UAE customs authorities foiled an attempt to smuggle 16.2 kilogrammes of crystal methamphetamine concealed inside the tyre of a vehicle entering the country through a land border crossing, state news agency WAM reported.

The seizure was carried out by UAE Customs, represented by the General Directorate of Ports Security at the Federal Authority for Identity, Citizenship, Customs and Port Security (ICP).

Customs inspectors became suspicious of the vehicle after advanced scanning systems detected unusual indicators, prompting a manual inspection.

The search uncovered the narcotics concealed inside the vehicle tyre, with the operation carried out in cooperation with the Customs and Security Support Department’s K9 unit, which specialises in narcotics detection.

The General Directorate of Ports Security has recorded 765 narcotics seizures since the beginning of the year, involving approximately 390 kilograms of narcotics and 50,000 narcotic pills, WAM reported.

ICP said it would continue to develop its inspection capabilities as part of efforts to combat drug smuggling and protect the security and safety of society.

For most of its history, the finance function had a clear job: control the costs, close the books, report the numbers. That job has not gone away, but across the Middle East it is no longer the point. Boards and chief executives now want their CFOs in the room where strategy is made, financially stress-testing the options, deciding where capital goes and tracking what it earns back.

Javed Iqbal, partner and CFO Program Leader at Deloitte Middle East, calls it a shift from a “licence to operate” mindset to something more forward-looking, the CFO as a catalyst for the wider business rather than its controller. It is a change that runs deeper than job titles. It is reshaping how finance is delivered, from shared service centres to global business services, how it is governed, and which skills matter, as the discipline moves, in his words, “from an accountant mindset to a greater analyst mindset.”

Iqbal, who led the recently held two-day Next Generation CFO Academy in Riyadh, talks to Gulf Business about the next generation of the finance operating model, where AI is genuinely earning its keep and where the hype still outruns the business case, and what will separate a high-performing CFO function from an average one over the next five years.

CFOs across the Middle East are being asked to do more than control costs and report numbers. How is the role itself changing, and where are you seeing the biggest shift in expectations from boards and CEOs?

The business is asking CFOs for ever-increasing inputs on enterprise strategy development and execution. From financially evaluating strategic options to optimum resource allocation and monitoring returns on investments. In addition, driving improvements around revenue growth, sustainable cost management, asset financial management and efficient funding.

In effect, shifting from a “license to operate” role around an operator/controller mindset to delivering as a forward-looking strategic/catalyst for the wider business.

Many companies in the region have spent years building shared service centres and centralising finance functions. What does the next generation of that model look like, and where are businesses still failing to capture the efficiencies they expected?

The finance delivery model has three key components: operations, governance, and business partnering. Operations has been on a journey from centralising transactional processing to building shared services to deploying value-creating global business services. Global business services has characteristics of being customer-centric, multi-functional, centres of excellence, outsourcing and digitally enabled. In essence, building scalable, resilient, and flexible back office platforms to enable growth.

Governance is going from financial controllership to business controllership, and business partnering from financial planning and analysis to enterprise performance management. Efficiencies will come from becoming customer-centric, focusing on the future of work (industrialisation and specialisation), and fully enabling digitalisation, including AI.

Cost pressure remains a major concern for companies, but aggressive cost-cutting can undermine growth. How are CFOs balancing margin protection with continued investment in technology, talent and expansion?

Taking a longer-term sustainable cost management approach rather than just deploying short-term tactical fixes that hurt growth. A longer-term approach challenges current services, operating models, deployed assets, cash management, and longer-term funding structures. Building more resilient and agile cost structures, leveraging partners and ecosystems.

ERP modernisation and AI are now central to finance transformation strategies. Where is AI already delivering measurable value in finance functions, and where is the hype still running ahead of the business case?

Firstly, the co-existence of multiple digital applications is pivotal to long-term value creation for finance functions. AI has huge potential to deliver process automation and even greater value-added capabilities on the advanced analytics agendas.

Currently, there is still a gap in achieving the full required ROI, but this will be bridged by taking a more holistic approach rather than just point-focused use cases. This holistic approach includes linking AI learning with data maturity, re-imagining work and transforming operating models with robust governance.

As companies automate more transactional finance work, which roles or skills are likely to become less important, and what capabilities will define the next generation of finance leaders?

Controllership and business partnering will become the dominant roles required, with finance building greater digital literacy and superior interpersonal skills, so that its workforce can manage digitally enabled processes and deliver advanced predictive and prescriptive insights

Finance transformation (FT) programmes can be costly and disruptive. What are the most common reasons these projects fail to deliver the promised return on investment, particularly in the Middle East?

More FT programmes would deliver superior efficiency and effectiveness if they leverage a more holistic approach. There is a seven-step approach that connects customers, services, work done, digital enablement and capability delivery, structure and sustainability. Also, ensuring Business, IT and HR work together with finance to develop and deliver the finance function of the future. The silver bullet is putting finance people at the center of the transformation.

In the near future, what will separate a high-performing CFO function from an average one, and which of today’s finance priorities do you think companies are underestimating?

A high-performing CFO function will be ‘famous’ for driving business insights as opposed to processing transactions. The skills in finance will include not just technical finance but also statistical, mathematical, data and digital individuals.

In effect, financial analysis coupled with business and performance analysis. Shifting from an accountant mindset to a greater analyst mindset

Google has unveiled Gemini 4 Argon, its most powerful artificial intelligence model to date, as the technology giant steps up its push to compete with OpenAI and Anthropic at the frontier of the rapidly evolving AI market.

The new model anchors Google’s Gemini 4 generation and has been designed to handle complex, long-running tasks spanning software engineering, enterprise work and cybersecurity.

Argon is larger than Google’s previous top-tier Pro models and is being positioned against the most advanced systems from rivals OpenAI and Anthropic, Reuters reported.

Google said Argon was built to “sustain deep reasoning across complex, long-horizon workflows”, with the company initially rolling it out to a select group of cybersecurity organisations through its Fairwind Program.

Broader access will follow in phases as Google tests safeguards and gathers feedback. The company said developers, enterprises and consumers would eventually receive access, beginning with paid API customers and Google AI Ultra subscribers, although it did not give a firm release date.

The model will launch at an introductory price of $2 per million input tokens and $10 per million output tokens, according to Google.

One of Argon’s biggest technical changes is a dramatic expansion in the amount of content it can generate during a single task. Google has increased the model’s output limit from 64,000 tokens to one million tokens, giving it greater capacity to work through lengthy and complicated problems.

Google said thousands of its employees are already using Argon internally for coding, research and writing.

Among the examples highlighted by the company, Argon helped Google researchers improve the efficiency of a quantum computing process by 40 per cent, while teams of AI agents identified data-centre memory optimisations that have freed more than 300 tebibytes of capacity.

Google is also deploying Argon agents to help migrate large C and C++ codebases to the Rust programming language.

Cybersecurity takes centre stage

Cybersecurity is emerging as one of the biggest battlegrounds for increasingly capable AI models.

Google said Argon can autonomously identify, validate and patch critical software vulnerabilities, with trusted cybersecurity partners receiving versions of the model without some of the guardrails that will apply to general users.

Cybersecurity company Wiz is already testing Argon through its Scan for Good initiative. Google said the model identified a critical vulnerability affecting healthcare software used by hospitals that previous frontier AI models had missed.

The company is meanwhile working with the US government under its voluntary process for pre-release access to advanced AI models.

Google’s launch comes as competition among the world’s leading AI developers intensifies. OpenAI and Anthropic have continued to push increasingly capable models into coding and enterprise applications, putting pressure on Google to demonstrate that its Gemini platform can compete at the top end of the market.

Google said Argon delivers performance comparable with frontier models including OpenAI’s Astra and Anthropic’s Opus across key coding and cybersecurity benchmarks.

Dubai-based carrier flydubai has suspended flights to and from Israel following an altercation on the flight deck of FZ1073, as authorities continue their investigation into what happened on board.

The airline said the temporary suspension would remain in place while investigators work to establish the circumstances surrounding the incident on September 30.

“Following the incident involving flight FZ 1073 on 30 September, and in coordination with the relevant authorities, flydubai flights to and from Israel will be suspended while the investigation continues,” a flydubai spokesperson said.

“The safety and wellbeing of our passengers, crew and operations remain our highest priority. This temporary suspension will allow the relevant authorities to continue their work and establish all the facts surrounding the incident.”

Flydubai said it remained in close coordination with government authorities, regulators and airport stakeholders and would review the suspension as more information became available.

“We are fully committed to supporting the ongoing investigation and maintaining the highest safety standards across our network,” the airline said.

The carrier apologised to passengers affected by the disruption and said it was working to provide support and alternative travel arrangements where possible.

No date has yet been given for the resumption of flights between Dubai and Israel.

What happened on flydubai flight FZ1073?

The suspension follows a serious incident aboard FZ1073, which was operating from Dubai International Airport (DXB) to Israel’s Ben Gurion International Airport (TLV) on September 30.

Flydubai subsequently confirmed that an altercation occurred in the flight deck during the journey.

Read more: flydubai confirms altercation on Dubai-Tel Aviv flight

The airline said on-duty flydubai crew who were travelling on the flight successfully secured the aircraft before diverting it to Tabuk Airport in northwestern Saudi Arabia, where it landed safely.

All passengers and crew were accounted for following the emergency landing.

Tabuk Airport said the captain and co-pilot sustained injuries and were transported to hospital for medical treatment.

Flight-tracking data also showed dramatic changes in the aircraft’s altitude during the incident.

According to Flightradar24, FZ1073 dropped from 30,875 feet to 16,750 feet between 05:22:07 and 05:22:36 — a fall of 14,125 feet in just 29 seconds.

The aircraft also transmitted emergency transponder codes during the incident.

Flydubai has stressed that the underlying reasons and motives behind the incident remain unknown and are subject to a formal investigation.

There is an irony in the current race to personalise marketing with artificial intelligence: the easier it becomes for every company to create personalised content at scale, the less distinctive personalised content becomes.

That is why I believe one of the most important marketing ideas of the AI era is counterintuitive. Over my 20+ year career, I have watched digital marketing move from websites to social, from broad segments to automation, and now from automation to generative AI.

Each wave promised greater personalisation. AI will certainly make personalisation cheaper. It may therefore make genuine human understanding more valuable.

For financial services, where trust and judgement and the consequences of decisions can be significant, that distinction matters.

AI is moving from experiment to infrastructure

The direction of travel is no longer theoretical. The DFSA’s 2025 survey found that 52 per cent of authorised firms in the DIFC were actively using AI, up from 33 per cent a year earlier. Generative-AI adoption rose 166 per cent year on year, and 75 per cent of firms expected to increase AI use over the following three years.

AI is moving rapidly from experiment to infrastructure in the DIFC.

Clients are moving too. EY reported that 71 per cent of GCC investors expected wealth managers to incorporate AI into their offerings.[2] This is not a market resisting technology. It is a market normalising it.

For marketers, the implication is uncomfortable: “we use AI” will soon have roughly the same differentiating power as “we have a website”. I would not build a strategy around simply having the tool. The advantage will come from the judgement behind it – where AI is used, where it is not, and whether the client experience becomes more useful as a result.

Source: DFSA AI Survey 2025

Personalisation is not the same as relevance

Marketing has always wanted to treat clients as individuals. AI gives us extraordinary new tools: propensity models, next-best actions, dynamic content, conversational interfaces, automated summaries and richer segmentation.

But personalisation can easily become cosmetic. We have all seen the temptation: a client’s name in an email, a recommendation triggered by one click, fifty versions of a message because the system can generate them. None of that automatically creates relevance. Sometimes one carefully framed message, built around a real client expectation, is worth more than a hundred technically “personalised” variations.

The test is simple: does the client feel that the institution understands the decision they are trying to make?

That requires context that data alone may not contain. A client who has suddenly increased cash holdings might be nervous about markets, preparing for a property purchase, funding education or planning a business investment.

Behaviour is a signal. It is not a complete story.

Working across multiple global markets over the last two decades has made this especially clear to me. The same observable behaviour can carry very different meanings depending on life stage, culture, family obligations, mobility and financial confidence.

Data can tell us what happened. Good marketing still has to ask why it might have happened – and remain humble about the answer.

A practical model for human-centred AI in financial marketing.

Human judgement becomes a premium layer

The best model, therefore, is not human versus machine. It is machine-enabled humanity.

AI should remove the low-value work that makes financial experiences slow and generic: searching, summarising, routing, drafting, detecting patterns and surfacing relevant information. It should give advisers, service teams and marketers more time to interpret, explain and empathise.

CFA Institute has made a similar point in its discussion of trust in digital wealth management: clients value seamless technology and human empathy, and people continue to place considerable weight on trusted human advice for consequential investment decisions.

That is a useful design principle. Automate repetition. Augment judgement. Personalise context. Bring in a human when stakes, ambiguity or emotion rise.

The new danger is industrial-scale sameness

Generative AI has another consequence marketers should take seriously: it raises the average quality of content while threatening to compress the difference between brands.

If everyone has access to competent writing, images, video, optimisation and personalisation, the market will be flooded with material that is technically polished and strategically forgettable. This is a challenge I think every marketing leader – myself included – needs to guard against. When production becomes cheaper, the instinct is to produce more. The better response is to raise the threshold for what deserves to be produced at all.

Producing more simply because we can is precisely the wrong response. Scarcity moves upstream. Point of view, judgement, proprietary insight, cultural fluency, taste and credibility become more important because they cannot be created simply by increasing content velocity.

In other words, AI makes craft abundant. It does not automatically make meaning abundant.

Trust requires explainability

Financial marketing also has a responsibility that many consumer categories do not. Personalisation is built on data, and AI introduces questions about accuracy, fairness, privacy and accountability.

The DFSA survey found that although 60 per cent of firms had some form of AI governance structure, 21 per cent still lacked clear accountability or oversight mechanisms even where AI use could be critical.[1] For clients, the technical details may be invisible, but the principle should not be: if an AI-enabled interaction informs a meaningful financial decision, the organisation should be able to explain the basis for it and where accountability sits.

Transparency, therefore, is part of the user experience. “Why am I seeing this?” may become one of the most important questions in personalised financial marketing.

The leadership question

The marketing leader’s task is no longer simply to adopt AI tools. It is to decide where machines genuinely improve the client experience and where human judgement creates more value.

My own test for any AI-enabled marketing use case is deliberately simple. Does it make the experience more useful? Does it make the decision clearer? Does it respect the client’s data and expectations? And does it free human beings to do something more valuable? If the answer is yes, AI can deepen relationships. If the answer is simply “it lets us produce more”, we may be optimising the wrong thing.

The coming competitive advantage will not belong to the brands that appear most automated. After years of marketing across different cultures and levels of financial sophistication, I am convinced of the opposite: technology creates the most value when the client notices the understanding, not the machinery.

The best automation makes the experience feel more considered, responsive and, paradoxically, more human.

Humanity needs to be designed, not assumed

There is one more trap. Organisations sometimes assume that adding a human adviser at the end of a digital journey automatically makes the experience human. It does not. A client who has repeated the same information three times, received generic prompts and then been handed to someone with no context has experienced a process failure, not empathy.

Human-centred design means continuity. The system should carry context forward, recognise when uncertainty is rising and give the employee enough information to add judgement rather than ask the client to start again.

The real promise of AI in this context is not simply fewer human touchpoints. It is fewer low-value interactions – so the human moments that remain can be materially better.

The writer is the director and head of Marketing, CEEMEA, Franklin Templeton.

The views and opinions expressed in this article are those of the author and do not necessarily reflect the views of the organisation.