams and organizations. Learning to teach through writing multiplies your impact without requiring your constant presence.

Creating onboarding guides for new developers

Well-crafted onboarding documentation dramatically shortens the time new team members need to become productive. According to research, 20% of employee turnover happens within the first 45 days [5], making first impressions through organized documentation crucial. Quality onboarding documentation directly affects both productivity and happiness of new employees.

Effective developer onboarding guides should include:

  • A concise two-page architecture overview
  • Visual system diagrams
  • Team-specific glossaries
  • Tech stack maps
  • Communication protocols [19]

First thing to remember, an onboarding guide serves as an index to project-specific content. Include details about engagement scope, team processes, codebase structure, coding standards, and team agreements [5]. The documentation should be comprehensive yet not overwhelming—focus on providing essential information that prevents overwhelming new hires.

Writing tutorials to share internal tools

Technical tutorials represent one of the most valuable forms of IT writing. Beyond serving as search engine content, tutorials showcase specific use cases, overcome technical objections, supplement documentation, minimize support questions, and improve developer activation rates [2].

Strong tutorials don’t just show readers how to accomplish something—they explain the why behind each step [2]. This context enables other developers to apply lessons creatively to their own work. When creating tutorials for internal tools, include both code samples and screenshots showing results, along with links to completed projects in repositories.

Mentoring through written feedback

Written feedback provides a powerful mentorship channel that scales your expertise. In contrast to GitHub-style line-by-line reviews, comprehensive written feedback creates opportunities for deeper learning. The feedback process should be interactive rather than unidirectional, with mentors avoiding focusing exclusively on weaknesses [20].

Effective written feedback should:

  • Address both strengths and weaknesses
  • Occur regularly with established frequency
  • Consider both academic and psychosocial aspects
  • Include clear examples when suggesting improvements [20]

Undeniably, written feedback creates advantages that in-person mentoring cannot match. It provides tracking capabilities where junior developers can reference previous comments for further research or discussion [21]. This knowledge transfer happens without interrupting regular work processes—making mentorship part of daily activities rather than a separate obligation.

Writing to Build Visibility and Career Growth

Beyond internal team benefits, strong IT writing skills act as powerful career accelerators, creating visibility that opens professional doors.

Publishing blog posts to showcase expertise

Technical blogging offers remarkable career advantages. Numerous developers attribute significant career breakthroughs directly to their writing. As one developer noted, “I owe my entire career to a couple of articles I wrote” [4]. Another secured a full-time offer at a cloud computing startup based solely on the strength of a few blog posts [4]. In fact, many consultants report gaining new clients specifically through articles they’ve published [4].

Blog posts function as tangible demonstrations of your thinking process, essentially providing “social proof” of your ability to learn publicly [4]. Often, these writing efforts evolve into larger opportunities—some developers have transformed collections of blog posts into published books or speaking engagements [4].

Using writing in performance reviews and promotions

Performance evaluations represent critical career moments where writing skills directly impact advancement. Generally, quality self-assessments require specific context and examples that showcase your unique contributions. For optimal results, use writing to synthesize data from multiple sources, identifying trends and summarizing main themes [22].

Always align your written assessments with your organization’s leadership principles and company values [22]. Beyond self-assessments, documenting your work throughout the year creates a comprehensive record of achievements to reference during promotion discussions.

How writing opens doors to speaking and leadership

IT writing frequently serves as a gateway to broader professional opportunities. Many developers report that articles led directly to conference speaking invitations [4]. These speaking engagements subsequently create paths toward leadership roles.

Beyond that, published writing establishes you as a thought leader, differentiating you from peers. One expert explained, “Instead of competing with all other engineers, you become The Choice in your area” [4]. This recognition extends beyond immediate job opportunities—professionals who establish themselves through writing often receive offers for contract work, book deals, and leadership positions [4].

Ultimately, much of professional success depends on persuading others to recognize your value [23]. Through consistent, quality writing, you build this recognition systematically, creating career momentum that extends far beyond your current role.

Conclusion

Technical writing skills truly serve as a force multiplier throughout your development career. As we’ve seen, documentation doesn’t merely supplement your code—it fundamentally transforms how you think, work, and collaborate. The evidence speaks volumes: teams with comprehensive documentation resolve bugs 28% faster while spending significantly less time answering repetitive questions.

Additionally, writing clarifies your thinking by forcing you to break complex ideas into manageable steps. This process naturally reveals knowledge gaps you might otherwise miss, allowing targeted improvement of your technical understanding. Therefore, each document you create strengthens both your current project and your future capabilities.

Furthermore, strategic documentation dramatically reduces costly rework. Rather than repeatedly solving identical problems, your team builds a reusable knowledge base that prevents wasted effort. This approach particularly benefits remote teams working across time zones, where asynchronous communication depends heavily on clear, accessible documentation.

Beyond individual productivity, writing skills substantially enhance collaboration through design documents and RFCs. These tools align stakeholders before coding begins, preventing expensive misunderstandings and creating valuable context for future team members. Consequently, decisions made today remain understandable years later, even after team composition changes.

Perhaps most importantly, teaching through writing allows you to scale your impact exponentially. Your onboarding guides, tutorials, and written feedback mentor colleagues without requiring your constant presence. This multiplication of knowledge transforms you from an individual contributor into a force that elevates entire teams.

Ultimately, strong technical writing opens doors throughout your career. From blog posts that showcase your expertise to documentation that supports promotion discussions, writing creates visibility that extends far beyond your current role. Many developers attribute significant career breakthroughs directly to their published work.

The investment in developing your technical writing skills pays dividends across every aspect of your development career—doubling your productivity while simultaneously creating opportunities for advancement. After all, the most successful developers aren’t just those who write excellent code, but those who effectively communicate their technical knowledge to others.

References

[1] – https://blog.luketurner.org/posts/writing-effective-design-documents/
[2] – https://www.productmarketingalliance.com/developer-marketing/writing-great-technical-tutorials-for-developers/
[3] – https://mailchimp.com/resources/technical-writing/
[4] – https://stackoverflow.blog/2021/08/09/how-writing-can-advance-your-career-as-a-developer/
[5] – https://microsoft.github.io/code-with-engineering-playbook/developer-experience/onboarding-guide-template/
[6] – https://www.axelerant.com/blog/effective-documentation-and-developer-experience
[7] – https://fullscale.io/blog/software-documentation-best-practices-outsourced-development/
[8] – https://shopify.engineering/good-documentation-productivity
[9] – https://www.atlassian.com/work-management/knowledge-sharing/documentation
[10] – https://www.makeareadme.com/
[11] – https://www.networkperspective.io/devex-book/documentation-avoiding-work-delays
[12] – https://www.atlassian.com/blog/communication/asynchronous-communication-for-distributed-teams
[13] – https://handbook.gitlab.com/handbook/company/culture/all-remote/asynchronous/
[14] – https://www.digitalocean.com/resources/articles/asynchronous-communication
[15] – https://www.atlassian.com/work-management/knowledge-sharing/documentation/software-design-document
[16] – https://www.practicallogix.com/how-to-ensure-your-srs-document-aligns-with-stakeholder-expectations/
[17] – https://dev.to/eminetto/making-technical-decisions-using-rfcs-1j4f
[18] – https://medium.com/juans-and-zeroes/a-thorough-team-guide-to-rfcs-8aa14f8e757c
[19] – https://fullscale.io/blog/developer-onboarding-best-practices/
[20] – https://pmc.ncbi.nlm.nih.gov/articles/PMC9387358/
[21] – https://smartbear.com/blog/developing-a-culture-of-mentorship-with-code-revie/
[22] – https://textio.com/blog/dos-and-donts-of-writing-performance-reviews-with-generative-ai
[23] -https://hbr.org/2023/05/a-new-approach-to-building-your-personal-brand

Accenture to Deploy ChatGPT Enterprise to Staff in Expanded Partnership With OpenAI

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Accenture is equipping tens of thousands of its IT professionals with ChatGPT Enterprise under a sweeping new partnership with OpenAI, a move that immediately lifted Accenture’s shares by more than 2.8% in premarket trading and signaled a deeper shift in how major consulting firms are restructuring their service models around AI.

Both companies said on Monday that they will also launch a new AI program that helps enterprises across sectors such as financial services, healthcare, and retail adopt AI-powered workflows. The goal is to embed generative AI throughout business operations at a moment when corporate clients are demanding faster, more automated systems.

Accenture’s decision is unfolding against a backdrop of uncertain government spending and an uneven global economy. The firm is simultaneously navigating an $865 million restructuring announced in September, designed to realign its workforce, lower costs, and lift efficiency.

Inside the industry, the rise of advanced AI tools has stirred fears of job displacement because these systems can process and generate information far faster than traditional consulting workflows. Still, Accenture is placing itself in a position to win enterprise business by integrating AI more aggressively into its operations.

As part of the agreement, Accenture’s teams will use ChatGPT Enterprise for both internal and external work, with the partnership expected to accelerate AI upskilling across roles — not only engineers but also analysts, consultants, and client-facing teams. The expanded use of ChatGPT Enterprise is part of a broader trend of companies seeking to automate tasks, reduce project timelines, and boost productivity.

OpenAI’s growing investment tentacles

The announcement also comes at a moment when OpenAI is rapidly widening its investment tent. While the company is still battling the difficulty of turning ChatGPT into a consistently profitable product, it has been diversifying — taking ownership stakes in companies positioned to benefit from AI.

One of the clearest examples is its recent deal with Thrive Holdings, the portfolio company launched by major OpenAI backer Thrive Capital. OpenAI will embed engineering, research, and product teams inside Thrive Holdings’ businesses to accelerate AI adoption and lower costs. Thrive Holdings, which buys, owns, and operates companies in real-economy sectors such as accounting and IT services, said the goal is to pair its operational footprint with OpenAI’s frontier models.

Joshua Kushner, CEO and founder of Thrive Capital and Thrive Holdings, described the partnership as an effort to bring “tremendous potential” to sectors overdue for modernization.

OpenAI has also taken positions in infrastructure partners essential to its long-term growth, such as Advanced Micro Devices (AMD) and CoreWeave — both of which provide critical compute capacity for large-scale model training. These stakes are structured to grow if the partner companies’ performance improves, effectively giving OpenAI a financial mechanism to offset the massive computing costs behind its models.

The Thrive Holdings partnership also doubles as compensation for OpenAI. People familiar with the deal said OpenAI’s stake grows if Thrive’s portfolio companies do well, turning AI deployment into a revenue-linked investment rather than a one-off service sale.

A broader industry pivot

Taken together, Accenture’s deployment and OpenAI’s investment strategy show how deeply AI is reshaping the consulting and enterprise-software world. As companies race to modernize internal workflows, the integration of ChatGPT Enterprise inside one of the world’s biggest consulting firms signals an acceleration of that shift.

Accenture is betting that its AI-first restructuring will help it win new enterprise contracts. And OpenAI, facing the enormous cost of running its models, is steadily weaving itself into the foundations of the corporate economy — not only through software but also through equity stakes that grow in value when AI transformation succeeds.

Both companies are positioning themselves for an era where AI-driven operational change is no longer optional, and the race is moving from experimentation to full-scale deployment.

Nadella Admits Microsoft’s Size Is Now a “Massive Disadvantage” as He Studies Startup Tactics to Compete in the AI Race

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Microsoft CEO Satya Nadella is hitting the books again — not for a management course or a new technical certification, but to understand how tiny, fast-moving startups are outpacing giants like his own company in the AI era.

Speaking on the MD MEETS podcast with Axel Springer CEO Mathias Döpfner, Nadella said he has spent entire weekends studying how young companies design products. He described it as slipping into “study hall mode,” motivated by the realization that Microsoft’s sheer scale — long considered its defining advantage — has now become “a massive disadvantage” when competing with nimble AI startups.

“This entire weekend, I spent all the time trying to get myself to understand how new companies are building products,” Nadella told Döpfner in the episode released Saturday.

What he sees inside smaller AI firms is a flat structure where scientists, engineers, and infrastructure teams often share the same table.

“They’re able to make decisions instantly,” he said. At Microsoft, by contrast, “I have three divisional heads who manage those three things,” a structure that naturally slows the company down.

The comments point to a core tension at Microsoft: even as the company has become a central player in the global AI race through its multibillion-dollar partnership with OpenAI — which has powered a surge in cloud demand, corporate adoption of AI tools and the launch of products like Copilot — its traditional hierarchy risks dragging its innovation velocity at the very moment when speed matters most.

Nadella’s concerns echo a broader Silicon Valley shift. Meta has spent the past two years flattening teams. Google has reorganized AI divisions multiple times. Amazon has pushed managers closer to engineering teams and trimmed middle layers. The industry’s consensus has shifted: bureaucratic distance kills good AI ideas before they materialize.

“Unlearning the things that made you successful”

Nadella told Döpfner that thriving in the AI era requires abandoning old habits — even habits that once defined Microsoft’s rise to power. He said leaders need to replace the “know-it-all” mindset with a “learn-it-all” mentality.

“The most important skill set for long-term relevance is — how do you be a learn-it-all and not a know-it-all,” he said, adding that “you have to unlearn the things that made you successful to learn something new.”

He emphasized that empathy and emotional intelligence are now indispensable leadership traits. AI is not just a technical shift, he argued — it is a cultural and organizational one, requiring leaders to read teams, customers, and the broader social mood with a different level of sensitivity.

This thinking is already shaping Microsoft’s internal structure. A leaked organizational chart reviewed by Business Insider revealed that Nadella now has 16 direct reports — a handpicked group tasked with breaking down silos, sharing information more efficiently, and accelerating Microsoft’s push into generative AI.

Why corporate AI projects fail

Nadella also delivered a blunt diagnosis of why so many companies flounder with their AI ambitions. Most executive teams approach AI as if it’s a traditional IT upgrade — something you “install,” plug into existing workflows, and expect immediate results from.

“That mistake is going to fail by definition,” Nadella said.

To make AI actually work, he said, companies need four foundational shifts:
• rethink workflows from scratch
• adopt modern, AI-native tools
• train employees extensively
• and ensure data is not locked inside outdated legacy systems

Until organizations rebuild these foundations, he said, their AI projects will collapse under their own dead weight. Only those willing to rethink their assumptions and trim the layers that slow decisions will actually see tangible gains.

Microsoft at a crossroads

The comments highlight Microsoft’s unusual position. The company has never been more central to the global AI boom. Its partnership with OpenAI turned it into the primary commercial gateway for generative AI tools. Azure has been flooded with demand. Corporate clients have moved quickly to adopt Copilot on the promise of productivity gains.

But Microsoft also faces the classic problem of incumbency: the mass of its own success.

Startups with 20 people can ship major architectural changes in a single weekend. Microsoft, with its enormous product ecosystem, compliance obligations, enterprise clients, legal reviews, and global risk posture, can’t operate that way — at least not easily.

Nadella is essentially trying to retrofit startup agility into a company with nearly 200,000 employees.

The result is a CEO who now spends his weekends reading about how companies one-thousandth the size build products — not because he wants to, but because he must.

The AI revolution is outpacing the traditional corporate machine. Nadella seems determined not to let Microsoft become one of the legacy giants that watches from the sidelines.

Growth Journey and Winning Markets – A Tekedia Mini-MBA Graduation Week Lecture

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In the Igbo Nation, the elders will say “when a man prepares food for his kinsmen, and they show up, it is a huge respect”. Good People, for the 18th time, Tekedia Mini-MBA continues to attract learners and professionals who come to “eat” our “food” of business knowledge. Thank you. As we round up this edition to kickstart the next edition in February, I will be leading all the live sessions this week. Our first Graduation Lecture is titled “Growth Journey and Winning Markets”.

Your business growth journey commences with the articulation of a core value proposition and the validation via a product-market fit. This initial phase is less about scale and more about establishing a foundational ‘moat’, a unique and defensible competitive advantage. As we learnt in Tekedia Institute, this moat may not be capital, which can be replicated, but rather a superior intellectual property, a robust network effect, or an unassailable supply chain.

Growth, therefore, is not merely the expansion of sales but the deepening of this competitive advantage, a strategic move to secure an enduring position. Without this underlying strategic anchor, rapid growth can prove to be a liability, as it scales up inefficiencies and exposes the business to competitive threats, akin to building a skyscraper on a foundation of sand.

Winning markets is the ultimate destination of a purposeful growth journey. This victory is not about monopolistic control but about establishing a dominant and resilient presence within a specific market segment. It requires an acute understanding of the market’s operating system, its regulatory environment, consumer behavior, and infrastructural nuances. For instance, a fintech firm seeking to win in Nigeria must navigate a complex blend of formal and informal economies, where digital solutions must coexist with and often leverage traditional financial habits.

Co-learners, the journey to win is a perpetual loop of adaptation and innovation, where firms are not only reacting to the market but actively shaping it through new business models and technological applications. In class today, I will explain why we have the capabilities needed to GROW and WIN, professionally and entrepreneurially. Zoom link in classboard.

Tue, Dec 2 | 7pm – 8pm WAT | Growth Journey and Winning Markets – Ndubuisi Ekekwe | Zoom Link in class board.

UBS-Owned Credit Suisse Charged by Swiss Prosecutors Over Mozambique ‘Tuna Bonds’ Scandal

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Swiss federal prosecutors have filed an indictment against UBS-owned Credit Suisse, accusing the bank of failing to prevent money laundering linked to the infamous Mozambique ‘Tuna Bonds’ scandal that plunged the African nation into a severe economic crisis a decade ago.

The charges, filed by Switzerland’s Office of the Attorney General (OAG), allege that Credit Suisse and its legal successor, UBS, are criminally liable due to organizational deficiencies that prevented the detection and reporting of illicit transactions. UBS, which took over the troubled Credit Suisse in 2023, has firmly rejected the allegations, stating it will “vigorously defend our position.”

The case relates to more than $2 billion in hidden loans that Credit Suisse arranged between 2013 and 2014 for three Mozambican state-owned companies, ostensibly to fund maritime security and a state-owned tuna fishing fleet. Instead, large portions of the funds were allegedly embezzled, creating a massive, undisclosed national debt that the Mozambican parliament had not approved.

The current OAG indictment focuses on a specific transaction that occurred in 2016. Approximately $7.9 million was allegedly transferred from the Mozambique Finance Ministry to accounts held by a foreign consulting company at Credit Suisse in Switzerland.

The OAG alleges the money was obtained or facilitated through the bribery of Mozambican public officials and misconduct in the public sector. Shortly after the money was credited, the account holder transferred $7 million to accounts in the United Arab Emirates.

The OAG’s primary charge against the bank centers on its failure to take “all the required and reasonable organizational measures” in 2016 to prevent the money laundering. Specifically, the indictment highlights “considerable defects” in the bank’s risk management, compliance, and internal directives systems.

A key element of the OAG’s case involves a former Credit Suisse compliance officer, who has also been charged with money laundering. Prosecutors allege that when the bank initiated enquiries into the suspicious transaction.

The compliance officer, despite having “numerous indications” of the illicit origin of the funds, recommended that management not file a report to Switzerland’s Money Laundering Reporting Office (MROS).

Instead, the compliance officer recommended that the bank merely terminate the business relationship. This action allegedly caused or permitted the remaining funds to be moved abroad and laundered.

The Swiss finance ministry had previously fined the bank’s ex-compliance chief for failing to notify anti-money laundering authorities about the transaction, a fine that is currently under appeal.

The Economic Crisis and Previous Settlements

When the full extent of the hidden $2.2 billion debt was exposed in 2016, Mozambique was plunged into a severe economic crisis. International donors, including the International Monetary Fund (IMF), temporarily halted critical financial support, triggering a sovereign debt default and a currency collapse.

Credit Suisse has already faced severe global penalties for its role in the scandal:

Authority Date Settlement/Fine Resolution Details
U.S. & U.K. Authorities 2021 ~$475 Million Resolved bribery and fraud charges; Credit Suisse pled guilty to wire fraud.
Mozambique 2021 N/A Agreed to forgive $200 million in debt owed by Mozambique to the bank.

The current OAG indictment brings the long-running scandal back to the Swiss domestic legal system, forcing UBS—which absorbed Credit Suisse’s legacy legal risks—to fight a high-stakes criminal charge related to events that predate its acquisition.

Summary of OAG Charges in Credit Suisse Mozambique Scandal

  1. Charge Against the Banking Entity (Credit Suisse / UBS)
    This is a corporate criminal charge targeting the bank itself for systemic failures.
  • Accused Entities: Credit Suisse Group SA and its successor companies, UBS SA and UBS Group SA.
  • Charge: Failure to prevent the offence due to organizational deficiencies (under Article 102 in conjunction with Article 305bis of the Swiss Criminal Code).
  • Allegation Details: The OAG alleges the bank did not take “all the required and reasonable organizational measures” in the relevant period, specifically in 2016, to prevent the money laundering that was committed. Prosecutors pointed to “considerable defects” in the bank’s risk management, compliance, and internal directives systems in connection with combating money laundering. This failure allowed suspicious funds, allegedly obtained through bribery of Mozambican officials, to be transferred. Credit Suisse only reported its suspicion to Switzerland’s Money Laundering Reporting Office (MROS) in 2019, long after the transactions and the public exposure of the scandal.

2. Charge Against the Former Employee

This is a charge targeting an individual for their direct involvement and negligence.

  • Accused Individual: An unnamed former Credit Suisse compliance officer.
  • Charge: Money Laundering (under Article 305bis of the Swiss Criminal Code).
  • Allegation Details: The former compliance officer was tasked with investigating the suspicious transfer of approximately $7.9 million from the Mozambique Finance Ministry to a client’s account in 2016. Despite having “numerous indications” that the funds could be of illicit origin, the officer allegedly recommended to management not to file a report to money laundering authorities (MROS) but merely to terminate the business relationship. By recommending termination and carelessly handling the investigation, the compliance officer is accused of having “caused or allowed” the remaining suspect funds to be transferred abroad and thus laundered.

The OAG has dropped criminal proceedings against another former Credit Suisse employee in the same case for reasons of “procedural economy,” given that the case against the compliance officer largely covers the same set of circumstances. UBS has publicly stated it firmly rejects the conclusions against the bank entity and will vigorously defend its position.