Demos and proofs of concept usually cover clean, common cases. Edge cases in production can cause poor customer experience and even financial or legal risk. The team at Tech 42 puts the gap plainly: “It can be easy to make AI work in a demo. Serving it to 100,000 people in a HIPAA-compliant environment is a different story.”
Lance Dofflemyer, the Tech 42 company’s CEO, learned early what it means to work where there is no room for error. Tech 42 treats ROI as an architectural question. The company says how a solution is designed directly affects what it costs to run in production. Technical architecture is one input. Volume matters, and so does how fast the AI needs to respond. The company also lists pricing tiers and private pricing agreements among the many factors it evaluates.
Tech 42 says it has seen customers try to build something on their own and see no return. Tech 42 came alongside them, helped solve their business challenges, and delivered the return they were looking for.
The Reactor Test
Early in his career, Lance evaluated whether a nuclear reactor could safely shut down if a fire broke out. Every day he looked at electrical circuits and asked, “What if a fire broke out here? Would it still have enough functionality to shut down?”
He also programmed and started up water plants that served millions of people. In both jobs there was no room for error, and Lance says the work taught him to think outside the box about failures and get creative with solutions.
That foundation carried over to the business side. Companies run on cash, he says, and AI mistakes can be costly. Once the costs and risks are accounted for and addressed, companies have the confidence to move their solutions into production.
Data science taught Lance the importance of good data. He has built machine learning models from scratch, and they were only as good as how well the training data matched what they would see in production.
Working across so many complex use cases also taught him to recognize common patterns. Safety systems that shut down a nuclear reactor have backups upon backups, he says, and the same thinking applies when Tech 42 designs AI solutions. The team accounts for failure and builds in backups too. In Lance’s words: “There’s nothing new under the sun. The terms and widgets may differ across industries, but the solutions are often similar.” In the end, Lance says, technology is useful when it improves people’s lives and those people can trust it and find it easy to work with.
Start With the Business
Tech 42 looks at business outcomes and goals before it talks about tools or architectures. A successful engagement, the company says, has to deliver measurable business benefits. It weighs customer experience, engineering best practices, and how easily the product can be understood, maintained, and evolved over time. The business cost of supporting a solution counts alongside the technical costs.
That changes how an initiative begins. Rather than asking what it can build quickly with AI, the team asks, “What business outcome are we pursuing, what capabilities should the customer gain, and how will we maintain the solution while customers use it?” The answers shape the architecture, the responsibilities, and the ROI from day one. One side of the ledger is how AI can improve the business, with a real number tied to it. The other is whether development, ongoing costs, and maintenance justify the investment.
The company’s litmus test is a real dollar figure for what an AI solution will cost to run in production. The team says it helps businesses work out those costs every day, before anything is built, and breaks them into terms a business can follow. One example: “If this nice AI chatbot adds $2/month per customer, does that wreck your margins?” Can the cost be passed to the customer, and would the customer pay for it? The conclusion is short: “Businesses run on cashflow, so the AI solution has to fit within that constraint.”
What has changed most for Lance is how clearly he can see both sides of the equation. He now understands the true cost of running AI solutions in production, and the bigger shift is how he looks at the value AI brings to the business and whether that value is worth the investment.
Lance sorts what he sees into two groups, AI Native companies and existing companies adopting AI. For the first group, he says, the landscape has changed dramatically over the last three years. What some startups were building three years ago is now commonplace in hyperscaler and mainstream products, and many AI companies are doing the same thing. Not all will survive. His arithmetic: “If 12 products all do the same thing, nine will probably fail and the strongest three will compete against each other.”
He has also seen a lot of classic startup mistakes: building a product no one wants, putting a wrapper around common LLM functionality, or building AI because it can do something, not because it solves an actual problem. If someone can’t tell him the value of an idea in a few sentences, Lance gets concerned. The market will always say what it wants if you listen, he says, but it moves fast, so it takes a constant ear.
For existing companies, ROI is front and center, and Lance wants to know whether the business problem is real. As he puts it: “AI is a tool, and you wouldn’t dig up your garden just because you got a new shovel.” He has talked to many companies with ambitious ideas who describe AI as an all-knowing force that magically figures things out once it is told what to do.
Then they find they lack the data to make the right decisions, and in some cases cannot obtain it at all. They often underestimate the complexity of safeguards, risk, ongoing maintenance, and support, along with the cost of AI and the infrastructure it runs on.
Tech 42 always starts with the business, Lance says, by asking where a company is headed over the next three to five years, what major problems it faces, and what its employees struggle with every day. The company can design and build any AI solution, he says, but what really matters is the business impact a company expects.
Where Business Cases Break?
Assuming the ROI looks good, the next challenge is production. Accounting for edge cases in a controlled environment is critical, the company says. The list includes abnormal customer behavior or questions, unexpected documents, attacks or malicious intent, and more complex situations the AI may encounter.
The more real-world examples a team uses to test a solution in development, the more robust it will be in production. Guardrails protect against unwanted behavior and should run in both directions, covering what is sent to the LLM and what comes back from it.
Trust is another factor. The team at Tech 42 puts it this way: “An AI that doesn’t provide the right context at the right time, or is only right 80% of the time, will erode trust with end users, who will likely abandon it.” Clear evaluation criteria for accuracy and customer experience matter for that reason. The more complex the domain, the more the company wants domain experts approving what the AI says. It often helps customers set up evaluation frameworks so they can test accuracy automatically over time and as new models come out.
Customer experience is also critical. A voice agent that takes several seconds to respond, then gives poor information, is a terrible experience, the company says. Its advice is to define what good looks like for the customers or internal stakeholders using the solution.
Customers are often unsure how much their own customers will use a product. Light usage may keep margins intact. Heavy usage can push LLM costs up until the product is no longer economically viable. The company says to establish up front a hard number for what a business is willing to pay per customer or per solution each month.
Stress testing at scale is important for reliability and customer experience. Something that works well with five users in a lab may grind to a halt at 10,000 users because components aren’t scaling or API limits are being hit. The company says it sanity checks architectures for scale before they are even built.
Compliance and security requirements, multi-tenant capability, auto-scaling infrastructure, and guardrails are among the major factors in getting to production. Another often-overlooked difference is the quality of the starting data set and the identification of edge cases. Without those well-defined, production performance may vary.
The company draws the line in two sentences: “A controlled demonstration shows that an AI system can complete selected tasks. A production system must show that it completes the right tasks reliably, at an acceptable total cost and risk.”
Context Over Cleverness
Agents add another layer. Most companies and data systems were not built with AI in mind, the team at Tech 42 says, so adapting existing systems and business processes can be challenging. The company often evaluates the impact on the business flow more than the technical impact. As agentic solutions become more mainstream, businesses update their existing processes, and technical solutions are built to support the new workflows.
The company says data, infrastructure, orchestration, and monitoring should be treated as one cohesive architecture, not separate systems, even where there is isolation for security. On the data side, agents need to know which records they are allowed to access, what a business term means, and whether the information is current.
People often make decisions in a business workflow based on experience, and that experience isn’t in a database. This business workflow must be captured and automated. Infrastructure has to provide secure APIs and modular systems that can monitor agent activity, cost, and failures. Controls are needed to coordinate agents, models, and tools while enforcing policies, logging decisions, and routing actions to humans for approval when risk and security require it.
Data availability and data organization are a common challenge, the company says, and there is often data-related work required to enable the desired AI outcomes. Across industries, the recurring constraint is whether an organization can give a model enough context to make the correct decision.
Relevant data often sits in separate systems, arrives late, or uses conflicting definitions. Teams may disagree on which source is authoritative, what the AI can access, and who must approve its actions. Some people have great domain knowledge of a business process that is not documented anywhere. In the company’s words: “It’s impossible to expect an agent to perform well when it’s not aware of what good looks like.”
Even a useful prediction has limited value if staff have to fit it manually into existing workflows. Without a clear business owner, a measurable outcome, and a way to learn from errors, pilots struggle to become routine operations. On what a better model can’t fix, the company is direct: “A more sophisticated model can improve an answer, but it can’t reconcile conflicting records, redesign a workflow, or establish accountability on its own.”
Lance describes a related case from the business side. He says he has uncovered great ROI opportunities for businesses, only to hear, “That’s great, but we just aren’t growing anymore.” He puts it bluntly: “That’s not an AI problem. That’s a fundamental business problem.”
Build, Buy, and Own
Some of the factors above apply to the build or buy decision, the company says, including ROI, the technical effort to develop and support a solution, the cost of compliance, and ongoing infrastructure costs, LLM usage among them.
Customers often start with off-the-shelf solutions to test and figure things out, which the company calls a perfectly fine approach. A small use case for a few users may be better served by something off the shelf. A solution serving 100,000 users will likely require infrastructure and ongoing support, and if a business lacks those resources, that affects the decision. Monitoring, guardrails, legal, and compliance have to be weighed as well.
The answer depends on how core the AI solution is to the business. Relying on an off-the-shelf product for a primary business function puts a company at risk. The company says it has seen customers work through the scenarios: “What happens if the third party goes out of business and your company can no longer function? What happens if they change their pricing model overnight and your margins erode? What happens if your customers want a feature and the third party can’t add it for six months?” The decision comes down to cost vs. risk. The more a business depends on the AI solution, the more it should consider building and owning it in-house, provided the ROI is there.
Some customers still run their own physical hardware instead of using hyperscalers, the company says, but they usually end up moving to one. Growth is often a reason, since securing and maintaining the hardware becomes an operational burden. These customers also lack the flexibility to choose GPUs at will, and their end customers are often already on a hyperscaler and prefer to integrate with products that run in the cloud rather than on-prem, since hyperscalers offer cross-account and cross-product tooling.
The team’s advice for a solution that is core to the business: don’t compromise by handing it over to a vendor. That doesn’t mean building every component from scratch, but companies should know their dependencies and have multiple options in case one stops working in the future, through changes in terms or pricing or the vendor going out of business. The company’s practical view: “It’s usually easier to hire an engineer to maintain something that’s important to your business than to integrate a new vendor solution.”
Trusted Advisors
The company says the greatest opportunity for solution partners is to move beyond implementation and become trusted advisors throughout a company’s AI adoption. As AWS expands its AI ecosystem, customers will have more options, but they will still need help identifying the right use cases, choosing solutions that fit their business, and turning early experiments into measurable results.
Tech 42 can add particular value through ongoing guidance on AI security and governance, especially as regulatory expectations evolve. Its own role is to help customers make sound decisions and adopt AI with confidence, and that goes beyond deploying the technology.
What defines a good partner, the team at Tech 42 says, is a thoughtful understanding of a customer’s goals. That means clarifying the problem a solution must solve, defining measurable outcomes, and identifying dependencies and risks before implementation begins. The best partners connect those decisions to an adoption plan, then remain accountable for whether the solution delivers value, not just whether it goes live.
Partners that reshape an enterprise start with the business and provide sustained accountability, the company says. They help redesign workflows, equip people to use them, and measure whether the solution improves cost, quality, speed, or customer experience in practice. They also help customers retain control of their IP, data, and architectural choices as technology evolves. In that role, a partner becomes a trusted advisor that helps the enterprise build the capacity to innovate repeatedly, not just adopt the next tool.
As trusted advisors, the team sometimes tells customers that AI is overkill for a use case or simply too expensive to justify. The company’s position on its own recommendations is short: “We only recommend what makes sense for our customers’ businesses. That sometimes means telling them not to do an AI project, even though that’s what we sell.”
Demos and proofs of concept usually cover clean, common cases. Edge cases in production can cause poor customer experience and even financial or legal risk. The team at Tech 42 puts the gap plainly: “It can be easy to make AI work in a demo. Serving it to 100,000 people in a HIPAA-compliant environment is a different story.”
Lance Dofflemyer, the company’s CEO, learned early what it means to work where there is no room for error. Tech 42 treats ROI as an architectural question. The company says how a solution is designed directly affects what it costs to run in production. Technical architecture is one input. Volume matters, and so does how fast the AI needs to respond. The company also lists pricing tiers and private pricing agreements among the many factors it evaluates.
Tech 42 says it has seen customers try to build something on their own and see no return. Tech 42 came alongside them, helped solve their business challenges, and delivered the return they were looking for.
The Reactor Test
Early in his career, Lance evaluated whether a nuclear reactor could safely shut down if a fire broke out. Every day he looked at electrical circuits and asked, “What if a fire broke out here? Would it still have enough functionality to shut down?”
He also programmed and started up water plants that served millions of people. In both jobs there was no room for error, and Lance says the work taught him to think outside the box about failures and get creative with solutions.
That foundation carried over to the business side. Companies run on cash, he says, and AI mistakes can be costly. Once the costs and risks are accounted for and addressed, companies have the confidence to move their solutions into production.
Data science taught Lance the importance of good data. He has built machine learning models from scratch, and they were only as good as how well the training data matched what they would see in production.
Working across so many complex use cases also taught him to recognize common patterns. Safety systems that shut down a nuclear reactor have backups upon backups, he says, and the same thinking applies when Tech 42 designs AI solutions. The team accounts for failure and builds in backups too. In Lance’s words: “There’s nothing new under the sun. The terms and widgets may differ across industries, but the solutions are often similar.” In the end, Lance says, technology is useful when it improves people’s lives and those people can trust it and find it easy to work with.
Start With the Business
Tech 42 looks at business outcomes and goals before it talks about tools or architectures. A successful engagement, the company says, has to deliver measurable business benefits. It weighs customer experience, engineering best practices, and how easily the product can be understood, maintained, and evolved over time. The business cost of supporting a solution counts alongside the technical costs.
That changes how an initiative begins. Rather than asking what it can build quickly with AI, the team asks, “What business outcome are we pursuing, what capabilities should the customer gain, and how will we maintain the solution while customers use it?” The answers shape the architecture, the responsibilities, and the ROI from day one. One side of the ledger is how AI can improve the business, with a real number tied to it. The other is whether development, ongoing costs, and maintenance justify the investment.
The company’s litmus test is a real dollar figure for what an AI solution will cost to run in production. The team says it helps businesses work out those costs every day, before anything is built, and breaks them into terms a business can follow. One example: “If this nice AI chatbot adds $2/month per customer, does that wreck your margins?” Can the cost be passed to the customer, and would the customer pay for it? The conclusion is short: “Businesses run on cashflow, so the AI solution has to fit within that constraint.”
What has changed most for Lance is how clearly he can see both sides of the equation. He now understands the true cost of running AI solutions in production, and the bigger shift is how he looks at the value AI brings to the business and whether that value is worth the investment.
Lance sorts what he sees into two groups, AI Native companies and existing companies adopting AI. For the first group, he says, the landscape has changed dramatically over the last three years. What some startups were building three years ago is now commonplace in hyperscaler and mainstream products, and many AI companies are doing the same thing. Not all will survive. His arithmetic: “If 12 products all do the same thing, nine will probably fail and the strongest three will compete against each other.”
He has also seen a lot of classic startup mistakes: building a product no one wants, putting a wrapper around common LLM functionality, or building AI because it can do something, not because it solves an actual problem. If someone can’t tell him the value of an idea in a few sentences, Lance gets concerned. The market will always say what it wants if you listen, he says, but it moves fast, so it takes a constant ear.
For existing companies, ROI is front and center, and Lance wants to know whether the business problem is real. As he puts it: “AI is a tool, and you wouldn’t dig up your garden just because you got a new shovel.” He has talked to many companies with ambitious ideas who describe AI as an all-knowing force that magically figures things out once it is told what to do.
Then they find they lack the data to make the right decisions, and in some cases cannot obtain it at all. They often underestimate the complexity of safeguards, risk, ongoing maintenance, and support, along with the cost of AI and the infrastructure it runs on.
Tech 42 always starts with the business, Lance says, by asking where a company is headed over the next three to five years, what major problems it faces, and what its employees struggle with every day. The company can design and build any AI solution, he says, but what really matters is the business impact a company expects.
Where Business Cases Break?
Assuming the ROI looks good, the next challenge is production. Accounting for edge cases in a controlled environment is critical, the company says. The list includes abnormal customer behavior or questions, unexpected documents, attacks or malicious intent, and more complex situations the AI may encounter.
The more real-world examples a team uses to test a solution in development, the more robust it will be in production. Guardrails protect against unwanted behavior and should run in both directions, covering what is sent to the LLM and what comes back from it.
Trust is another factor. The team at Tech 42 puts it this way: “An AI that doesn’t provide the right context at the right time, or is only right 80% of the time, will erode trust with end users, who will likely abandon it.” Clear evaluation criteria for accuracy and customer experience matter for that reason. The more complex the domain, the more the company wants domain experts approving what the AI says. It often helps customers set up evaluation frameworks so they can test accuracy automatically over time and as new models come out.
Customer experience is also critical. A voice agent that takes several seconds to respond, then gives poor information, is a terrible experience, the company says. Its advice is to define what good looks like for the customers or internal stakeholders using the solution.
Customers are often unsure how much their own customers will use a product. Light usage may keep margins intact. Heavy usage can push LLM costs up until the product is no longer economically viable. The company says to establish up front a hard number for what a business is willing to pay per customer or per solution each month.
Stress testing at scale is important for reliability and customer experience. Something that works well with five users in a lab may grind to a halt at 10,000 users because components aren’t scaling or API limits are being hit. The company says it sanity checks architectures for scale before they are even built.
Compliance and security requirements, multi-tenant capability, auto-scaling infrastructure, and guardrails are among the major factors in getting to production. Another often-overlooked difference is the quality of the starting data set and the identification of edge cases. Without those well-defined, production performance may vary.
The company draws the line in two sentences: “A controlled demonstration shows that an AI system can complete selected tasks. A production system must show that it completes the right tasks reliably, at an acceptable total cost and risk.”
Context Over Cleverness
Agents add another layer. Most companies and data systems were not built with AI in mind, the team at Tech 42 says, so adapting existing systems and business processes can be challenging. The company often evaluates the impact on the business flow more than the technical impact. As agentic solutions become more mainstream, businesses update their existing processes, and technical solutions are built to support the new workflows.
The company says data, infrastructure, orchestration, and monitoring should be treated as one cohesive architecture, not separate systems, even where there is isolation for security. On the data side, agents need to know which records they are allowed to access, what a business term means, and whether the information is current.
People often make decisions in a business workflow based on experience, and that experience isn’t in a database. This business workflow must be captured and automated. Infrastructure has to provide secure APIs and modular systems that can monitor agent activity, cost, and failures. Controls are needed to coordinate agents, models, and tools while enforcing policies, logging decisions, and routing actions to humans for approval when risk and security require it.
Data availability and data organization are a common challenge, the company says, and there is often data-related work required to enable the desired AI outcomes. Across industries, the recurring constraint is whether an organization can give a model enough context to make the correct decision.
Relevant data often sits in separate systems, arrives late, or uses conflicting definitions. Teams may disagree on which source is authoritative, what the AI can access, and who must approve its actions. Some people have great domain knowledge of a business process that is not documented anywhere. In the company’s words: “It’s impossible to expect an agent to perform well when it’s not aware of what good looks like.”
Even a useful prediction has limited value if staff have to fit it manually into existing workflows. Without a clear business owner, a measurable outcome, and a way to learn from errors, pilots struggle to become routine operations. On what a better model can’t fix, the company is direct: “A more sophisticated model can improve an answer, but it can’t reconcile conflicting records, redesign a workflow, or establish accountability on its own.”
Lance describes a related case from the business side. He says he has uncovered great ROI opportunities for businesses, only to hear, “That’s great, but we just aren’t growing anymore.” He puts it bluntly: “That’s not an AI problem. That’s a fundamental business problem.”
Build, Buy, and Own
Some of the factors above apply to the build or buy decision, the company says, including ROI, the technical effort to develop and support a solution, the cost of compliance, and ongoing infrastructure costs, LLM usage among them.
Customers often start with off-the-shelf solutions to test and figure things out, which the company calls a perfectly fine approach. A small use case for a few users may be better served by something off the shelf. A solution serving 100,000 users will likely require infrastructure and ongoing support, and if a business lacks those resources, that affects the decision. Monitoring, guardrails, legal, and compliance have to be weighed as well.
The answer depends on how core the AI solution is to the business. Relying on an off-the-shelf product for a primary business function puts a company at risk. The company says it has seen customers work through the scenarios: “What happens if the third party goes out of business and your company can no longer function? What happens if they change their pricing model overnight and your margins erode? What happens if your customers want a feature and the third party can’t add it for six months?” The decision comes down to cost vs. risk. The more a business depends on the AI solution, the more it should consider building and owning it in-house, provided the ROI is there.
Some customers still run their own physical hardware instead of using hyperscalers, the company says, but they usually end up moving to one. Growth is often a reason, since securing and maintaining the hardware becomes an operational burden. These customers also lack the flexibility to choose GPUs at will, and their end customers are often already on a hyperscaler and prefer to integrate with products that run in the cloud rather than on-prem, since hyperscalers offer cross-account and cross-product tooling.
The team’s advice for a solution that is core to the business: don’t compromise by handing it over to a vendor. That doesn’t mean building every component from scratch, but companies should know their dependencies and have multiple options in case one stops working in the future, through changes in terms or pricing or the vendor going out of business. The company’s practical view: “It’s usually easier to hire an engineer to maintain something that’s important to your business than to integrate a new vendor solution.”
Trusted Advisors
The company says the greatest opportunity for solution partners is to move beyond implementation and become trusted advisors throughout a company’s AI adoption. As AWS expands its AI ecosystem, customers will have more options, but they will still need help identifying the right use cases, choosing solutions that fit their business, and turning early experiments into measurable results.
Tech 42 can add particular value through ongoing guidance on AI security and governance, especially as regulatory expectations evolve. Its own role is to help customers make sound decisions and adopt AI with confidence, and that goes beyond deploying the technology.
What defines a good partner, the team at Tech 42 says, is a thoughtful understanding of a customer’s goals. That means clarifying the problem a solution must solve, defining measurable outcomes, and identifying dependencies and risks before implementation begins. The best partners connect those decisions to an adoption plan, then remain accountable for whether the solution delivers value, not just whether it goes live.
Partners that reshape an enterprise start with the business and provide sustained accountability, the company says. They help redesign workflows, equip people to use them, and measure whether the solution improves cost, quality, speed, or customer experience in practice. They also help customers retain control of their IP, data, and architectural choices as technology evolves. In that role, a partner becomes a trusted advisor that helps the enterprise build the capacity to innovate repeatedly, not just adopt the next tool.
As trusted advisors, the team sometimes tells customers that AI is overkill for a use case or simply too expensive to justify. The company’s position on its own recommendations is short: “We only recommend what makes sense for our customers’ businesses. That sometimes means telling them not to do an AI project, even though that’s what we sell.”
Also Read :- CIO Times Magazine For More Information
