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.

How to Drive Artificial Intelligence Usage

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Artificial Intelligence is starting to gain more attention in the media, different industries, analysts, consumers. How do Business Strategists get their organizations to start embracing the use of Machine Learning, Natural Language Processing, Robotic Process Automation???

The Proof is in the Pudding meaning there are some key elements that are needed for Business Leaders to embrace to support the prevalence of AI in the corporate world

1. Education – people need to understand the basic framework of AI and how it can help their business

2. Use Cases – there needs to real examples of how Machine learning has accomplished X or Neural Networks have done Y …. a step further than conceptual theories.

3. Seamless onboarding – This is absolutely a real hurdle that I am hearing about more often than the first two elements. To get people to embrace change you need to help them do it without major hoops to jump through to do it. If business cases are required create a template and framework that is frictionless so people will embrace vs. procrastinate.

4. If you are part of the team that is trying to promote Artificial Intelligence within your company – stay proactive and in touch with the teams that have expressed interest in testing out AI areas. Be prepared to help these teams until there are “self service” tools available.

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Ride the Wave of Industry Transformation – Robotic Automation

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Wave of Automation

Although I know that the broader field Artificial Intelligence has started to be realized in the business world today as a Business strategist and Process Advocate (not a coder or data scientist) I see the opportunity to immerse my talents into Robotics Process Automation. Here are a few insights I would like to share as to why RPA is the path for me:

  1. There are free resources, training and tools courtesy of other RPA vendors and early innovators such as UI Path to help educate, train and spread the opportunity.
  2. The RPA platforms are built with business users in mind (drag and drop options)
  3. Robotics Process Automation is a springboard that other industries will emerge out off – think about the implications of having a small workforce for entrepreneurs and small businesses – it gives me goosebumps at the possibilities that will be realized
  4. Its just a smarter way of running operations – less manual tasks, (= less human errors), employees can focus on other strategic, creative , process functions and roles.

Currently there are vast areas and opportunities for RPA in Financial Services but I am starting to explore how Healthcare, Telecom, Media and Advertising are figuring out the advantages – would welcome any input from leaders in those verticals

Check out the RPA Bible at hthttps://info.symphonyhq.com/hubfs/RPABible.pdf

What are the Stepping Stones to Move AI from Theory to Business Applications

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I am continuing to watch how fellow Business Strategists are embracing areas of Artificial Intelligence into their Roadmaps and Operations. Take the plunge – that ‘s what tests, incubators and use cases are for . I have been listening to podcasts , reading articles and listening the conference speakers – get in the game now or you will get left behind.

Think about leveraging Robotic Process Automation to improve business processes, close the gaps and just plain cover your behinds. There is so much efficiency that can be realized if people step outside the box and look at the % of manual tasks your teams are doing from day to day. Why not put together some real juicy test cases that will use RPA and free up teams to manage the programs, learn new skills, focus on bigger “Rocks” that will deliver more value to the business.

How about personalization of offers to your customers ? Who wouldn’t be delighted to receive an offer that made sense for their specific need – based on a Machine Learning model using Un Supervised data.

I suspect that in meeting rooms across the USA Strategists are discussing these and more ideas but are either waiting for Executives to get on board or taking the “Wait and see what company X comes up with approach”. Don’t wait on a consensus or a first mover for too much longer or you will miss the boat…….

Artificial Intelligence and The Credit Card Business

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Where to begin…. as an Industry veteran within the Credit Card business there are SO MANY AREAS of OPPORTUNITY that exist today to transform the business.

NLP -Natural Language Processing

An easy win in keeping in touch with market and customer perceptions of your brand through social media.

To dig deeper into your customer service conversations via call monitoring to understand the tone and real meaning behind the conversation – are your agents helping the customer? Is their customer satisfaction detectable?

Machine Learning Models

ML models have been successful in detecting fraud, identifying the right customer segments for new offers, acquisition, retention, inactives – there is a whole world of use cases that are being thought out in meeting rooms of Credit Card companies today.

RPA – Robotic Process Automation

I am a HUGE FAN of efficiency so when I read and see how RPA is changing the business landscape of manual tasks and processes that so many Financial Service companies are comprised off I want to Jump up and down in glee ! I think the current challenge is FEAR-people are afraid that these computer bots are going to replace them. Not necessarily the case as humans need to manage the RPA programs. People need to change their paradox of feeling comfortable doing data input and mundane tasks to I AM ACQUIRING A NEW SKILL of how processes will be done in the future.

Well this was just my first pass at outlining high level opportunities …we have a long way to go on this journey