Technology
Trusted AI Needs Human at the Helm
By Linda Saunders
AI promises to make our jobs easier, our work more productive, and our businesses more valuable. New research from Slack finds that 80% of employees using generative AI tools are experiencing a boost in productivity — and that’s just the beginning.
And, with the introduction of AI assistants — including Salesforce’s own Einstein Copilot — the potential for businesses is only growing. AI assistants can already answer questions, generate content, and dynamically automate actions. And someday, these assistants will become digital sales and service agents, anticipating our needs and operating on our behalf.
But with each new AI advancement comes new ethical concerns. It’s one thing if an AI assistant offers a bad product recommendation, but if it takes misguided actions on real-world concerns like personal finances or medical information — the stakes suddenly become much higher.
As we enter this new era of human-AI interaction, how can we harness the power of AI without opening ourselves up to dangerous risks?
Keeping a human at the helm
The AI revolution is an evolution. We’re taking quantum leaps forward every day, but we can’t always explain why AI does the things that it does — or eliminate every instance of inaccuracy, toxicity, or misinformation.
For these reasons, it’s important that we keep humans firmly in control of AI systems. But as AI becomes more and more sophisticated, it can be hard to figure out how to layer in that human touch. We’ve all heard of keeping “humans in the loop,” but with this new generation of AI, it’s sometimes just not realistic for us to engage in every AI interaction or review every AI-generated output.
That’s why, at Salesforce, we believe trusted AI needs a human at the helm. Instead of asking humans to intervene in every individual AI interaction, we’re designing more powerful, system-wide controls that put humans at the helm of AI outcomes and enable them to focus on the high-judgement items that most need their attention. In other words, humans aren’t always rowing the boat — but we’re very much steering the ship.
With a human at the helm, we can design AI systems that leverage the best of human and machine intelligence. For example, we can unlock incredible efficiencies by tasking AI to review and summarise millions of customer profiles. At the same time, we can build trust by empowering humans to lean in and use their judgement in ways that AI can’t.
Making AI a copilot, not an autopilot
There’s a reason this generation of AI products are called copilots and not autopilots. As AI becomes more powerful and autonomous — making decisions and taking actions on individuals’ behalf — keeping a human at the helm becomes even more important. By combining the capabilities of AI with the strength of human judgment, we can make AI more effective and trustworthy.
Here are three ways we’re keeping humans at the helm of Salesforce AI:
-
Prompt Builder Helps Us Automate in Authentic Ways: Prompts, or the instructions we send to generative AI models, are very powerful. A single, human-generated prompt can help guide millions of trusted outputs — but only if it’s constructed thoughtfully. With our newly announced Prompt Builder, we’re helping customers craft effective prompts by seeing the likely output in near real-time to help ensure they get the AI outcome they want. We’ve also added different edit modes within Prompt Builder that allow users to tune and revise their prompts to provide more helpful, accurate, and relevant results.
-
Audit Trails Help Us Spot What We’ve Missed: Our Einstein Trust Layer offers a robust audit trail that allows customers to assess AI’s track record and pinpoint where their AI assistant may have gone wrong and where AI went right. These features help identify issues across large datasets that humans might not spot; and can empower us to use our judgement to make adjustments based on the needs of our organisation. For example, Audit Trail can alert us when an AI tool’s outputs are flagged as “thumbs down” a certain number of times — a sign that the AI-generated outputs might not be meeting the business goals. By aggregating implicit feedback signals, like how often users edit an output before using it, Audit Trail can give us a bird’s eye view of our systems, allowing us to identify trends and take action.
-
Data Controls Help Us Better Guard Our Data: AI is nothing without data. That’s why we’ve designed robust controls in Data Cloud — our fast-growing platform that helps bring siloed customer data together in one place — to help businesses securely action their data. Data Cloud features help organizations harness data for AI-powered insights and intelligence. In contrast, longstanding Salesforce core data controls like permission sets, access controls, and data classification metadata fields empower humans and AI models alike to protect and manage sensitive data.
Pioneering a new approach for the AI era
As the AI era continues to unfold, both humans and technology must evolve along with it. The AI revolution is not just about technological innovation — it’s also about empowering humans to sit successfully at the helm of AI, and use it in ways that are trustworthy and effective.
Our approach is evolving, and we are committed to continued research, learning, and multi-stakeholder collaboration on this topic. But with a human at the helm, we believe we can combine the best of human and machine intelligence for this new AI era — leaning into AI’s capabilities and freeing up humans to do what they do best: be creative, exercise their judgement, and connect more deeply with one another.
With AI and humans working together, we can create more productive businesses, more empowered employees, and ultimately, more trustworthy AI.
Linda Saunders is the Salesforce Director for Solution Engineering Africa
The rules have changed. How companies actually know their customers — really know them — looks almost nothing like it did ten years ago. Old-school research methods are drowning. Too slow, too narrow, too dependent on humans manually stitching together datasets that have already gone cold. Markets shift in days now, not quarters. And the cost of a slow read on consumer behavior keeps climbing. This isn’t just a tooling upgrade. The underlying logic of how businesses decide what to build, what to charge, and who to reach has been gutted and rebuilt from scratch. Staying reactive isn’t a strategy anymore. It’s a liability.
1. Real-Time Data Processing and Pattern Recognition
Consumer intelligence used to run on stale numbers. Analysts dug into data weeks — sometimes months — after whatever actually happened. Modern AI kills that lag. Entirely. These systems chew through enormous volumes of behavioral data on the fly, surfacing patterns that human teams couldn’t find in the same timeframe with ten times the headcount. Machine learning algorithms can process millions of customer interactions, transactions, and behavioral signals simultaneously — pulling clean signal out of what would otherwise be undifferentiated noise. A retailer can track sentiment across social media, reviews, and support tickets right now, catching a brewing problem or an emerging trend in hours rather than weeks. Inventory shifts, pricing moves, message pivots — all of it happens before a trend fully crystallizes. That’s a different game entirely.
2. Predictive Analytics and Consumer Behavior Forecasting
Here’s what actually changed: AI stops consumer intelligence from being a backward-looking exercise. Instead of cataloguing what customers already did, companies can now forecast what they’re likely to do next — and with striking accuracy. Advanced ML models thread together historical patterns and live behavioral signals to predict churn, flag high-value prospects, and project demand across entire product lines. A telecom company can spot which customers are quietly drifting toward a competitor before they ever make the switch — and intervene first. That’s not a marginal improvement. It’s a fundamentally different posture. Resources flow toward the segments that actually matter, rather than spreading thin across the whole base and hoping something sticks.
3. Personalization at Scale
Consumers expect personalized experiences. Full stop. Meeting that expectation at scale — for millions of people at once — is simply beyond what human analysts and traditional segmentation can deliver. Machine learning models read individual purchase histories, browsing patterns, preferences, and demographic signals to build dynamic profiles that drive product recommendations, custom messaging, and tailored interfaces. When building and refining these individualized profiles, marketers who need to enrich their first-party data with verified behavioral signals rely on audience data providers to ensure their models are trained on accurate, high-quality consumer information. An e-commerce platform can serve each visitor a genuinely different experience — different layouts, different offers, different content — all built around that visitor’s unique fingerprint. Conversion lifts. Lifetime value climbs. People respond when recommendations actually fit their lives, not just the average of everyone else’s.
4. Sentiment Analysis and Brand Perception Monitoring
Knowing how consumers feel about a brand means wading through unstructured mess. Reviews, comment threads, support tickets, social posts, video captions — none of it parses cleanly by hand at any useful speed. Natural language processing handles it. NLP systems automatically scan text-based content across digital channels, classifying sentiment as positive, negative, or neutral while bucketing feedback by topic, product feature, or customer segment. An automaker can track online conversations about a specific reliability concern and catch it before it snowballs into a full-blown reputation crisis. No waiting for quarterly surveys. No lag. Brand perception monitoring becomes continuous — and decisions about product fixes, messaging shifts, or service interventions get grounded in real signal rather than gut instinct.
5. Competitive Intelligence and Market Positioning Analysis
Competitive intelligence used to mean manual tracking, sprawling spreadsheets, and perpetually incomplete pictures. AI automates the entire collection-and-analysis loop. ML models watch competitor pricing moves, product launches, promotions, and messaging shifts across digital channels — then stack that data against a company’s own position. Gaps surface. Threats register earlier. A financial services firm can monitor exactly which themes competitors are pushing on social media and which ones are actually generating engagement — then sharpen their own positioning accordingly. Real-time visibility into competitive dynamics means strategic calls about where to invest, which markets to enter, and how to stand apart in crowded categories aren’t made blind anymore.
Conclusion
What AI has done to consumer intelligence isn’t incremental. It’s structural. Real-time processing of massive datasets. Forecasting future behavior instead of autopsying the past. Personalization that reaches millions, not hundreds. Continuous sentiment monitoring. Automated competitive tracking. None of these were realistic options a decade ago. They are now. Companies that wire these capabilities into their core operations make faster, sharper decisions — ones that show up directly in revenue, satisfaction scores, and market share. Those that don’t will keep falling further behind. And the gap between organizations that wield these tools well and those still grinding through traditional approaches? It’s not closing. It’s widening every quarter.
By Adedapo Adesanya
Redtech, a financial technology company backed by Mr Tony Elumelu’s Heirs Holdings, has intensified its pan-African expansion strategy as it extends its payment infrastructure beyond Nigeria and leverages recent global recognition to strengthen its footprint across the continent.
The fintech firm was named in the payments category of the World’s Top Fintech Companies 2026 ranking by CNBC and Statista. It is among the only 11 African companies recognised in this year’s edition.
Developed by CNBC and Statista, the annual ranking identifies 500 leading fintech companies from a pool of more than 3,500 businesses worldwide. Serving as a data-driven benchmark, the ranking highlights companies shaping the future of financial services through technology, innovation and scalable digital solutions.
The company said it is accelerating its push into new African markets with the rollout of digital banking and payment solutions.
As part of this expansion, the UBA RedPay mobile application is now operational in Benin, Burkina Faso, Côte d’Ivoire, Mali and Senegal, marking the company’s first significant digital banking presence outside Nigeria.
It has also introduced virtual account services in Ghana through a partnership with UBA, broadening its payment collection capabilities in West Africa.
The company said the move aligns with its long-term ambition to build a unified payment infrastructure that enables businesses to collect, process, reconcile, disburse and manage funds seamlessly across African markets.
Commenting on the company’s growth strategy, the chief executive of Redtech, Mr Emmanuel Ojo, said Africa’s increasingly interconnected digital economy requires payment infrastructure that can support cross-border commerce.
“Recognition from CNBC and Statista reflects the growing relevance of African Fintech companies on the global stage and validates our ambition to build Redtech into Africa’s payment infrastructure company.
“We are building the technology that enables businesses of every size to collect, pay and manage money seamlessly across channels and markets. As African commerce becomes increasingly digitally connected across multiple market borders, businesses need payment infrastructure that is reliable, secure, interoperable and designed for the realities of operating across the continent.
“Our goal is to help power that growth by making payments simpler and more connected for African businesses, while building solutions that reflect global standards.”
Redtech continues to scale its operations, with available numbers showing that the fintech has processed approximately N45.84 trillion ($33.21 billion) in transaction value through its flagship RedPay platform and deployed more than 55,000 point-of-sale terminals serving merchants across sectors including banking, fintech, retail, hospitality, energy and utilities.
Looking ahead, the company said it plans to expand its collections and financial infrastructure capabilities across all 54 African countries, enabling businesses and financial institutions to manage transactions across multiple markets through a single technology platform.
Technology
CREDICORP Expands Consumer Credit for Locally-assembled Digital Devices With C.L.I.C.K.D.
By Modupe Gbadeyanka
To expand affordable consumer credit for locally assembled laptops and devices for digital workers, the Nigerian Consumer Credit Corporation (CREDICORP) has launched the C.L.I.C.K.D (Credit for Laptops, Internet, Connectivity and Knowledge Digital Devices) scheme.
This initiative is in partnership with the federal government through the Three Million Technical Talent (3MTT) Programme.
It was designed to democratise access to consumer credit, expand economic opportunity and empower millions of Nigerians to improve their quality of life through responsible borrowing.
At the unveiling of the scheme on Tuesday in Abuja at the Afreximbank African Trade Centre (AATC), the chief executive of CREDICORP, Mr Uzoma Nwagba, said the initiative focuses on fellows’ training through the Learn2Earn platform, many of whom are acquiring in-demand digital skills without access to the devices needed to complete their training and transition into employment or entrepreneurship.
Delivered in collaboration with Fidelity Bank as credit administration partner and NASENI and Imose Technologies as device manufacturers, it will provide 1,000 locally assembled laptops to eligible fellows across Nigeria, with 77 beneficiaries in Abuja receiving their devices at the launch ceremony as the first phase of a nationwide rollout.
Assembling the devices in Nigeria shows how consumer credit can expand digital inclusion, strengthen local manufacturing and deepen the country’s technology ecosystem.
“C.L.I.C.K.D. transforms digital devices from a barrier into an opportunity. By embedding affordable consumer credit into a national talent programme like 3MTT, starting with locally assembled laptops, we are giving qualifying Nigerians a responsible pathway to the tools they need to learn, work and earn, while advancing the federal government’s vision for industrial development and job creation on both sides,” Mr Nwagba averred.
Also commenting, the Minister of Communications, Innovation and Digital Economy, Mr Bosun Tijani, said, “Nigeria’s digital economy can only thrive when our people have both the skills and the tools to succeed.
“Through C.L.I.C.K.D., we are helping qualifying Nigerians participate more fully in the opportunities created by the 3MTT initiative while strengthening local manufacturing through the use of locally assembled devices.”
C.L.I.C.K.D. is CREDICORP’s flagship device financing initiative, open to working Nigerians nationwide, with the 3MTT programme as launch partner for this first phase. Interested Nigerians can register at www.credicorp.ng/clickd.
