Category: Artificial Intelligence (AI)

There is a lot of talk right now about AI.

Every franchisor, multi-location brand, and business owner is hearing some version of the same thing:

“You should be using AI.”

But that is not the real question.

The real question is:

Where should AI start?

Because AI can help in a lot of places. It can help with reporting, marketing, customer support, operations, finance, admin work, and many other parts of the business.

But if you start in the wrong place, it becomes just another tool, another experiment, or another project that does not really move the business forward.

That is why I like starting with a simple audit.

Not a technical audit.

Not a complicated AI strategy session.

Just a practical business audit that helps you see where manual work, slow reporting, repeated tasks, and inconsistent execution are costing you time and money.

I call this the AI Cost-Saving Audit.

It is built for franchisors, multi-location brands, and location-based businesses.

The goal is simple:

Find the first few areas where AI can actually help reduce cost, save time, and improve execution across locations.

How the Audit Works

The audit looks at five business areas:

1. Reporting & Visibility
2. Marketing & Local Execution
3. Operations & Admin
4. Finance & Control
5. Cross-Location Consistency

Each area has five questions.

For each question, you score your business from 0 to 2.

0 means this is not an issue.

1 means this is sometimes an issue.

2 means this is a clear issue.

Each section gives you a score out of 10.

The scoring guide is simple:

0 to 3 means it is probably not urgent.

4 to 6 means it is worth reviewing.

7 to 10 means there may be a strong AI opportunity.

The important part is to answer honestly.

Do not answer based on how you want your business to work.

Answer based on how your business works today.

If reporting is still manual, mark it.

If your team is chasing locations for updates, mark it.

If your marketing team is still manually adapting everything for each location, mark it.

This audit only works if you are honest about where the friction is.

Area 1: Reporting & Visibility

The first area is reporting and visibility.

For many franchise and multi-location businesses, this is one of the biggest hidden problems.

On paper, the business may have systems.

There may be a POS system, CRM, marketing tools, spreadsheets, review platforms, finance reports, and dashboards.

But when HQ needs a clear view across all locations, someone still has to pull data from different places and stitch it together.

So ask yourself:

Are managers or HQ still pulling reports manually from multiple systems?

Does it take more than one day to get a usable roll-up view across locations?

Are location issues usually spotted only after the damage is already visible in the results?

Do different teams use different versions of the same numbers?

Is benchmarking locations still more manual than it should be?

This is where AI can be very useful.

For example, if your team is spending hours every week pulling reports, cleaning spreadsheets, and summarizing what happened across locations, there may be an AI opportunity.

AI could help create reporting summaries, flag exceptions, show which locations need attention, or help leadership get a faster view of what is happening.

The goal is not always to replace your dashboard.

Sometimes the opportunity is simply helping your team understand the dashboard faster.

Area 2: Marketing & Local Execution

The second area is marketing and local execution.

This is a big one for franchise and multi-location brands because marketing is not just one campaign.

You may have a national campaign, but every location has its own local market, local reviews, local SEO, local offers, local events, and local customer behavior.

So ask yourself:

Is local marketing inconsistent across locations?

Does your team spend too much time adapting content for each location?

Is marketing spend hard to connect to location-level outcomes?

Are reviews, local SEO, or local campaign responses too slow or too manual?

Does the brand team become a bottleneck when supporting many locations?

A simple example:

Your brand team creates one campaign.

Now that campaign needs to be adapted for 20, 50, or 100 locations.

The copy may need to change.

The offer may need to change.

The city name may need to change.

The local angle may need to change.

That type of work can become very manual very quickly.

AI can help here if the process is repeated and the brand guidelines are clear.

It can help create first drafts, local variations, review responses, campaign summaries, or local SEO updates.

The key is that a human still reviews and approves. AI does the first pass. Your team keeps control.

Area 3: Operations & Admin

The third area is operations and admin.

This is where a lot of hidden cost sits.

Most businesses do not lose time only on big strategic work.

They lose time on repeated small things.

The same questions.

The same follow-ups.

The same checklists.

The same weekly admin tasks.

The same “where do I find this?” messages from locations.

So ask yourself:

Do store or location teams repeat the same admin tasks every week?

Do support questions from locations consume too much HQ time?

Are SOPs, checklists, or internal answers hard to find quickly?

Do routine workflows depend too much on one experienced person?

Are delays caused more by follow-up and coordination than by the actual work?

This is one of the most practical areas for AI.

For example, maybe your location teams keep asking HQ the same questions:

Where is the SOP for this?

How do we handle this customer issue?

What is the process for this request?

Which checklist do we follow?

Who approves this?

If the answers already exist somewhere, AI can help make those answers easier to find.

That could become an internal support assistant trained on your SOPs, checklists, policies, and internal documents.

Again, the point is not to remove people from the process.

The point is to reduce repeated manual support so your HQ team can focus on higher-value work.

Area 4: Finance & Control

The fourth area is finance and control.

In a franchise or multi-location business, small finance issues can add up quickly.

One missed item may not seem like a big deal.

But if similar issues happen across many locations, it becomes real money.

So ask yourself:

Are finance follow-ups, audits, or checks still heavily manual?

Is it difficult to compare profitability cleanly across locations?

Do leaders find out about margin issues too late?

Are exceptions, anomalies, or missed items hard to catch early?

Do recurring finance tasks require too much spreadsheet work?

This is not about handing your finance function over to AI.

That is not the point.

The better starting point is exception spotting.

For example:

Which location has unusual numbers?

Which report is missing something?

Which cost looks higher than expected?

Which sales number does not match the usual pattern?

Which item needs a human to review?

AI can help with the first pass.

It can summarize, compare, flag, and prepare.

Then the finance team reviews what matters.

That can save time and help the business catch issues earlier.

Area 5: Cross-Location Consistency

The fifth area is cross-location consistency.

This is one of the core challenges in franchise and multi-location businesses.

You may have the same brand, same playbook, same SOPs, and same process.

But in reality, locations may execute things differently.

Some locations follow the process well.

Some locations do their own thing.

Some are strong.

Some need help.

Some communicate clearly.

Some need repeated follow-up.

So ask yourself:

Do locations execute the same process in different ways?

Is brand consistency difficult to maintain across the network?

Does HQ struggle to know which locations need attention first?

Do strong and weak locations look too different operationally?

Is communication from HQ to locations slower or less clear than it should be?

This is where AI can help HQ see patterns faster.

For example, AI could help compare location performance, summarize issues, identify which locations need support, or help monitor whether the same process is being followed across the network.

The value here is not just automation.

The value is visibility.

HQ cannot manually inspect everything across every location all the time.

AI can help bring the right things to the surface.

Turning Scores Into Action

Once you score all five areas, you will have a score out of 10 for each one.

Now look at the highest scores.

Those are probably the areas where the pain is highest.

But this is important:

A high score does not automatically mean it should be your first AI project.

A high score only tells you there is business pain.

The next step is to turn that pain into a specific opportunity.

Pick your top three areas.

For each one, write down:

The priority area.

The business pain.

The possible cost saving or impact.

For example:

Priority area: Operations & Admin

Business pain: Location teams keep asking the same questions, and HQ spends too much time answering them manually.

Cost saving: Reduce repeated HQ support time and give locations faster answers.

Or:

Priority area: Marketing & Local Execution

Business pain: The brand team spends too much time adapting campaigns for each location.

Cost saving: Reduce manual content work and help the team support more locations.

Or:

Priority area: Reporting & Visibility

Business pain: HQ spends too much time pulling weekly reports from multiple systems.

Cost saving: Reduce manual reporting time and catch location issues faster.

This step matters because “use AI for marketing” is too broad.

“Use AI to create first drafts of location-specific campaign content” is much better.

That is a real workflow.

And real workflows are where AI starts becoming useful.

Can AI Solve This Now?

After you identify your top three opportunity areas, the next question is:

Can AI actually solve this now?

Because not every painful problem is a good AI problem.

Some problems are painful, but the process is messy.

Some problems are painful, but the data is not available.

Some problems are painful, but every output is fully custom.

So for each of your top three areas, ask five questions:

Does the process repeat often?

Does the input already exist somewhere?

Is the output predictable enough to standardize?

Would faster response or better visibility create real business value?

Can a human review exceptions instead of doing everything manually?

If an area gets three or more Yes answers, it is usually a good candidate for an AI pilot.

Let’s say Operations & Admin gets four Yes answers.

The process repeats often.

The questions already exist.

The SOPs already exist.

The output is predictable.

And a human can review anything that needs judgment.

That could be a strong first AI pilot.

Now compare that with a problem where everything is custom, no data exists, no one owns the process, and the output is different every time.

That may still be an important problem.

But it may not be the first AI project.

And that is okay.

The point of this audit is not to force AI into every area.

The point is to find the first area where AI can realistically help.

What Makes a Good First AI Pilot?

A good first AI pilot is usually not the biggest idea.

It is usually the clearest repeated workflow.

Good first pilots usually look like:

Repeated weekly or daily work.

Slow roll-up reporting.

Repeated admin or support questions.

Location-by-location content adaptation.

Review responses.

Exception spotting.

Summaries and follow-up workflows.

The areas that are usually not the best first pilots are:

One-off strategic work.

Messy processes with no clear owner.

Tasks with no usable data source.

Work where every output is fully custom.

High-risk work with no human review step.

The best first AI pilot should be practical.

It should be narrow.

It should have clear inputs.

It should have a clear output.

And it should keep a human in control.

That is how AI becomes useful inside the business.

Not as a random tool.

Not as a chatbot experiment.

But as a way to reduce manual work, improve visibility, and help HQ support locations better.

Final Thought

The question should not be:

“How can we use AI?”

That question is too broad.

A better question is:

“Which repeated workflow is costing us time or margin, and is that workflow ready for AI?”

That is what this audit helps you answer.

By the end, you should know three things:

Your highest pain areas.

Your top three cost-saving opportunities.

Your best first AI pilot candidate.

That gives you a much better starting point.

And once you have that, AI becomes much more practical.

It is not about chasing the newest tool.

It is about finding the places where manual work is adding up across locations, and then building AI into those workflows in a way that actually helps the business.

The 2026 State of AI for Business Report surveyed more than 2,100 professionals, 84% of whom work at B2B organizations and about a third of whom are marketers. This makes this one of the most relevant datasets for B2B professionals trying to understand where AI is taking their profession.
Continue reading “This Is What B2B Marketers Need to Know About the Future of Work”

Your phones ring all day with the same questions. Hours, address, “are you open,” “can I book for Saturday.” Your staff stop serving the customer in front of them to pick up, or the call rolls to voicemail and the caller dials the store down the road.

A customer support voice agent answers every call on the first ring, at every location, day and night. It handles the routine questions and hands the rest to a person. This guide shows you how to set one up in Retell AI for a single location, in steps you can follow without a technical background.

Honest part first. It will not replace your team. It clears the repetitive calls so your staff can serve customers and close bookings. Plan for a couple of hours of setup and about a week of listening before it runs smoothly.

What a customer support voice agent handles
store hours, address, and directions

A voice agent does best on predictable calls:

• store hours, address, and directions
• whether a location is open that day
• simple bookings and reservations
• sending the caller to the right person or store

It is weak on judgment calls. Upset customers, refunds outside policy, and anything unusual belong with a person. Set that handoff from the start, and the agent earns its keep on the calls you were losing.

What you need before you start

• A Retell AI account. Sign up at retellai.com. New accounts include a small amount of free usage, so you can test before you pay.
• A list of the questions your locations get on the phone.
• Your hours, address, and a plan for what the agent says when it cannot help.

Step 1: Create your account and open the dashboard

Sign up on the Retell website and log in. You land on the main screen where everything is managed. A menu runs down the left side. That menu is your map for this whole series

Step 2: Start a new phone assistant

Open the section for assistants, which Retell calls agents, and click to create a new one. Retell asks what type you want. For answering common questions, pick the basic option built for support. You can switch to a more advanced type later once you see how it behaves.

Step 3: Tell it how to behave

A box lets you write the agent’s instructions in plain English. You do not need clever wording. Cover four things:

1. Who it is. “You answer the phone for [Your Business] in [Town].”
2. What it should and should not do. “Answer questions about this location only. If you do not know, take a message or pass the caller to a person.”
3. What a good call looks like. “Help the caller get an answer and book or reach the right person.”
4. How it sounds. “Friendly, short, and clear. Repeat back details to confirm them.”

Add your hours, address, and after-hours line right here.

Tip: Always tell it what to do when it is stuck. A line like “If you cannot answer, offer a callback” stops it from guessing.

Step 4: Pick a voice

Retell gives you a list of voices, and you can play each one. Choose the voice that fits how you want your stores answered. Warmer for a salon or restaurant, steadier for a clinic or service brand.

Step 5: Leave the speaking settings alone for now

A panel controls fine details, such as how fast it replies and how it handles interruptions. The defaults work for a first build. Adjust them after you hear a real call, not before.

Step 6: Give it your information

Rather than typing every detail into the instructions, give the agent a folder of information to read from. Retell calls this a knowledge base. Think of the training binder you hand a new front-desk hire. Add your FAQ, your prices, or a link to your website.

The next post in this series covers this in depth, including how to share one set of information across every location.

Step 7: Test it before you connect a phone number

A button lets you talk to the agent from your computer before any real call comes in. Use it. Ask the real questions your stores get. Check three things. Does it answer correctly? Does it stay on topic? Does it handle “I do not know” without inventing an answer?

Tip: Have two or three staff test it in their own words. People rarely ask the way you expect.

Step 8: Connect a phone number

Go to the phone numbers section. Buy a number through Retell or use one you already own. Set your new agent to answer that number.

Step 9: Call it yourself and confirm

Call the number. Run a few real situations: ask your hours, try to book, and ask one thing it should not know. Confirm it answers, sounds right, and handles the hard question the way you set up.

What this saves you

You now answer every call at one location, day or night, with no one tied to the phone. The calls you used to miss, the ones that quietly became a competitor’s customer, now get picked up. For a multi-unit operator, that gap repeats across every store, so the saving multiplies as you roll out.

Set up one location well, then use the rest of this series to route callers to the right store, keep every location’s answers correct, and make them all sound the same.

Next step: If you would rather skip the setup, Weam builds and runs customer support voice agents for franchise and multi-unit brands, plugged into the tools you already use. Book a cost-saving audit to see what it would save across your locations.

Marketers, take note: New research using 2.7 million data points from the 2026 Winter Olympics revealed how AI systems form and preserve narratives about brands, athletes, and organizations.
Continue reading “Your Brand Reputation Precedes You With AI, Whether You Like It or Not”

As we enter 2026, AI-powered chatbots are evolving from simple automated tools to strategic business partners. Modern organizations are increasingly leveraging AI chatbots to streamline operations, enhance customer experience, and unlock new revenue streams.

With the market expected to surpass $10 billion in value in 2026, and a majority of enterprises embedding chatbots into core operations, AI‑driven conversational platforms are rapidly moving from optional to essential technology in business strategies.

For businesses aiming to accelerate digital transformation, understanding the latest AI chatbot development trends in 2026 is essential to stay competitive, agile, and customer-focused.

In this article, we share key insights and emerging trends in AI chatbot development, providing a roadmap for organizations aiming to optimize their operations and customer engagement in the coming year.

Why AI Chatbots Are Critical in 2026?

AI chatbots aren’t just a nice‑to‑have; they are central to digital transformation strategies worldwide:

• The global AI chatbot market is valued at $10–11 billion in 2026, with analysts forecasting continued rapid expansion.
• 91% of companies with 50+ employees use chatbots in at least part of their customer journey.
• 64% of small businesses plan chatbot adoption by 2026.
• 59% of consumers believe generative AI will change customer interaction norms.

Moreover, nearly half of all website customer interactions are managed by chatbots today, and 62% of consumers prefer chatbot support over waiting for a human agent.

The Growing Role of AI Chatbots in Modern Business

AI chatbots are no longer just customer support tools. They reduce operational costs by automating repetitive interactions, provide 24/7 support, and deliver personalized experiences that foster customer loyalty.

Beyond customer service, chatbots are now integral in:

• Sales and marketing
• Human resources and employee support
• IT helpdesk and internal workflows
• Supply chain management

Modern chatbots powered by AI and advanced Natural Language Processing (NLP) can go beyond scripted answers, making them indispensable for enterprise efficiency and scalability.

Organizations that invest in next-generation chatbot technologies position themselves to transform not just how they interact with customers, but how they operate end-to-end.

Top AI Chatbot Development Trends for 2026 

1. Hyper-Personalization Through Contextual Understanding 

Modern chatbots leverage advanced NLP models to deliver tailored recommendations, not generic scripts. This aligns with the fact that over 60% of consumers believe AI will change how they interact with companies, a key driver of personalization efforts.

Benefits:

• Personalized recommendations
• Seamless multi-step troubleshooting
• Enhanced sales conversions and customer satisfaction

Hyper-personalized chatbots act as trusted digital assistants, transforming both customer interactions and internal operations.

2. Multimodal Interactions: Voice, Text, and Beyond 

The future of chatbots is multimodal. While text-based chatbots remain common, audio and visual AI interfaces are on the rise, with voice integration becoming a standard feature in nearly half of new deployments and expected to grow further.

45% of new AI chatbot deployments already include voice capabilities, and this is expected to reach 78% by 2026 as voice and multimodal interactions become baseline expectations.

Voice interfaces powered by AI speech recognition and synthesis are becoming mainstream, especially on mobile and IoT devices. Businesses can deploy chatbots that switch effortlessly between text and voice, catering to user preferences and contexts.

3. Enterprise-Grade Security and Privacy by Design 

In 2026, privacy expectations rank among the top concerns as chatbots penetrate new business functions, even as adoption grows.

As chatbots handle sensitive customer and operational data, security and privacy become paramount. Regulations like GDPR and CCPA require strict data protection, but beyond compliance, customers expect secure interactions.

USM advises businesses to partner with AI developers who prioritize privacy engineering and adopt federated learning or on-device AI models where data never leaves the user environment, minimizing breach risks while maintaining personalization.

4. Seamless Integration with Business Systems and Workflows 

AI chatbots will act as integrated nodes in business ecosystems. This means seamless interoperability with CRM, ERP, HR platforms, marketing automation tools, and supply chain management systems.

AI chatbots can now automate 40–60% of routine HR, IT helpdesk, and procurement tasks, cutting handling times by 70% and lowering staffing costs by up to 30%.

These integrations enable chatbots to perform sophisticated actions, from updating customer records and triggering workflows to initiating purchases or managing inventory alerts, all through conversational interfaces.

Such connectivity reduces manual work, accelerates response times, and enables proactive engagement based on live business data.

5. Advanced Conversational AI with Large Language Models (LLMs) 

LLM‑powered chatbots such as generative AI systems will dominate ~82.7% of global chatbot usage, reflecting broad enterprise and consumer adoption.

Large Language Models (LLMs) like GPT‑class models empower chatbots to handle complex queries and natural conversation. These LLM‑enabled bots now power most leading enterprise conversions and customer interactions thanks to improved understanding and creative responses.

In 2026, chatbots powered by fine-tuned LLMs will serve as virtual advisors, knowledge bases, and even brand storytellers, delivering coherent, natural, and engaging conversations that build trust.

However, businesses must carefully manage LLM-powered chatbots’ use to avoid risks like misinformation or bias, implementing guardrails and human-in-the-loop systems for quality control.

6. AI-Powered Analytics and Continuous Learning 

Data-driven improvement is a core chatbot trend. Advanced analytics track interaction quality, customer satisfaction, conversion metrics, and bottlenecks. Using AI and analytics dashboards, chatbots continuously learn from conversations, feedback, and business outcomes to improve their accuracy and value.

USM encourages organizations to invest in chatbot platforms with built-in analytics dashboards and automated retraining capabilities. This enables rapid iteration and alignment with evolving business goals and customer needs.

7. Industry-Specific, Domain-Aware Chatbots 

Industries like healthcare, finance, and retail now deploy chatbots trained on domain expertise, not just basic NLP, providing relevant, compliant, and reliable support.

For example, healthcare chatbots will understand medical terminology, patient privacy laws, and clinical workflows. Financial services bots will be versed in regulatory compliance and risk assessments. These domain-aware chatbots provide more relevant, compliant, and effective support, driving deeper impact.

USM’s experience developing tailored AI solutions for diverse sectors highlights the power of domain expertise combined with cutting-edge AI.

8. Human-AI Collaboration for Complex Problem Solving 

Despite rapid AI advances, certain tasks require human judgment and empathy. Future chatbots will seamlessly escalate conversations to human agents with context, enabling hybrid workflows that combine AI efficiency with human insight.

This collaboration enhances customer experience, reduces resolution time, and optimizes workforce allocation. In 2026, businesses will implement intelligent routing, agent assist tools, and unified communication platforms that empower human-AI teams.

 

Strategic Guidance for AI Deployments in 2026 

For organizations across industries embarking on digital transformation, integrating advanced chatbots requires a thoughtful, phased approach:

1. Define Clear Business Objectives 

Start by identifying the specific operational challenges and customer experience goals your chatbot must address. Whether it’s reducing call center volume, improving sales conversions, or automating internal workflows, clear KPIs guide development and measurement.

2. Invest in Scalable, Flexible Platforms 

Choose chatbot frameworks that support multimodal interaction, LLM integration, robust analytics, and easy system integration. Cloud-native, API-first platforms enable agility and future-proofing.

3. Prioritize Data Quality and Privacy 

Effective AI depends on clean, relevant data. Implement data governance policies and ensure privacy compliance from day one. Consider privacy-preserving AI techniques to build customer trust.

4. Start with Pilot Programs and Iterate Fast 

Deploy chatbots in controlled environments to gather user feedback, test integrations, and tune AI models. Use analytics to refine conversation flows and improve performance rapidly.

5. Design for Human-AI Collaboration 

Plan for seamless escalation paths and equip your workforce with AI-powered tools. Empower agents with real-time insights to deliver better service.

6. Commit to Continuous Learning and Improvement 

Treat chatbots as evolving assets. Use conversation data and performance metrics to retrain models, update knowledge bases, and adapt to changing customer needs.

 

Conclusion 

Integrating AI chatbots in 2026 isn’t just a tech upgrade; it’s a strategic business leap. With real business data on adoption rates, market growth, ROI, and customer preference, your article now has the credibility and relevance to rank higher, engage executives, and convert decision‑makers.

At USM, we provide tailored AI chatbot solutions that scale with your needs and deliver measurable ROI. Businesses adopting these trends can gain significant competitive advantages and lead in the next era of digital transformation. Book Executive AI Briefing

 

We had a campaign we wanted to get in front of the right people. The problem was familiar: We had a targeted list of business leaders we genuinely thought would benefit from what we were promoting, but no clean process for actually reaching them at scale. And we didn’t have enough time to do it the slow way [read: without AI].
Continue reading “Here’s How to Use an AI Agent to Build a Cold Outreach Campaign”

Most marketers have a transactional relationship with AI, A. Lee Judge says. They put in a request. They get out an asset. They edit it until it sounds like their voice or their brand’s. Then, they repeat.

But Judge, founder of B2B content marketing and production company Content Monsta, explains AI isn’t meant to replace human content creators; it’s meant to elevate them. Continue reading “It’s Time to Use AI as Your Thinking Partner”

Walk into any large financial institution today and you’ll find the same scene: dozens, sometimes hundreds, of AI pilots and almost nothing in production. The business case is obvious, the ROI is overwhelming and the technology works in the demo. And yet the projects stall at the same gate, every time, when someone in risk or compliance asks a deceptively simple question, “Show me how it made that decision”. If the answer is “we can’t”, the project doesn’t graduate from proof of concept and quietly dies.

In my experience there are really only two paths out of that meeting. The first is the quiet death I’ve just described, and it accounts for the overwhelming majority of stalled initiatives. The second is that the project limps forward by bolting a human onto the end of the process, on the basis that if a person reviews every output then the decision is, technically, a human one. In Europe this approach has the comfort of regulation behind it, because Article 14 of the EU AI Act explicitly requires effective human oversight of high-risk systems, and similar expectations are emerging from supervisors in most major markets. It sounds responsible. The problem is that it rests on an assumption about human beings that the evidence simply doesn’t support, and I’ll come back to why.

From use cases to architectures

It’s worth understanding how we got here. Two years ago most enterprises were busy switching AI experiments off, reining in the hundreds of ungoverned use cases that bloomed when generative AI first arrived. What has emerged since is more interesting. Rather than approving individual use cases one committee meeting at a time, the leading institutions have started pre-approving architectures. If you can get the architecture right, meaning you know where the probabilistic components sit, where the deterministic controls sit and where the audit trail comes from, then you can repeat that pattern across hundreds of use cases. If you get it wrong, every project becomes a fresh fight with the governance committee.

This is a profound shift, and it cuts against the narrative coming out of the frontier labs, which amounts to a promise that you shouldn’t worry about today’s shortcomings because a better model is coming next month. Enterprises have stopped waiting for the risks to evaporate. They have been through the trough of disillusionment and come out the other side with a pragmatic conclusion: for the meaningful proportion of use cases where precision, determinism and explainability are non-negotiable, the answer isn’t a bigger model, it’s a different architecture.

Humans are terrible guardrails

Which brings me back to the second path, the human in the loop. Automation bias is one of the deepest cognitive biases we have, and it doesn’t take long to assert itself. Put a person in front of a stream of AI-generated outputs and ask them to challenge each one and within weeks they stop reading properly. They get tired, they get comfortable, and they approve. Worse, the very skills they would need in order to challenge the machine begin to decay through disuse, so automation bias slides quietly into de-skilling. A human checkbox at the end of a pipeline doesn’t transform an AI output into a human decision; it launders accountability while judgement atrophies.

This matters enormously for the agentic wave, because agentic AI properly understood is not a product category called “AI agents” but AI with genuine agency, the ability to take action autonomously. Autonomy at scale and human review of every output are mathematically incompatible. You cannot have straight-through processing and a person reading everything, so something else has to provide the guarantee, and that something has to be engineered into the stack in the form of deterministic logic, explicit policy and causal audit trails, rather than bolted on as a tired human at the end of the process.

I believe regulators broadly underestimate this. Article 14 was written with the right intent, but the implicit assumption running through much supervisory thinking, in Europe and elsewhere, is that human review is a sufficient control. The institutions deploying at any real volume already know that it isn’t.

The systemic risk nobody is pricing

There is also a second-order problem brewing. When everyone in a market uses the same handful of foundation models, trained on substantially the same data, the only thing differentiating one institution from another is the context and institutional knowledge they bring to those models. Strip that away and you get convergence: similar signals, similar decisions and increasingly synchronised behaviour. Humans have historically been the market’s shock absorbers, slow and inconsistent but gloriously diverse in their judgement, and replacing them with a monoculture of models builds a system that is brilliant right up until it encounters something its training data never contained. Machine learning is predicated on the assumption that the future will resemble the past, and the most expensive moments in financial history are precisely the ones where it didn’t.

Layer on concentration risk, with a handful of compute-constrained model providers experiencing demand growth that outstrips the supply of compute, and you have operational dependencies that would never pass muster if we called them what they are: single points of failure in the supply chain of critical financial infrastructure. One pragmatic principle deserves much wider adoption, which is to cut the tether at runtime. Use large models where they genuinely excel, in the build process, in drafting and in synthesis, but don’t allow the uptime of a production decision system to depend on someone else’s GPU availability.

Generality is the enemy of precision

The deeper issue is a mindset we imported from the consumer internet. The original machine learning successes paired extremely rich data with extremely simple decisions, such as which advert to show you next. We then spent a decade porting that “data is the answer” mindset into domains with far worse data and vastly more complex decisions, and we are now compounding the error with general-purpose models trained, to all intents and purposes, on everything.

A system designed to be good at everything cannot be precise at your thing. Regulated decisions don’t live in the statistical haze of internet text; they live in regulation, policy, procedure and the hard-won institutional knowledge sitting in the heads of experienced people. The organisations that win the next phase won’t be the ones with the biggest model bill, but the ones that treat their own knowledge as a first-class citizen in the AI stack, explicitly represented, reasoned over and auditable end to end, with probabilistic components deployed where flexibility helps and deterministic components deployed where guarantees are required.

That hybrid approach, whether you call it neurosymbolic, governed AI or simply good engineering, is what gets agentic AI out of pilot purgatory. The future of enterprise AI is not a larger language model. It is an architecture worthy of the decisions we are asking it to make.

New research from SmarterX surfaced not just where B2B marketers are in their AI journey but how they feel about it.
Continue reading “What B2B Marketers Really Think About AI in 2026”

Our 2026 State of AI for Business Report surveyed more than 2,100 business professionals — 86% of whom are B2B marketers — and asked what AI training they want most. Continue reading “Three Steps to Start Integrating AI and AI Agents Into Your Marketing Workflows”