Author Archives: Donna

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.

AI Won’t Transform Your Business — Change Management Will

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Everyone’s talking about AI. Few are talking about the harder part: getting people to actually use it.

The truth? AI is less about algorithms and more about adoption, trust, and alignment. Without change management, even the most sophisticated AI project can fail.

🔎 Look at IBM’s Watson Health — billions invested, but it stumbled because clinicians didn’t trust the tool, workflows weren’t adapted, and expectations weren’t managed.

🔎 Or Humane’s AI Pin — hyped as the future of wearable tech, but employees’ warnings and user readiness were ignored. The device launched without cultural or customer alignment, and it quickly collapsed.

Both examples highlight a simple fact: technology without change management doesn’t stick.

Here’s what effective AI change management looks like:

Clear vision & leadership alignment → Tie AI to real business outcomes, not experiments. Communication & transparency → Help employees understand the “why” and the “how.” Skills & training → Equip people to succeed alongside AI, not feel replaced by it. Governance & trust → Build frameworks to address bias, ethics, and accountability. Continuous feedback loops → Listen, iterate, and scale responsibly.

This is why hiring an AI + change management SME is becoming essential. They bridge the gap between technical possibility and organizational reality — ensuring your investment drives ROI, adoption, and long-term impact.

🚀 Bottom line: AI won’t transform your business on its own. Change management will. Companies that recognize this will thrive. Those that don’t? They’ll end up as cautionary tales.

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.

Commercial Card Landscape is Flourishing

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The Emerging Middle market has become one of the fastest-growing and most dynamic segments of the global economy. No longer a sleepy middle child between small businesses and large enterprises, this sector is scaling fast—and it needs financial partners who can keep up.

What Is the Middle Market, Really?

We’re talking about companies with annual revenues roughly between $10 million and $1 billion. They’re not startups scrapping for seed money, and they’re not behemoths with a team of in-house finance experts. They’re ambitious, growing firms with serious financial needs—and they’re often underserved.

Why the Middle Market Is Booming

Several forces are fueling the middle market’s rise:

Digitization: Mid-sized companies are adopting enterprise-level tech to scale operations, improve customer experience, and drive revenue. Globalization: Many are expanding beyond borders, navigating cross-border transactions and managing complex supply chains. Private Equity: Investment firms are targeting mid-sized companies for their growth potential and lower risk profiles. Resilience: During economic turbulence, middle market firms have shown they’re agile enough to pivot but stable enough to survive.

This isn’t a trend—it’s a transformation.

The Gap: Where Banks and Card Providers Fall Short

Most banks are structured to serve either small businesses or large corporations. The middle market falls through the cracks. Traditional lending products don’t always scale with them. Corporate credit solutions often come with red tape. Payment systems aren’t flexible enough. And customer service? Usually one-size-fits-all.

For card providers, the picture is similar. Most mid-sized companies have outgrown small business credit cards, but don’t yet qualify for the tailored, perks-heavy corporate programs offered to Fortune 500s.

That’s a huge missed opportunity.

What This Segment Needs

Custom Credit Solutions: Credit limits, terms, and repayment structures that grow with the business. Flexible Payment Platforms: Integration with ERP systems, automated accounts payable and receivable tools, real-time cash flow insights. Cross-Border Services: Streamlined international payments, FX management, and multi-currency cards. Tiered Card Programs: Business cards that reward spend but also offer transparency, controls, and integrations for finance teams. Human Support + Smart Tech: AI-powered tools backed by relationship managers who understand the pace and complexity of middle-market growth.

Why It’s Smart Business

The middle market represents a massive, often untapped source of long-term revenue for financial institutions. These companies are:

Loyal: They stay with partners who help them scale. High-Volume: They move large sums of money regularly. Growth-Focused: As they expand, so does their need for capital, tools, and services. Underserved: Whoever shows up first and delivers value will likely win the relationship for years.

This is the growth engine of modern business, and it’s hungry for tailored financial solutions. The institutions that act now—by building products, services, and relationships specifically for this segment—won’t just help the middle market thrive. They’ll fuel their own future in the process.

Embracing Innovation in Payments

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As I am keeping up to date with everything happening in the Payments landscape today it occurs to me that innovation is much more than a “aha moment” that a single person comes up with. For innovation to truly occur it requires team efforts and for people to embrace it and advocate for it.

Case in point – Artificial Intelligence – its been around since the 1950’s but has only really started flourishing within the last 10 years due to the speed of technology and expansion of data. What’s amazing now is the endless applications of AI in payments from taking on Fraud, AML, to cross selling based on behavior, to helping Small Businesses with invoicing, cash management, to automated underwriting………….

So for all these areas to transform teams need to drive the AI in payments and the market need has to be recognized. I believe that both Payment leaders and start ups are starting to realize the opportunity – let’s see where the road takes the Payment industry!

Shifting the U.S. Payment Ecosystem

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The ideal payment system: Fast, Seamless and Cost effective

The US payment system is continuing to improve as both public and private players in the ecosystem realize that merchants,consumers and businesses will benefit from cost effective digital payments.

Interbank connectivity through ACH, The Clearing House’s RTP network and FedNow services are prime examples of how account to account payments are getting “upgraded”. These instant payments will help drive digital commerce in secure, transparent channel.

Payment innovation will require the ability to know not only recent transactions but proactively predicting future customer /business needs and transactions.

The emergence of payment Fintechs is an opportunity for traditional financial institutions to partner and support the growth of an alternate payment system that benefits everyone with the adequate levels of risk and security covered. (the consumer, the merchant, the system behind the scenes and the financial institutions on both ends of the transaction flow)

Machine Learning and Customer Experience

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By Donna Bailey  Principle DB Innovators LLC

Payments today is an exciting industry to be involved in – it seems to be ever changing on a monthly basis-what excites me is data, technology and how to provide a best-in-class customer experience.

Innovation has always been part of my DNA so when Artificial Intelligence started gaining new momentum it made sense to research and understand how this area would and could impact payments, the business and the customer.

Satisfied customers = business growth

Machine Learning is the subset of Artificial Intelligence-this article focuses on how companies are using Machine learning to improve the customer experience.

Machine learning is based on classical statistics. Statistical inference does form an important foundation for the current implementations of artificial intelligence. It’s important to realize that AI has been around since the 1950’s but only gained significant ground due to the speed of computers and the vast amount of data sets out there over the past 10 years.

Machine learning is all about models, data and computers – supervised, unsupervised or reinforced learning.

According to Forbes- 75% of companies using ML in their customer experience initiatives are seeing a 10% increase is customer satisfaction*

(Satisfied customers=business growth)

Some critical levers that ML helps to move are :targeting, offer presentation, chatbots, virtual assistants. The more data you have about your customers the better services and products can be built to service them/their needs.

Leveraging customer past purchasing transactions, any experience with customer services, response to offers are all ideal data inputs to help the Machine learning model target the right customer segment or present the relevant online products or offers.

In the payments industry there are some well-known areas that Machine Learning has proven to help businesses and customers.

Chatbots/Customer Service

Erica a virtual financial assistant from Bank of America developed in 2017 really pioneered banking chatbots/virtual assistants. The more customers interact with Erica the more it learns about their situations. Erica integrates with customer service consoles and spending and budget tools. This is a great example of how a chatbot is helping customers manage their money and saving the bank service costs.

Fraud/Anti Money Laundering

One of the more prevalent examples of Machine learning being used in payments is to identify anti money laundering and fraud.

PayPal’s Braintree Auth payments uses PayPal’s consumer transaction data in conjunction with software developer Kount’s fraud detection capabilities to authorize high volumes of transactions and verifications in near real-time. Each credit card transaction or verification is analyzed in milliseconds using hundreds of fraud detection tests.

Money Movement

Mastercard is using Machine Learning to fix ACH transfers that fail. According to

One in 50 ACH transactions fail – through machine learning this issue is being addressed and will reduce the frequency of failed transactions being processed.

Machine Learning is also a critical tool for Risk and Compliance teams and processes in financial services. According to a Deloitte whitepaper Anti Money laundering costs $25 billion to monitor and prevent in the USA. The use of machine learning models will significantly improve results and lower costs.

Collections/Targeting

Banks leverage ML in assessing credit risk segments for multiple use cases such as credit card acquisition, collections, loan applications etc. This reduces the cost of underwriting

Collection practices and debt restructuring work best when closely aligned with borrowers’ changing circumstances and propensity to pay.

Machine learning can help companies build robust dynamic models that are better able to segment delinquent borrowers, and even identify self-cure customers (that is, customers that proactively take action to improve their standing). This enables them to better tailor their collection strategies and improve their on-time payment rates.

TrueAccord’s HeartBeat, for instance, is a machine learning tool that helps lenders customize personal interactions in real time, based on its ability to detect why a customer’s payments are late. Companies using machine learning have been able to reduce their bad debt provision by 35 to 40 percent.

McKinsey has seen 10 to 15 percent improvements in recovery rates and 30 to 40 percent increases in collections efficiency.***

Following an account delinquency, issuers allow a brief time window (usually 90 days) before they write off the receivables and turn collection over to third-party providers. This brief period is an ideal time for issuers to apply collection strategies that draw heavily on the capabilities of machine learning.

There are great efficiencies in using AI to determine credit worthiness for acquiring new customers. Machine Learning can be used in targeting and determining risk segments which can save financial institutions millions of dollars in charge offs. These models can also help to identify the right products for different types of customers like “new to credit” or underbanked.

These are just a few examples of way Machine learning is transforming customer experience in financial services – keep an eye out for more sophisticated solutions surfacing as technology and data continue to evolve.

*Forbes AI And ML Can Transform Financial Services, But Industry Must Solve Data Problem First

** The case for artificial intelligence in combating money laundering and terrorist financing A deep dive into the application of machine learning technology

***McKinsey & Company Beyond the buzz: Harnessing machine learning in payments

September 1, 2016 | Article

        How Artificial Intelligence is impacting Frictionless Payments

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        As technology continues to evolve it’s an exceptional time to be part of the payments industry where areas of artificial intelligence are continuing to make digital payments and money movement faster and easier for businesses and consumers.

        Artificial intelligence is helping to deliver personalized banking experiences in a number of ways.

        Machine vision is storing facial recognition data to enable payments go through today. Biometrics is a business a usual method to access or initiate payments through the smartphone. Voice activated payments through AI powered virtual assistants is another innovative area that will help continue to support the growth of digital commerce.

        AI continues to help uncover fraud, AML and support Risk in money movement. Machine learning is helping payment and financial institutions with KYC (know your customer) by enabling a 360 view of the customer though data from a variety of sources both online and offline. The beauty of leveraging AI technologies such as machine learning is that the programs learn from use cases over time so your programs will “ get smarter”.

        Who cannot be excited about continuous improvement …..:0

        Banking as a Service makes sense is today’s Digital world

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        A ride worth strapping in for

        Lately I am seeing a wide variety of BAAS offerings in the market place. This is really exciting because new companies have immerged while legacy institutions are pivotting to embrace the model so in the end the consumer or business benefits.

        The APIs that are enabling a choice of banking service with minimal cost and speed to market delivery are making payments a dynamic industry to keep abreast of in order to stay competitive.

        BAAS offerings are delivering Digital banking solutions to all different market segments globally. For every stage in the customer lifecycle a providing has to ability to offer multiple festures that should make the end customer experience a positive one.

        When you look at notable brands partnering with as a service providers and license holders you get a real time perspective of how payments continues to evolve so buckle up and enjoy the ride !

        The Best is Yet to Come

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        Usually I write about an area of payments that is exploding or I am building my knowledge on through research, training or talking with people.

        Your career is a journey and we have all had our highs and lows but the real gem is in the evolution of it. If you invest in building your experience, knowledge, network- the journey is worth it. One day you meet that great new partner or colleague or pursue a dream job and life has changed. I am excited for everyone out there who is pursuing that next chapter whether it’s a training course, new opportunity or new product build.

        Stay positive, be grateful and give back – it’s all worth it in the end.

        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

        Digital Wallets – Table Stakes

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        One of the biggest impacts Covid 19 had on the economy was driving digital commerce. It still safer, easier and convenient to shop online for almost anything. If you needed to shop in person merchants and customers alike preferred contactless.

        As things are starting to open up due to vaccination progress digital commerce will still be a first choice for many consumers and businesses. The question is is your business ready for mobile wallet ready?

        Push provisioning is a process both with the wallets( Apple, Google,Samsung) and the Card networks. Make sure you understand the requirements for each party involved – they differ both wallet to wallet and the network schemes. Most importantly your end customers need to to understand how to set up and update the card on file. It’s not seamless and be a hassle free.

        Today’s end consumer and business has settled into a fast,convenient digital payment experience and as technology keeps evolving with biometrics,wearables,etc. their expectations are high so be ready to evolve with the marketplace.

        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

        The Flow of Money….

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        So many new players and innovations are emerging its challenging to keep up with them all. Real Time Payments has been my current area of research and investigation and it’s been really eye opening to see what solutions are out there, what’s in development and where things are going.

        First off – I have found that not every provider offers true “real time” – as in money movement within minutes vs. days.

        Update to this point “ real time “ cross border money movement does exist in a few different flavors from the Card networks to some newer players who can deliver.as Compliance and AML are handled and there are a select range of financial institutions who are signing up for this opportunity because guess what – it’s a need that seems to be growing everyday.

        There are Fintechs out there though that will provide the rails for the flow of payments whether it’s domestic or cross border – the speed and compliance factors need to be taken under consideration. It’s been fun figuring out who offers what and where and I am looking forward to understanding the underlying technology that makes money movement possible.

        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 😉

        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.

        Continue reading →

        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…….