The laborious process of holiday shopping, traditionally a complex matrix of deciding, price comparing, and review checking, is being fundamentally disrupted by the rise of conversational Artificial Intelligence (AI).

Consumers are now seamlessly integrating generative AI platforms like OpenAI’s ChatGPT, Google’s Gemini, and Perplexity into their buying journeys, treating them as personalized, omniscient shopping assistants. This shift is not merely a trend; it is a seismic commercial event poised to reshape the digital retail landscape and redefine the meaning of online presence.

According to market data published by CNBC, AI has quickly become an indispensable driver of sales and customer engagement this holiday season. Salesforce projects that AI will propel a staggering $263 billion in global online holiday sales, accounting for a significant 21% of all holiday orders globally in 2025. This massive figure is supported by high consumer adoption, with various surveys indicating that between 40% and 83% of consumers are planning to utilize AI tools for their shopping needs this year.

The value of an AI-fueled customer is proving demonstrably higher for retailers. Data from Adobe shows that shoppers arriving on retail websites via generative AI platforms are 30% more likely to make a purchase and are 14% more engaged—evidenced by spending more time on the site and having a lower immediate bounce rate—compared to those from traditional non-AI sources. Consequently, these AI-driven visits are generating 8% more revenue per session.

This phenomenon is transforming product discovery. For many shoppers, the AI assistant acts as a curator, unearthing relevant brands that might otherwise be overlooked. This is leading to significant shifts in purchasing patterns, as evidenced by one retail tech CEO who reported that approximately half of the gifts she bought came from brands she had never encountered before the AI guided her search.

The new customer journey starts with a conversational query, such as, “Where can I find the best gift under $50 for a tech-savvy teenager who cares about sustainability?” The AI then serves up a tailored, product-level conclusion, rather than a list of generic links.

The New SEO: Pivoting to Answer Engine Optimization (AEO)

The profound shift in customer search behavior is compelling retailers to abandon outdated digital strategies centered around traditional Search Engine Optimization (SEO) in favor of Answer Engine Optimization (AEO). SEO, a game of keyword density and link building designed to rank links on a search results page, is proving ineffective in the age of conversational AI, where the goal is to be the single, definitive answer the AI provides.

AEO focuses on semantics, content structure, and credibility to ensure a brand’s products are chosen by the AI model. Since AI platforms prioritize providing a single, highly relevant, and trustworthy answer, brands are now engaged in a critical restructuring of their content:

• Conversational Structure: Content must be optimized for natural language and provide direct answers to questions in a concise, authoritative format, often using an upfront summary or rich answer blocks.
• Rich Data and Trust: Retailers must provide highly detailed, trustworthy data, including real-time inventory, clear product specifications, brand certifications, and aggregated customer feedback, rather than simply relying on basic attributes.
• Solution-Focused Content: Brands like Ethique Beauty have shifted their content strategy from describing products (e.g., “shampoo bar”) to addressing customer problems (e.g., “solution for oily scalp,” or “how to sleep with curly hair”). This approach aligns with the conversational nature of AI queries, which often begin with a problem or need rather than a specific product name.

Retailers are now directing funds away from traditional paid social media and search campaigns, which are seeing performance declines, and into the infrastructure required for AEO. This strategic pivot, while requiring significant internal and consulting investment, yields a high return on investment because the customer delivered by the AI is typically more qualified and further along the purchasing funnel.

The Retail Giants’ AI Strategy Split and Internal Tools

The country’s biggest retailers are pursuing distinct and competitive strategies to win the AI shopper, primarily defined by their relationship with external large language model (LLM) platforms:

Amazon’s aggressive approach to blocking external AI crawlers, coupled with its focus on its powerful internal assistant Rufus, signals a strong belief that its massive data moat is a competitive advantage that should not be shared. Conversely, Walmart and Target are betting that placing their products where the customer is starting the conversation—in ChatGPT—will provide superior reach, even if it means sharing the customer experience.

Walmart CEO Doug McMillon has championed this, stating that agentic AI will be a core growth driver, helping people “save time and have more fun shopping.” Target has observed that its Gift Finder tool is already driving higher engagement and larger shopping carts than its previous tools, highlighting the superior performance of conversational, problem-solving AI.

Where AI Falls Short

Despite the momentum, AI shopping tools are not yet universally perfect. Resistance to adoption persists due to functional and psychological barriers, including perceived complexity, risk concerns, and value deficits compared to traditional methods.

When the technology fails to meet conversational expectations, the results can be frustrating. One startup founder, Diana Tan, provided detailed body type, preference, and budget information to a chatbot for a capsule wardrobe recommendation. The tool repeatedly suggested “boring basics” like black shirts and gray pants, leading her to feel like she was talking to a “demented grandmother” and ultimately abandoning the tool.

For consumers who enjoy the process of shopping, the highly efficient, hyper-focused nature of AI can eliminate the serendipitous discovery of browsing. As Tan noted, it “takes the joy out of shopping.”

These instances show that while AI excels at research and optimizing for known intent, it still struggles with tasks requiring deep creative nuance, abstract style sense, or maintaining a human-like, flexible conversation flow. This dichotomy means retailers must continue to maintain a dual strategy, optimizing for both the hyper-efficient AI shopper and the traditional browser, until the AI’s creative and empathetic capabilities mature.

Google on Thursday rolled out a revamped version of its Gemini Deep Research agent, rebuilding the tool on top of Gemini 3 Pro, the company’s latest flagship foundation model.

The new system is designed not only to generate long-form research reports, but also to function as an embeddable research engine that other developers can plug directly into their own applications.

The timing was not lost on anyone in the industry. What once looked like a broad field of AI competitors is becoming narrowed into a head-to-head sprint between two giants that now announce upgrades within the same news cycle, each determined to stay a step ahead in the escalating race for model dominance.

Google’s upgraded Deep Research system is no longer just a tool for generating polished research reports. It has broadened into a programmable research agent suited for large-scale information synthesis, built to survive massive context loads and long reasoning cycles that would normally push an LLM into error territory.

The company is positioning it as a backbone for due diligence review, drug toxicity evaluation, and other high-precision analytical tasks. The major engineering shift was the introduction of Google’s new Interactions API — a feature aimed at letting developers embed Deep Research-grade capabilities into their own products and control the agent layer more directly.

Google also disclosed plans to integrate Deep Research into a series of its core services, including Search, Finance, the Gemini app, and NotebookLM. It is edging toward an internet where people no longer hunt for information themselves; they dispatch an agent trained to do it for them. Google leaned heavily on the claim that Gemini 3 Pro is its “most factual” model so far, tuned to lower hallucination rates in long-chain reasoning tasks where a single incorrect step can ruin the entire output.

To back its performance claims, Google introduced a new benchmark called DeepSearchQA, intended to test how well an agent handles complex, multi-step information missions. It was released as open source. The company also tested the system on Humanity’s Last Exam, a notoriously difficult general-knowledge benchmark filled with obscure tasks, and on BrowserComp, which evaluates an AI agent’s ability to carry out browser-based operations.

In addition to the widely-praised Gemini 3, Deep Research topped the leaderboard on Google’s own benchmark and on Humanity’s. But OpenAI’s ChatGPT 5 Pro landed close behind in both tests and overtook Google on BrowserComp. Even those results barely had time to settle. Hours later, OpenAI announced GPT-5.2, which the company said outperforms rivals — including Google — across a wide suite of standard benchmarks.

This back-to-back rollout marked another escalation in a rivalry that has grown unusually public. The industry now watches releases from both companies like heavyweight rounds. Each upgrade arrives with the sense of a reply to the other.

Google introduced a package that pushes its agentic vision further, giving developers a programmable research companion and moving the company closer to agent-based search. OpenAI countered with a model built to strengthen its leadership in reasoning, speed, and multimodal performance.

The speed of these releases also signals how aggressively both companies are trying to define the next phase of AI development. The focus is shifting from raw model power to advanced agents that can plan, browse, run tools, complete tasks over long stretches, and handle increasingly complex workloads with minimal supervision. Whoever builds the most reliable and scalable agent layer stands to redefine how people use software, how information moves across the web, and how organizations make decisions.

For now, Google seems to be pushing deeper into agentic research systems integrated across its own ecosystem, while OpenAI continues to refine its core model lineup and deploy it across consumer and enterprise channels. Each announcement seems to trigger the next. Each benchmark update sets off another. And as these systems continue to evolve in public view, the industry’s center of gravity keeps shifting toward a true duel for AI supremacy.

The Dangote Petroleum Refinery has cut its ex-gantry petrol price to N699 per liter, a reduction of N129 from the previous N828, and a move that is already reshaping competition in a market long dominated by import-driven pricing.

Sources inside the refinery confirmed the adjustment, and the Independent Petroleum Marketers Association of Nigeria (IPMAN) later validated it.

“The refinery reduced the price to N699 per liter and told marketers that we can start buying the product for the festive season,” IPMAN’s national president, Abubakar Garima, told TheCable.

The price cut has added to the growing shift in Nigeria’s petroleum market. For decades, petrol prices in Nigeria were heavily anchored to the cost of imported products. Dangote’s arrival has tilted that equation. By lowering its ex-depot price to N699, the refinery is setting a benchmark that importers cannot match under current global and domestic conditions.

Aliko Dangote, who has repeatedly expressed the refinery’s goal to dominate the local market, is competing directly with importers.

“Prices are going down. The reason why prices have to go down is because we have to also compete with imports,” he said, adding that both diesel and petrol “will continue to be sold in the market at a very reasonable price.”

The cut is already putting pressure on the Nigerian National Petroleum Company Limited (NNPC), which reduced pump prices across its Lagos and Abuja retail stations in late November to N900 and N940 per liter. Industry sources say that although the NNPC and independent marketers will be forced to make further adjustments once Dangote’s new price filters through the market, they will not be able to compete.

Nigeria’s long-standing dependence on imported petrol is mired in exorbitant import costs. Even when global prices eased, Nigeria faced elevated landing costs because of the weaker naira, freight charges, insurance, and port fees. During late 2024, the landing cost oscillated between N829 and N976 per liter. In November 2024, figures widely circulated by market operators, including the Major Energy Marketers Association of Nigeria (MEMAN), put the landing cost in the N970s. By October 2025, updated industry data showed a drop to the low N830s as global prices cooled and shipping routes stabilized.

Those figures explain why imported fuel cannot compete with Dangote’s new pricing. At N699, the refinery is offering a product more than N130 below the best-case import landing cost and nearly N280 below the high end seen in late 2024. This leaves NNPC and independent importers in no position to bring in foreign petrol and sell at affordable rates without incurring heavy losses.

Against this backdrop, Nigeria’s petrol import is expected to drop further. Nigeria spent N1.28 trillion on petrol imports in the third quarter of 2025, down from N2.3 trillion in Q2, but it remains a major burden on foreign exchange reserves. The drop in Q3 was tied to lower global price averages and occasional improvement in refined product availability. Nigeria’s import bill remains large because domestic output has only recently begun to scale.

In 2020, petrol imports cost N2.01 trillion. By 2021, that figure rose sharply to N4.56 trillion. In 2022, it hit N7.71 trillion. In 2023, it eased slightly to N7.51 trillion. Then came the surge in 2024: N15.42 trillion, driven by a more than 40 percent slide in the naira and higher global shipping and insurance fees.

These costs created long-standing volatility in Nigeria’s retail petrol market, forcing pump prices to chase global dynamics. With Dangote offering large volumes priced locally, that structure is beginning to shift. The refinery’s latest cut puts pressure on all fuel retailers — including NNPC — to align with domestic, rather than imported, pricing.

That shift is happening as Dangote pursues an ambitious expansion plan. The refinery, currently rated at 650,000 barrels per day, is already producing diesel, aviation fuel, and petrol. Work is ongoing to raise total output to 1.4 million barrels per day within three years, a scale that would make it the world’s largest single-site refinery.

The Federal Government has welcomed this expansion, calling it a “game-changer” with the potential to lift West Africa’s entire energy landscape. Beyond stabilizing domestic supply, the government sees Nigeria becoming a regional export hub, with refined products heading to neighboring countries and beyond.

There is also reprieve for one of the refinery’s biggest challenges. OPEC’s latest data shows Nigeria pumped 1.436 million barrels per day in November 2025, up from 1.401 million barrels per day in October. That trend gives the country more room to allocate crude for domestic refining.

Meanwhile, Nigeria’s national petrol consumption has slowed. Federal data shows average daily usage fell to 52.9 million liters in November 2025. Analysts say this is linked to earlier high pump prices, changing transport habits, and the gradual entry of Dangote’s products, which are expected to stabilize supply and lower retail prices in 2026.

With the N699 price now in play, traders say the market has entered a new phase. The days when imported fuel determined domestic pricing are fading. As Dangote ramps up production in 2026, the downstream sector is shifting toward a new price anchor — one set inside Nigeria rather than offshore. However, energy analysts warn that this will be impacted by international oil prices.