Agentic Commerce

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The emergence of AI agents capable of making autonomous purchasing decisions is reshaping the payments landscape. Agentic commerce—where artificial intelligence systems act on behalf of consumers to discover, negotiate, and complete transactions—presents both extraordinary opportunities and complex challenges for the payments ecosystem.

The Promise and the Problem
AI agents are poised to transform commercial transactions. Unlike traditional e-commerce where humans click through checkout flows, agentic commerce enables software to make purchasing decisions based on learned preferences and sophisticated algorithms. An agent might automatically reorder groceries, negotiate insurance rates, or book travel that optimizes for cost and convenience.
For payments providers, this opens remarkable opportunities. Transaction volumes could multiply exponentially as agents handle routine purchases. Micro-transactions become viable without human approval friction. Agents enable dynamic pricing and real-time optimization of payment methods based on rewards or exchange rates.
The fundamental challenge is determining when an AI agent has legitimate authority to spend money. How do we verify that a transaction genuinely reflects the owner’s intent rather than a hallucination or compromise? Current authentication frameworks were designed for human-initiated transactions, making Strong Customer Authentication and similar regulations problematic when software makes dozens of autonomous daily decisions.
Liability and Trust
When an AI agent makes erroneous purchases—ordering 50 pounds of bananas instead of 5—who bears responsibility? The consumer, agent provider, or merchant? Payment networks have chargeback systems for traditional commerce, but these assume human decision-making. New frameworks must distinguish between agent errors, consumer miscommunication, merchant misconduct, and system compromises while balancing consumer protection with practical adoption.
Effective AI agents require extensive data about preferences, habits, and financial constraints. Payment providers will have unprecedented visibility into purchasing patterns and AI decision-making logic, creating significant privacy risks. Questions about data portability and preventing manipulation of agent behavior will shape competitive dynamics.
Infrastructure Evolution
Payments infrastructure must evolve substantially for agentic commerce. Current latencies acceptable for humans become problematic for agents making rapid decisions. Real-time payment rails may become essential rather than premium features. Standardized APIs will be critical for agents to discover payment options and execute transactions across diverse merchants—without them, the ecosystem fragments.
Fraud detection systems currently identify anomalous human behavior patterns. When AI agents routinely make purchases across geographies or categories, these signals become unreliable. Payment providers need new approaches that distinguish legitimate agent behavior from compromised systems, creating a complex dynamic where machine learning monitors other machine learning.
Regulatory Challenges and Opportunities
Regulators are only beginning to address agentic commerce. Existing consumer protection laws assume humans make purchasing decisions with conscious intent. Anti-money laundering and Know Your Customer requirements were designed for people, not software agents. New frameworks must address agent transparency, consent, and accountability without stifling innovation or pushing commerce into unregulated spaces.
Despite these challenges, agentic commerce creates tremendous innovation opportunities. Companies solving authentication could enable new business models. Payment networks adapting fastest to agent-driven patterns could capture disproportionate value. AI could make payments more inclusive by helping consumers optimize choices and navigate complex fee structures.
Building the Future
The transition to agentic commerce will be gradual. Early adoption will focus on low-risk, high-frequency transactions like recurring subscriptions and routine household purchases. As trust builds, agents will handle higher-value and more complex transactions.
Success requires collaboration across payment networks, financial institutions, technology companies, merchants, and regulators to establish standards and build protective systems. The companies viewing agentic commerce as partnership opportunity rather than threat will thrive.
The convergence of AI and payments represents a fundamental shift in commercial infrastructure. While challenges around authentication, liability, privacy, and regulation are substantial, they’re not insurmountable. By approaching these issues thoughtfully and collaboratively, the industry can unlock agentic commerce’s potential while protecting consumers. The question isn’t whether AI agents will reshape payments, but whether we’ll build that future thoughtfully or haphazardly.

The Automation Journey for Marketers and Product Managers

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Automation is no longer a novelty — it has become table‑stakes for both marketing and product teams.  The rise of powerful machine‑learning (ML) and artificial‑intelligence (AI) capabilities means that any organization that relies solely on manual processes will be at a disadvantage.  In this post we explore why automation is so important for marketers and product managers, how it helps them work together more effectively, and the practical tools (including Zapier) that can bring these benefits to life.

Why automation matters for marketing teams

Modern marketing spans multiple channels (email, social media, web, SMS and more), and the expectations for personalised experiences are higher than ever.  Automation tools allow marketers to keep pace and deliver tailored messaging without burning out.  According to a 2024 guide from the Digital Marketing Institute, the primary benefits of marketing automation include:

Time and cost savings:  Automation platforms take over repetitive tasks such as sending emails, scheduling social media posts and managing contact information, freeing marketers to focus on creative, strategic work .  Automated workflows also deliver real‑time analytics that highlight what’s working and what isn’t, enabling teams to optimize their campaigns and lower operational costs . Cross‑departmental collaboration:  Marketing automation tools improve communication between sales and marketing.  Integrated platforms provide sales‑enablement features that allow marketing teams to nurture leads with targeted campaigns, content and communications, fostering alignment across teams . Better budget allocation:  By automating routine work and surfacing actionable data, teams become more productive.  This efficiency allows marketing budgets to be reallocated toward campaigns that produce greater returns , which is vital as marketing budgets continue to shrink . Precise audience targeting:  Automation enables real‑time monitoring of user behaviour.  Platforms can track engagement and automatically segment audiences based on past interactions, delivering personalized communications when recipients are most likely to engage .  This data‑driven targeting increases both return on investment and customer loyalty. Consistent branding:  Automated systems ensure that visuals, messaging and tone stay consistent across channels.  The result is a cohesive brand presence that stands out in saturated digital environments .

Importantly, automation does not replace marketers.  As DMI notes, “automation is not about taking marketers’ jobs away… it simply enhances existing capabilities” .  When routine tasks are automated, marketers can devote more energy to creativity, strategy and storytelling.

How automation empowers product managers

Product managers (PMs) are responsible for guiding a product from vision to launch and beyond.  They rely heavily on data to make decisions, but manually collecting and analysing this information is time‑consuming and often overwhelming.  AI‑powered automation tools give PMs a powerful assist by:

Processing and analysing massive datasets:  Product management has evolved from guessing to data‑driven decision‑making.  AI and automation enable PMs to digest large volumes of quantitative and qualitative data quickly, spotting market trends and customer preferences that would be difficult to identify manually .  AI‑driven analytics can build detailed customer profiles, revealing pain points and purchasing habits, which helps tailor both product features and marketing strategies . Streamlining product development:  AI can analyse historical data and market signals to recommend product features, design elements and pricing strategies, speeding up decision‑making while reducing the risk of launching products that miss the mark .  It also aids in forecasting demand and optimizing resource allocation, resulting in cost savings and higher profitability . Enhancing decision making through predictive analytics:  Productboard notes that AI’s predictive capabilities allow PMs to anticipate future trends, customer needs and potential challenges.  Machine‑learning models can forecast demand, optimize inventory and inform product roadmaps . Personalization and customer engagement:  AI can automate the analysis of user behaviour and sentiment to deliver personalized features and recommendations.  This level of customization improves customer satisfaction and loyalty .  AI‑powered chatbots and virtual assistants also provide 24/7 customer support, reducing the burden on human teams . Automating routine tasks:  Like marketers, PMs spend much of their time on repetitive work such as collecting customer feedback or compiling reports.  AI tools automate these processes, freeing PMs to focus on strategy and creative problem‑solving . Assisting in product discovery and idea generation:  During product discovery, AI systems can conduct market research, perform sentiment analysis and segment customers to identify unmet needs .  They can also generate and validate ideas, build rapid prototypes and conduct real‑time competitive analysis .

These benefits explain why AI and automation are transforming product management.  A 2023 UXmatters article points out that by injecting data‑driven insights into strategic planning, AI enables product managers to allocate resources more effectively and improve customer satisfaction .  However, the same article emphasizes that AI is not a silver bullet; product managers must still bring human intuition to creative decisions and remain vigilant about data privacy, bias and technical implementation challenges .  Successful organizations therefore view automation as a supplement to human expertise, not a replacement .

Unifying workflows with tools like Zapier

Zapier’s value lies not just in its number of integrations but in its flexibility.  By reducing context switching and automating data hand‑offs, it helps marketing teams scale personalized, AI‑driven campaigns across multiple channels .  Product managers also benefit.  A 2024 guide to efficiency tools for product managers notes that Zapier “boosts productivity by allowing PMs to connect all of their team’s apps and services… without any coding” .  By automating the flow of information between project management platforms, feedback tools and analytics dashboards, PMs can maintain a single source of truth and focus on high‑value decisions.

Zapier is just one example.  The marketing automation landscape features platforms like Marketo, HubSpot, ActiveCampaign and Customer.io; each offers features such as omnichannel campaign orchestration, audience segmentation, lead scoring and AI‑driven content personalization .  Similarly, product teams use tools like Productboard, Airfocus and Zeda.io for roadmapping and feedback management .  The common thread among these tools is that they automate away the busywork of data collection and integration, leaving human experts to apply judgement and creativity.

Challenges and considerations

Automation is powerful, but teams must be thoughtful about how they implement it.  Key considerations include:

Data privacy and ethics:  The UXmatters article warns that massive volumes of customer data raise privacy concerns and compliance obligations (e.g., GDPR).  Product managers and marketers must balance the quest for insights with ethical data usage . Human intuition and creativity:  AI can process data and suggest actions, but it cannot replicate human insight.  UXmatters notes that product management often requires creative leaps that aren’t obvious from statistics alone .  Automation should support, not replace, the human role. Technical barriers and integration challenges:  Implementing AI and automation requires technical skills and integration with existing systems.  Teams may need to invest in training and cross‑functional collaboration to overcome these hurdles . Bias and accuracy:  AI models are only as good as the data they learn from.  Biased or incomplete training data can lead to biased decisions and inaccurate recommendations .  Ongoing monitoring and human oversight are essential.

Conclusion

Automation is reshaping marketing and product management, not by replacing human professionals but by augmenting their capabilities.  For marketers, automation drives efficiency, improves collaboration with sales, optimizes budgets and enables precise audience targeting.  For product managers, AI‑powered automation turns mountains of data into actionable insights, streamlines development and fosters innovation.  Tools like Zapier make these capabilities accessible by connecting thousands of apps, layering AI into workflows and eliminating manual data entry.  As organizations continue to adopt automation, the key to success will be balancing technology with human judgement, upholding ethical data practices and fostering a culture that embraces continual learning and experimentation.

True Product Management

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Lately I have been researching the definition of a product manager and believe me there is a wide spectrum out there….

What I am really excited about is that my own internal definition of true product management aligns with the thinking of the best and strongest known companies out in the market today ( i.e HP, Oracle, Google)

They follow Lean Agile principles- delivering products that inspire and bring value

You evaluate the risks up front

Products are built collaboratively not sequentially

It’s about creating solutions NOT implementing features

I am super excited that my product point of view is aligned with the industry leaders -helps me to understand people who miss the mark

Electricity and Payments – there is a synergy

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As I continue my journey in the payments ecosystem I realize that the “pay by card” norm continues to fade as consumers and businesses are transacting by using their phones, watches, cars, accounts.

Money movement from either a tangible device or intangible origination point (such as an account) reminds me of electricity. We need electricity in modern society (just like we need payments) but we don’t see the current moving from the origination point to the lamp, router,device. We have transformed how we use electricity through cars, etc. just as payments continues to evolve.

I would say that the USA is behind as far as usage for the payment method (cards are still the norm for the mass market) but disruption is occurring in small pockets and broader adoption is inevitable.

The synergy between electricity and payments will continue as innovators continue to challenge the norms, invent more frictionless experiences and deliver the end output that we all depend on.

So the next time you turn on a light think payments and how the future is bright for both industries:0

Payments What a Trip!

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As 2020 ends and we head into a new year my focus has broadened to uncovering the exciting innovation in payments. Everything from virtual cards to paying with biometrics is out there and there is so much more coming down the pike.

I continue to be impressed by the engineers, marketeers and product managers out there continually addressing what the customer need is regardless of B2B, B2C, or B2B2C .

As a seasoned financial services leader I am finding it interesting observing the challenges faced by the different players. Fintechs have the speed to market but not the experience or due diligence in handling regulatory areas, Legacy processing companies have the experience but are constrained by their disconnected systems, Old school banks have the experience, branding and customer base but are tied to legacy systems and bureaucracy in getting things done. There are a few gems out there who have the right ingredients ( speed , innovation and experience ) but they need to shine more brightly for customers to find them.

I am a firm believer is “walking the talk” so I encourage the customers who are searching for the right tech/processing partner to make sure you cut through the bologna. Make sure potential processors really provide the services they tout on their web sites. As an experienced Marketer communication is an effective and powerful medium but so are smoke screens 😉