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Crewing for Yacht Delivery Services

When you're thinking about a job in the yachting industry, don't overlook delivery jobs when conducting your search. There are several reasons yacht owners or management companies look for crew to deliver their yacht. One common reason is that the yacht either has been refitted or is going to be refitted and it needs to be transported to or from the shipyard. Another reason many yacht owners hire delivery crew is that they are planning a special cruise in another country and they need a crew to get the yacht there safely.

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While some owners simply choose to have their yachts shipped to their destination, having an experienced crew onboard delivering the yacht can actually save money. For example, the delivery crewmembers can perform many of the small repair or maintenance tasks that need to be accomplished before the yacht reaches its destinations.

Yacht owners also like having their yachts delivered rather than shipped because it gives them more flexibility in the schedule and route.

Freelance or Delivery Service Company?

When it comes to getting a job delivering yachts, you may have two choices open to you. First, if you're a qualified, trained, and experienced captain, you can look for work independently as a freelance worker. That means that you will have to market yourself, network on the docks and local area, and drum up the work yourself. You may also have to hire a few crewmembers to work with you. If you are well connected in the industry this could be a good alternative for you.

However, for most crewmembers, it's highly likely that you will need to sign on with a delivery service company. These companies specialize in this kind of service. Some delivery companies also offer additional services like training, consulting, or delivering other boats than yachts. Opportunities include:

• Yacht captain positions
• Yacht engineer jobs
• Positions for mates
• Deckhand jobs

Since these are one-way trips, you'll receive a per day fee as well as your food, and the client should also supply a plane ticket for you to return to the home dock or city.

Delivering Yachts To Break Into the Industry

You'll also get to travel to a great variety of locations, from domestic coastlines to the Caribbean and even Asia and other countries, depending on the owner's requirements and/or the delivery company's service area. Some delivery companies only deliver to the Caribbean or other specific locations.

Delivery captains also say that working these jobs helps you to build connections with others in the industry, which can result in future work. The more networking you do, the more likely you are to find other work.

If you're just starting out and willing to work as a volunteer or lower pay to get the experience and sea hours, then working as part of a delivery crew may be a great choice for you. It can also work well for experienced captains who want to have more flexibility in their schedules or work as independent contractors.

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BE BRAVE – BE INSPIRED – SAIL OVER YOUR HORIZON

Great Career Opportunity Mile Builder Gain Sea Miles Experience Learn On The Job Sail Around The World

Work with us!

Delivery Captain offers an amazing career opportunity for all the sailing enthusiasts out there who are passionate about pursuing their dreams and making a profitable career out of it. Whether you are an experienced sailor or just starting out in this field, this is the chance you have been waiting for.

Gain valuable sea miles and on-the-job experience as part of our delivery team. Build your professional resume and increase your job opportunities with leading crewing companies, yacht brokers, and boat owners by gaining experience on different types of yachts.

Our fast-track program provides direct sailing experience and the opportunity to work with qualified professionals who will contribute to your career formation.

Upon successful completion, Personal References and Logbooks will be provided to all crew members as required. Join the yacht delivery industry today with Delivery Captain

PLEASE REMEMBER THAT IN ORDER TO RECEIVE INFORMATION ABOUT ANY JOB YOU NEED TO CREATE AN ACCOUNT. THE ONLY WAY WE COMMUNICATE INITIALLY WITH ANYONE IS VIA NEWSLETTER. THANK YOU.

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World Health Organization

Covid-19 travel recommendations by destination, which countries are open for tourism during covid-19.

Please keep in touch with the latest news on COVID-19 as this may affect your plans to join any delivery. Find out which countries are open and which ones are closed or very difficult to travel to. 

US VISA: Please bear in mind that based on our direct experience we advise all our crew who intend to enter the USA via sea to have a B1/B2 Visa.

Entry via sea using ESTA will not be accepted by the US government.

Please bear in mind that although in the EU you only need a 3-month passport validity to be granted travel entry, in the US you are required to have at least 6 months. This excludes your staying in and out. 

These proposed fonts of information are not the only ones available on the web, these are only an indication. Please feel free to make your search.

Upcoming Deliveries: Poland - Saint Thomas France - Grenada  France - Ft.Lauderdale La Rochelle Miami La Rochelle Gibraltar

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Sailing Yacht Jobs & available Sailing Yacht Crew listed on Find a Crew™

Tell me about find a crew™ and sailing yachts.

Whether it be on a luxury sailing yacht or weekend trailer sailer, a monohull or a multihull, the feeling of moving along the water using only the power of the wind is addictive. Working on a sailing yacht is an adventure in itself, providing you with a rewarding way to experience some of the most beautiful places in the world. Different skills are required for different yacht sizes and journey types, so you should do some research into what you are looking for and what you are able to offer if you are looking to work on a sailing yacht. Find a Crew™ is used by many professional and recreational sailing yachts around the world to look for a variety of crew positions. If you are only interested in working on a sailing yacht, you can fine tune your profile to indicate this so that you are only matched against sailing yachts.

Yacht delivery crew

Halcyon Yachts endeavour to recruit only the best yacht delivery crew.

Our yacht delivery skippers are commercially endorsed RYA Yachtmaster Ocean or above. Many are also qualified RYA Yachtmaster Instructors and all are highly experienced. If you feel that you can meet the high standards required to work for us as a skipper then please submit your CV using the email address below.

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Best in the business.

Our first mates are all also qualified skippers and have relevant experience.

Our Deck crew must be capable of competently standing a solo watch at night and are generally qualified to at least RYA Day Skipper level.

Become a member

If you wish to be part of a professional yacht delivery team then please register with us and you will be informed of suitable positions.

To become a member of Halcyon Yachts we ask for a one-off membership fee of £25. Membership benefits include some fantastic discounts at various RYA sailing schools and also on clothing and technical equipment. As a member of Halcyon Yachts you will always be the first to hear about our many and varied sailing opportunities.

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Our standards are high, but if you think you have the required skills and are interested in sailing with Halcyon Yachts then please complete this form

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Crew travel insurance.

Yachtsman’s Travel Insurance from Topsail Insurance. Policies offer high levels of cover and the flexibility to suit all types of journeys and time periods away from your place of residence. The Yachtsman’s Travel Insurance Policy is suitable for those who sail as a crew, own their own boat or charter vessels

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We are Wilson Halligan

The leaders in superyacht recruitment

• Job ID #2251845
• 41m (135ft) Motor Yacht
• Starting 27th Mar 2024
• Sint Maarten
• Posted 12th Mar 2024

A crewmember required for a crossing or boat delivery. The exact training, qualifications and experience will depend on the exact requirements of the delivery position. It could be a qualified position such as assistance on the bridge, or in the engine room or on the interior skills required to assist with laundry and crew cooking.

Salary: This will depend on the delivery position.

Deckhand/Mate

Boat type: Absolute Navetta 48

Location: New Bern, NC

Greece, Turkey, Cyprus and back to Greece

Boat type: Vindo 50 MS

Location: skiathos

Mate / crew for historic gaff ketch on charters

Boat type: Wooden Gaff ketch, 1920

Location: Gosport / Ramsgate

RYA Cruising Instructors wanted - Greece

Boat type: Various Yachts

Location: Lefkada, Greece

Captain & Chef duo wanted

Boat type: Sunreef 50

Location: Fort Lauderdale USA

Skipper wanted for summer cruise into the Med.

Boat type: Fisher 37

Location: A Coruna

Delivery Crew

Boat type: Various Sailing Yachts

Location: Various

Crew - HI to WA

Boat type: Columbia 36

Location: Honolulu HI

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How to make more money outside of your job using these 8 side hustles

• Instawork's Kira Caban and Bankrate's Ted Rossman shared options for side-hustle work.
• Working as a security guard at a nearby concert venue is one option.
• Another is a side hustle that involves something you're passionate about.

Side hustles can be anything from building a profitable business to spending a few minutes completing an online survey or working a second job that falls outside your primary work schedule.

"For people who are looking for a side hustle, think about the time of year that we're in and sort of some of the bigger events or ongoings that people really love to go to," Kira Caban, the head of strategic communications for Instawork — a platform where people can find flexible work — told Business Insider. "That's usually a good indicator that these businesses are going to need help handling customer demand, and it's a great way to make extra money on the side."

A lot of people make money through side work. In a LendingTree survey of American adults adults aged 18 to 42 conducted in November, 58% of Gen Zers and 53% of the millennials said they had a side hustle . Additionally, 80% of survey respondents who said they had a side hustle said they were actually more reliant on this income given the current economy.

While some side gigs might be a temporary way to pick up some money for a vacation or a big purchase, many side hustlers are in it for the long haul. Nearly 40% of respondents to a Bankrate survey of American adults conducted in April 2023 said they had side hustles; 44% of those with a side hustle said they thought they would always need to do extra work — whether that is "to make ends meet," "for discretionary expenses," or both. Ted Rossman, a senior industry analyst at Bankrate, told BI he hoped more side hustlers were taking on extra work as a passion project or "to get ahead to pay off debt, to save, and invest more."

"What we found was that most people are doing it just to get by, which I thought was kind of a sad reality that people felt like they needed to and they feel like they're going to keep needing to," Rossman said.

Below are some side-work options for 2024, according to Caban and Rossman.

If you like listening to music while working your day job, you might enjoy working at concert venues.

If artists are coming to play near where you live, Caban said, it could be a lucrative side-job opportunity.

"They're going to need people that work concession stands," Caban said. "They're going to need people to help with security. They're going to need people to do retail and cashiering, and it's a great way to sort of get in on the action."

Take Davis Perrigo, for example, an accountant who moonlighted as a security guard for a Taylor Swift concert when she played in Nashville last year because he couldn't land concert tickets.

Baseball games

Big arena concerts might not be the only side-job opportunity involving a stadium event. Caban said another gig could be at a baseball game.

She said professional baseball players are "going to be in season very shortly with opening day and then the rest of the season."

There are a lot of side hustle options that can be done from home, such as completing surveys , Rossman said.

"It's not a get-rich-quick kind of thing — unless, I suppose, you're dedicating a ton of time to it — but that's something that's pretty quick and easy to do," Rossman said.

Scott, a person who previously shared his side-hustle recommendations with BI, has completed surveys as a side hustle , such as through platforms Connect and Prolific .

"If you have really busy schedules, if you're not looking to make a ton of money from side hustles, then these survey sites are definitely perfect for you," Scott said. "You just have to be really picky and choosy."

Pet sitting or other kinds of care work

Another side hustle could be care-related, like watching someone's pets or other care-related side hustles.

"In our research, we found that the most common side hustle for men has to do with landscaping and home repair," Rossman said. "For women, it tends to be either crafts or childcare. So those all seem pretty accessible things that a lot of people could get involved with."

Related stories

Meghan Lim, who has walked dogs and watched pets as side work, previously told BI, "The best price to start for dog walking is $20 for a 30-minute walk." Lim also suggested meeting with a prospective client first before taking care of their pet so you could ask them questions about it.

"There are situations where you may not end up liking the responsibility or maybe you just don't find the right fit with the owner," Lim said.

Food delivery

There are several food- and grocery-delivery platforms people can use for side work, such as Uber Eats or Instacart. Rossman thinks the barriers to entry are low for food delivery.

Erin Couch works a sales job and started delivering groceries with Instacart as a side gig in February 2023. Couch told BI she's able to make money from tips, too, and did this gig on the weekends to help pay off her credit-card debt.

"I use my Instacart money to pay off my debt once a week, $100 every Sunday — and that's it for right now," Couch said. "Once I feel more comfortable with my money situation, I'll start paying more to get it down."

Holiday-related jobs

If you are planning ahead or don't have time for a side job yet, you could take on a holiday-related gig toward the end of the year. The holiday season is "notoriously" an expensive time, Caban said.

"We will see people look for fun events, holiday parties, all of the things that you think about when you're celebrating the holidays," Caban said.

Caban said these jobs could include being a server for an event, a bartender, or a person who helps with setting up or cleaning after events. She added that it could be a great way to enjoy the season while making money to offset additional costs.

But side hustles for the holiday season don't have to involve events. BI previously reported that being an Airbnb host could be an option.

A passion could become a side hustle too

If you don't want to head to an event for a side job, think about things you enjoy. Rossman said side gigs could be a way to work on something you have a passion for — and it could be something you don't get a chance to do for your day job.

"Especially with somebody's primary job, sometimes they're not able to do something that's a true passion project," Rossman said. "You need to do something that pays the bills."

Rossman added that "if you're going to be putting in extra time and effort above and beyond your primary job" to work on a side hustle then it could be more enjoyable if it's something you care about.

"Who knows? Maybe that even leads to a full-time gig down the road or furthers your experience in an area that you're interested in," Rossman said.

Find a side job similar to your primary job

Rossman said people could also leverage their skills, experience, and network to find side work.

He gave the example of a graphic designer taking on side projects where they could apply their design skills. Those looking to do something that involves similar tasks or skills to their job might want to first talk to their primary employer.

"You have to be careful that you're not doing anything competitive or anything that would concern your primary employer," Rossman said.

Caban shared similar advice as Rossman. Caban said that people could start with what they're excited about or had experience doing.

"You may be really passionate about art or that creative side, but that's not something that you do in your day job," Caban said. "I think that you need to take stock in the things that you're passionate about and then apply some of the skills that you have from your day job and figure out how to combine the two so that you can get going and start making that extra money."

In general, Rossman's main piece of advice is to "follow your passion, figure out what you're good at, think about that total cost of ownership — the time you're putting in, what you're going to get out of it, what kind of materials you might need to source."

Have you made money from a side job or a side hustle? Reach out to this reporter to share at [email protected] .

Watch: Marketing teams need both creative and technology talent — and not everyone should do both, says PwC's CMO

• Main content

A generative AI reset: Rewiring to turn potential into value in 2024

It’s time for a generative AI (gen AI) reset. The initial enthusiasm and flurry of activity in 2023 is giving way to second thoughts and recalibrations as companies realize that capturing gen AI’s enormous potential value is harder than expected .

With 2024 shaping up to be the year for gen AI to prove its value, companies should keep in mind the hard lessons learned with digital and AI transformations: competitive advantage comes from building organizational and technological capabilities to broadly innovate, deploy, and improve solutions at scale—in effect, rewiring the business  for distributed digital and AI innovation.

About QuantumBlack, AI by McKinsey

QuantumBlack, McKinsey’s AI arm, helps companies transform using the power of technology, technical expertise, and industry experts. With thousands of practitioners at QuantumBlack (data engineers, data scientists, product managers, designers, and software engineers) and McKinsey (industry and domain experts), we are working to solve the world’s most important AI challenges. QuantumBlack Labs is our center of technology development and client innovation, which has been driving cutting-edge advancements and developments in AI through locations across the globe.

Companies looking to score early wins with gen AI should move quickly. But those hoping that gen AI offers a shortcut past the tough—and necessary—organizational surgery are likely to meet with disappointing results. Launching pilots is (relatively) easy; getting pilots to scale and create meaningful value is hard because they require a broad set of changes to the way work actually gets done.

Let’s briefly look at what this has meant for one Pacific region telecommunications company. The company hired a chief data and AI officer with a mandate to “enable the organization to create value with data and AI.” The chief data and AI officer worked with the business to develop the strategic vision and implement the road map for the use cases. After a scan of domains (that is, customer journeys or functions) and use case opportunities across the enterprise, leadership prioritized the home-servicing/maintenance domain to pilot and then scale as part of a larger sequencing of initiatives. They targeted, in particular, the development of a gen AI tool to help dispatchers and service operators better predict the types of calls and parts needed when servicing homes.

Leadership put in place cross-functional product teams with shared objectives and incentives to build the gen AI tool. As part of an effort to upskill the entire enterprise to better work with data and gen AI tools, they also set up a data and AI academy, which the dispatchers and service operators enrolled in as part of their training. To provide the technology and data underpinnings for gen AI, the chief data and AI officer also selected a large language model (LLM) and cloud provider that could meet the needs of the domain as well as serve other parts of the enterprise. The chief data and AI officer also oversaw the implementation of a data architecture so that the clean and reliable data (including service histories and inventory databases) needed to build the gen AI tool could be delivered quickly and responsibly.

Creating value beyond the hype

Let’s deliver on the promise of technology from strategy to scale.

Our book Rewired: The McKinsey Guide to Outcompeting in the Age of Digital and AI (Wiley, June 2023) provides a detailed manual on the six capabilities needed to deliver the kind of broad change that harnesses digital and AI technology. In this article, we will explore how to extend each of those capabilities to implement a successful gen AI program at scale. While recognizing that these are still early days and that there is much more to learn, our experience has shown that breaking open the gen AI opportunity requires companies to rewire how they work in the following ways.

Figure out where gen AI copilots can give you a real competitive advantage

The broad excitement around gen AI and its relative ease of use has led to a burst of experimentation across organizations. Most of these initiatives, however, won’t generate a competitive advantage. One bank, for example, bought tens of thousands of GitHub Copilot licenses, but since it didn’t have a clear sense of how to work with the technology, progress was slow. Another unfocused effort we often see is when companies move to incorporate gen AI into their customer service capabilities. Customer service is a commodity capability, not part of the core business, for most companies. While gen AI might help with productivity in such cases, it won’t create a competitive advantage.

To create competitive advantage, companies should first understand the difference between being a “taker” (a user of available tools, often via APIs and subscription services), a “shaper” (an integrator of available models with proprietary data), and a “maker” (a builder of LLMs). For now, the maker approach is too expensive for most companies, so the sweet spot for businesses is implementing a taker model for productivity improvements while building shaper applications for competitive advantage.

Much of gen AI’s near-term value is closely tied to its ability to help people do their current jobs better. In this way, gen AI tools act as copilots that work side by side with an employee, creating an initial block of code that a developer can adapt, for example, or drafting a requisition order for a new part that a maintenance worker in the field can review and submit (see sidebar “Copilot examples across three generative AI archetypes”). This means companies should be focusing on where copilot technology can have the biggest impact on their priority programs.

Copilot examples across three generative AI archetypes

• “Taker” copilots help real estate customers sift through property options and find the most promising one, write code for a developer, and summarize investor transcripts.
• “Shaper” copilots provide recommendations to sales reps for upselling customers by connecting generative AI tools to customer relationship management systems, financial systems, and customer behavior histories; create virtual assistants to personalize treatments for patients; and recommend solutions for maintenance workers based on historical data.
• “Maker” copilots are foundation models that lab scientists at pharmaceutical companies can use to find and test new and better drugs more quickly.

Some industrial companies, for example, have identified maintenance as a critical domain for their business. Reviewing maintenance reports and spending time with workers on the front lines can help determine where a gen AI copilot could make a big difference, such as in identifying issues with equipment failures quickly and early on. A gen AI copilot can also help identify root causes of truck breakdowns and recommend resolutions much more quickly than usual, as well as act as an ongoing source for best practices or standard operating procedures.

The challenge with copilots is figuring out how to generate revenue from increased productivity. In the case of customer service centers, for example, companies can stop recruiting new agents and use attrition to potentially achieve real financial gains. Defining the plans for how to generate revenue from the increased productivity up front, therefore, is crucial to capturing the value.

Upskill the talent you have but be clear about the gen-AI-specific skills you need

By now, most companies have a decent understanding of the technical gen AI skills they need, such as model fine-tuning, vector database administration, prompt engineering, and context engineering. In many cases, these are skills that you can train your existing workforce to develop. Those with existing AI and machine learning (ML) capabilities have a strong head start. Data engineers, for example, can learn multimodal processing and vector database management, MLOps (ML operations) engineers can extend their skills to LLMOps (LLM operations), and data scientists can develop prompt engineering, bias detection, and fine-tuning skills.

A sample of new generative AI skills needed

The following are examples of new skills needed for the successful deployment of generative AI tools:

• data scientist:
• prompt engineering
• in-context learning
• bias detection
• pattern identification
• reinforcement learning from human feedback
• hyperparameter/large language model fine-tuning; transfer learning
• data engineer:
• data wrangling and data warehousing
• data pipeline construction
• multimodal processing
• vector database management

The learning process can take two to three months to get to a decent level of competence because of the complexities in learning what various LLMs can and can’t do and how best to use them. The coders need to gain experience building software, testing, and validating answers, for example. It took one financial-services company three months to train its best data scientists to a high level of competence. While courses and documentation are available—many LLM providers have boot camps for developers—we have found that the most effective way to build capabilities at scale is through apprenticeship, training people to then train others, and building communities of practitioners. Rotating experts through teams to train others, scheduling regular sessions for people to share learnings, and hosting biweekly documentation review sessions are practices that have proven successful in building communities of practitioners (see sidebar “A sample of new generative AI skills needed”).

It’s important to bear in mind that successful gen AI skills are about more than coding proficiency. Our experience in developing our own gen AI platform, Lilli , showed us that the best gen AI technical talent has design skills to uncover where to focus solutions, contextual understanding to ensure the most relevant and high-quality answers are generated, collaboration skills to work well with knowledge experts (to test and validate answers and develop an appropriate curation approach), strong forensic skills to figure out causes of breakdowns (is the issue the data, the interpretation of the user’s intent, the quality of metadata on embeddings, or something else?), and anticipation skills to conceive of and plan for possible outcomes and to put the right kind of tracking into their code. A pure coder who doesn’t intrinsically have these skills may not be as useful a team member.

While current upskilling is largely based on a “learn on the job” approach, we see a rapid market emerging for people who have learned these skills over the past year. That skill growth is moving quickly. GitHub reported that developers were working on gen AI projects “in big numbers,” and that 65,000 public gen AI projects were created on its platform in 2023—a jump of almost 250 percent over the previous year. If your company is just starting its gen AI journey, you could consider hiring two or three senior engineers who have built a gen AI shaper product for their companies. This could greatly accelerate your efforts.

Form a centralized team to establish standards that enable responsible scaling

To ensure that all parts of the business can scale gen AI capabilities, centralizing competencies is a natural first move. The critical focus for this central team will be to develop and put in place protocols and standards to support scale, ensuring that teams can access models while also minimizing risk and containing costs. The team’s work could include, for example, procuring models and prescribing ways to access them, developing standards for data readiness, setting up approved prompt libraries, and allocating resources.

While developing Lilli, our team had its mind on scale when it created an open plug-in architecture and setting standards for how APIs should function and be built.  They developed standardized tooling and infrastructure where teams could securely experiment and access a GPT LLM , a gateway with preapproved APIs that teams could access, and a self-serve developer portal. Our goal is that this approach, over time, can help shift “Lilli as a product” (that a handful of teams use to build specific solutions) to “Lilli as a platform” (that teams across the enterprise can access to build other products).

For teams developing gen AI solutions, squad composition will be similar to AI teams but with data engineers and data scientists with gen AI experience and more contributors from risk management, compliance, and legal functions. The general idea of staffing squads with resources that are federated from the different expertise areas will not change, but the skill composition of a gen-AI-intensive squad will.

Set up the technology architecture to scale

Building a gen AI model is often relatively straightforward, but making it fully operational at scale is a different matter entirely. We’ve seen engineers build a basic chatbot in a week, but releasing a stable, accurate, and compliant version that scales can take four months. That’s why, our experience shows, the actual model costs may be less than 10 to 15 percent of the total costs of the solution.

Building for scale doesn’t mean building a new technology architecture. But it does mean focusing on a few core decisions that simplify and speed up processes without breaking the bank. Three such decisions stand out:

• Focus on reusing your technology. Reusing code can increase the development speed of gen AI use cases by 30 to 50 percent. One good approach is simply creating a source for approved tools, code, and components. A financial-services company, for example, created a library of production-grade tools, which had been approved by both the security and legal teams, and made them available in a library for teams to use. More important is taking the time to identify and build those capabilities that are common across the most priority use cases. The same financial-services company, for example, identified three components that could be reused for more than 100 identified use cases. By building those first, they were able to generate a significant portion of the code base for all the identified use cases—essentially giving every application a big head start.
• Focus the architecture on enabling efficient connections between gen AI models and internal systems. For gen AI models to work effectively in the shaper archetype, they need access to a business’s data and applications. Advances in integration and orchestration frameworks have significantly reduced the effort required to make those connections. But laying out what those integrations are and how to enable them is critical to ensure these models work efficiently and to avoid the complexity that creates technical debt  (the “tax” a company pays in terms of time and resources needed to redress existing technology issues). Chief information officers and chief technology officers can define reference architectures and integration standards for their organizations. Key elements should include a model hub, which contains trained and approved models that can be provisioned on demand; standard APIs that act as bridges connecting gen AI models to applications or data; and context management and caching, which speed up processing by providing models with relevant information from enterprise data sources.
• Build up your testing and quality assurance capabilities. Our own experience building Lilli taught us to prioritize testing over development. Our team invested in not only developing testing protocols for each stage of development but also aligning the entire team so that, for example, it was clear who specifically needed to sign off on each stage of the process. This slowed down initial development but sped up the overall delivery pace and quality by cutting back on errors and the time needed to fix mistakes.

Ensure data quality and focus on unstructured data to fuel your models

The ability of a business to generate and scale value from gen AI models will depend on how well it takes advantage of its own data. As with technology, targeted upgrades to existing data architecture  are needed to maximize the future strategic benefits of gen AI:

• Be targeted in ramping up your data quality and data augmentation efforts. While data quality has always been an important issue, the scale and scope of data that gen AI models can use—especially unstructured data—has made this issue much more consequential. For this reason, it’s critical to get the data foundations right, from clarifying decision rights to defining clear data processes to establishing taxonomies so models can access the data they need. The companies that do this well tie their data quality and augmentation efforts to the specific AI/gen AI application and use case—you don’t need this data foundation to extend to every corner of the enterprise. This could mean, for example, developing a new data repository for all equipment specifications and reported issues to better support maintenance copilot applications.
• Understand what value is locked into your unstructured data. Most organizations have traditionally focused their data efforts on structured data (values that can be organized in tables, such as prices and features). But the real value from LLMs comes from their ability to work with unstructured data (for example, PowerPoint slides, videos, and text). Companies can map out which unstructured data sources are most valuable and establish metadata tagging standards so models can process the data and teams can find what they need (tagging is particularly important to help companies remove data from models as well, if necessary). Be creative in thinking about data opportunities. Some companies, for example, are interviewing senior employees as they retire and feeding that captured institutional knowledge into an LLM to help improve their copilot performance.
• Optimize to lower costs at scale. There is often as much as a tenfold difference between what companies pay for data and what they could be paying if they optimized their data infrastructure and underlying costs. This issue often stems from companies scaling their proofs of concept without optimizing their data approach. Two costs generally stand out. One is storage costs arising from companies uploading terabytes of data into the cloud and wanting that data available 24/7. In practice, companies rarely need more than 10 percent of their data to have that level of availability, and accessing the rest over a 24- or 48-hour period is a much cheaper option. The other costs relate to computation with models that require on-call access to thousands of processors to run. This is especially the case when companies are building their own models (the maker archetype) but also when they are using pretrained models and running them with their own data and use cases (the shaper archetype). Companies could take a close look at how they can optimize computation costs on cloud platforms—for instance, putting some models in a queue to run when processors aren’t being used (such as when Americans go to bed and consumption of computing services like Netflix decreases) is a much cheaper option.

Build trust and reusability to drive adoption and scale

Because many people have concerns about gen AI, the bar on explaining how these tools work is much higher than for most solutions. People who use the tools want to know how they work, not just what they do. So it’s important to invest extra time and money to build trust by ensuring model accuracy and making it easy to check answers.

One insurance company, for example, created a gen AI tool to help manage claims. As part of the tool, it listed all the guardrails that had been put in place, and for each answer provided a link to the sentence or page of the relevant policy documents. The company also used an LLM to generate many variations of the same question to ensure answer consistency. These steps, among others, were critical to helping end users build trust in the tool.

Part of the training for maintenance teams using a gen AI tool should be to help them understand the limitations of models and how best to get the right answers. That includes teaching workers strategies to get to the best answer as fast as possible by starting with broad questions then narrowing them down. This provides the model with more context, and it also helps remove any bias of the people who might think they know the answer already. Having model interfaces that look and feel the same as existing tools also helps users feel less pressured to learn something new each time a new application is introduced.

Getting to scale means that businesses will need to stop building one-off solutions that are hard to use for other similar use cases. One global energy and materials company, for example, has established ease of reuse as a key requirement for all gen AI models, and has found in early iterations that 50 to 60 percent of its components can be reused. This means setting standards for developing gen AI assets (for example, prompts and context) that can be easily reused for other cases.

While many of the risk issues relating to gen AI are evolutions of discussions that were already brewing—for instance, data privacy, security, bias risk, job displacement, and intellectual property protection—gen AI has greatly expanded that risk landscape. Just 21 percent of companies reporting AI adoption say they have established policies governing employees’ use of gen AI technologies.

Similarly, a set of tests for AI/gen AI solutions should be established to demonstrate that data privacy, debiasing, and intellectual property protection are respected. Some organizations, in fact, are proposing to release models accompanied with documentation that details their performance characteristics. Documenting your decisions and rationales can be particularly helpful in conversations with regulators.

In some ways, this article is premature—so much is changing that we’ll likely have a profoundly different understanding of gen AI and its capabilities in a year’s time. But the core truths of finding value and driving change will still apply. How well companies have learned those lessons may largely determine how successful they’ll be in capturing that value.

The authors wish to thank Michael Chui, Juan Couto, Ben Ellencweig, Josh Gartner, Bryce Hall, Holger Harreis, Phil Hudelson, Suzana Iacob, Sid Kamath, Neerav Kingsland, Kitti Lakner, Robert Levin, Matej Macak, Lapo Mori, Alex Peluffo, Aldo Rosales, Erik Roth, Abdul Wahab Shaikh, and Stephen Xu for their contributions to this article.

This article was edited by Barr Seitz, an editorial director in the New York office.

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