0:08
SAP aligns commerce data for AI personalisation
SAP aligns fragmented commerce data structures to enable operational AI personalisation at the execution layer.
Enterprise leadership routinely establishes objectives to anticipate customer requirements and deliver relevant interactions across digital touchpoints. However, the actual infrastructure running inside these enterprises fails to support systematic execution at the required volume.
Recommendation engines display generic product listings because the underlying behavioural data remains isolated. Marketing departments dispatch email communications based on rigid calendar schedules rather than adapting to individual user habits. Corporate loyalty programs issue rewards based entirely on financial transactions while ignoring broader relationship metrics.
The technical ambition exists, yet the foundational architecture remains incomplete. Clean data resides in disconnected repositories. AI capabilities sit dormant within the technology stack. Organisations lack the operational discipline required to execute continuous experimentation. SAP engineered the ‘Advanced Success Plan’ for SAP Customer Experience solutions to resolve these deployment failures.
System architects cannot activate advanced personalisation through standard configuration switches. Enterprise implementations require systematic construction across three connected operational layers encompassing data, decisioning, and delivery.
Data serves as the required baseline architecture. Enterprise systems must aggregate unified, real-time customer profiles while maintaining strict consent awareness. These profiles consolidate information from completed commerce transactions, historical engagement records, active browsing behaviour, customer service tickets, and ongoing loyalty activity. AI models require these complete behavioural data points to function; without this aggregated data, the algorithms operate on defective inputs.
The decisioning layer processes these behavioural data points into executable directives. AI algorithms evaluate the incoming data streams to determine the optimal next product to display, select the exact promotional offer to present, and calculate the precise moment to initiate contact. This layer demands rigorous governance frameworks. System administrators must define operational parameters dictating when the automated algorithm controls the output and when human operators override the machine logic.
The delivery layer executes the personalised experience and presents it to the customer. The system transmits these tailored interactions through the digital storefront, directly into email inboxes, via mobile push notifications, and across loyalty program interfaces. Enterprise architecture requires precise orchestration across these channels to ensure the outgoing communication matches the customer’s live context.
The Advanced Success Plan targets these three layers simultaneously, deploying expert technical guidance and governance structures to transition organisations away from disconnected point solutions toward an integrated operating model.
SAP Commerce Cloud operates as the storefront execution engine for large-scale personalisation. The software features an AI-assisted product recommendation system that displays relevant inventory to individual visitors at precise moments during their shopping sequence. The engine surfaces trending merchandise, related catalogue items, and complimentary accessories designed to drive cross-selling and upselling metrics.
The system bypasses static manual merchandising configurations to evaluate real-time behavioural inputs. This automated evaluation improves conversion performance and increases product discovery at a volume that human merchandising teams cannot manually replicate.
Administrators running SAP Commerce Cloud often fail to activate these advanced features due to predictable technical barriers. Deficient data quality degrades the accuracy of the recommendation models. Integration complexities sever the data connections between the storefront application and the upstream customer profile databases. Marketing departments lack the internal testing frameworks necessary to tune and optimise the algorithms.
The Advanced Success Plan deploys targeted technical interventions to clear these blockages. Technical teams execute data readiness assessments to measure baseline information quality and map the integration pathways required to transmit clean behavioural data into the personalisation engine.
3:52
OpenAI’s Jalapeño chip is Big Tech’s spiciest move away from Nvidia
Nvidia has dominated the AI chip market for years, but the era of total dependence might be ending.   OpenAI just shared its plans to spice things up with Jalapeño, its custom inference chip built with Broadcom, joining Google, Apple, and SpaceX in a growing list of companies building their way out of single-supplier risk. The goal is less of a […]
4:17
Why everyone from OpenAI to SpaceX is building their own chips (and turning up the heat on Nvidia)
Nvidia has dominated the AI chip market for years, but the era of total dependence might be ending.   OpenAI just shared its plans to spice things up with Jalapeño, its custom inference chip built with Broadcom, joining Google, Apple, and SpaceX in a growing list of companies building their way out of single-supplier risk. The goal is less of a […]
4:43
OpenAI limits GPT-5.6 rollout after government request, says restrictions shouldn’t be the norm
“We don’t believe this kind of government access process should become the long-term default,” says OpenAI. “It keeps the best tools from users, developers, enterprises, cyber defenders, and global partners who need them.”
5:01
OpenAI's GPT 5.6 rollout now requires US government approval on a "customer by customer basis"
At the request of the U.S. government, OpenAI will initially make its new GPT-5.6 model available only to select partners, with access approved on a "customer by customer" basis. CEO Sam Altman says this isn't a "preferred long term model." After the forced takedown of Anthropic's Fable, AI labs are afraid of a de facto licensing regime for AI models.
The article OpenAI's GPT 5.6 rollout now requires US government approval on a "customer by customer basis" appeared first on The Decoder.
5:32
Linux Foundation and 20 tech giants launch Akrites to fix open-source flaws before AI-powered attacks hit
About twenty tech companies, AI labs, and banks are joining forces through Akrites to fix vulnerabilities in critical open-source software before AI tools can exploit them.
The article Linux Foundation and 20 tech giants launch Akrites to fix open-source flaws before AI-powered attacks hit appeared first on The Decoder.
5:55
Anthropic doesn't need junior engineers anymore thanks to AI and warns of an economic shock when other industries follow
"Returns on intuition": Why Anthropic no longer needs junior engineers and warns of an economic shock.
The article Anthropic doesn't need junior engineers anymore thanks to AI and warns of an economic shock when other industries follow appeared first on The Decoder.
6:16
Altman won't go public for less than $1 trillion, so OpenAI's IPO may slip to 2027
Advisors are telling OpenAI to hold off on going public until next year. The triggers: volatile tech markets and SpaceX's weak stock performance after its record IPO. SoftBank, one of OpenAI's biggest backers, lost 13 percent in a single day.
The article Altman won't go public for less than $1 trillion, so OpenAI's IPO may slip to 2027 appeared first on The Decoder.
6:40
AI startup Lindy ditched Claude entirely for Deepseek, saving millions as cost pressure mounts on Anthropic
Lindy’s finance team hit a wall when the monthly bill for running Claude started outpacing the salaries of the engineers building the product. The gap grew fast enough that the CEO started framing the switch as a survival decision rather than a tech upgrade.
Deepseek’s models run on a different pricing structure, and the company found they could run the same workloads for a fraction of the cost. That saved them millions in the first few months, freeing cash that now goes straight into hiring and product polish instead of keeping the cloud bill in check.
The move also nudged Lindy’s roadmap. With cheaper compute, they can experiment more aggressively, iterate faster, and keep the user‑facing features they promised without constantly worrying about the bottom line. It’s a pragmatic pivot that shows how tightly AI startups are tied to the economics of the models they choose.
7:31
An AI model programmed nonstop for 19 days on a single MirrorCode task that cost $2,600 to run
Epoch AI's new MirrorCode benchmark tests whether AI models can recreate complete programs without access to the original code. Claude Opus 4.7 leads with a 56 percent solve rate, rebuilding a 16,000-line toolkit in just 14 hours. But every model tested still fails on the most complex tasks.
The article An AI model programmed nonstop for 19 days on a single MirrorCode task that cost $2,600 to run appeared first on The Decoder.
