Nearly nine in ten companies now use AI in at least one business function, according to McKinsey’s 2026 State of AI survey. So most companies have tried it. Far fewer have figured out how to make generative AI pull its weight in day-to-day operations.
Generative AI is the kind of AI that creates things. A chatbot drafting an email reply counts. So does a coding assistant finishing a function, or a tool that turns one product photo into ten versions for an ad. All of it comes from models trained on very large amounts of data.
In a business setting, the model at the center is usually a large language model (LLM), and that’s what most enterprise generative AI solutions are built on. There’s one problem with an off-the-shelf LLM, though. It has no idea what’s in your contracts or your price list. RAG (retrieval-augmented generation) gets around that. When an employee types a question, the system first finds the relevant pages in documents the company has approved, then gives those to the model so its answer is based on them. Once that’s wired into the ERP and CRM, and someone is monitoring the answers, the business can start trusting it. Without those parts, it’s really just a chatbot.
Plenty of company knowledge sits in email threads, PDFs, and call recordings. Nobody built a dashboard for any of that. Gen AI can read it and answer questions about it in plain English.
What do gen AI solutions for business look like day to day? Call summaries are a common starting point, along with pulling key fields out of contracts and forms. Developers use copilots to write and test code. Service reps get suggested replies while the customer is still talking. Some companies go further and let AI agents run an entire process, like checking an order or working through a claim, with a person reviewing the result. Sustainability teams are experimenting too, using gen AI sustainability consulting solutions to pull emissions figures out of scattered reports.
So where’s the payoff? Research from MIT’s NANDA initiative suggests it usually comes from unglamorous back-office automation, even though most gen AI budgets end up in sales and marketing. One Blackstraw client saw its analysis cycles get 40 to 60% faster after pulling its scattered LLM tools onto a single platform.
Anyone who has used a chatbot has seen it make something up with complete confidence. In a business setting that’s a real risk, and so is the chance of sensitive data slipping into an answer or someone steering the model off course with a cleverly worded prompt.
The fix is mostly discipline. Decide who can see what before launch, and keep the deployment inside your own cloud. Test it properly, then keep logs once it’s running. Cost deserves the same attention. One Blackstraw client trimmed its AI operating costs by 20 to 35% simply by tracking spend down to the individual prompt.
Start small. Choose one workflow your team runs constantly, like answering the same customer question or pulling together the same weekly report, and time it before you change anything. That’s the number you’ll measure the project against.
Then comes the build-or-buy question. In the MIT study, tools bought from specialist vendors or built with a partner worked about 67% of the time. Internal builds worked about a third as often (Fortune). Either way, run the pilot with real users and the same security you’d use in production. Let the managers who own the process lead the rollout, too. They know where the work actually gets stuck.
After launch, check whether people keep using it, and whether it’s saving time and cutting errors. Count what it costs to run as well, since inference and maintenance are part of the bill.
Blackstraw has been doing this kind of work since 2018. Based in Tampa, our 600+ engineers has completed over 200 deployments for more than 60 customers worldwide. Our gen AI and ML solutions cover content intelligence, agentic AI, and copilots, and they sit on top of solid data engineering backed by partnerships with Databricks, Microsoft, and Google Cloud. We keep our architectures cloud-agnostic so clients aren’t tied to one provider, and we use ready-made accelerators to get pilots into production faster.
Better models will keep arriving, but on their own they won’t close the gap between using AI and getting results from it. The companies that are seeing results did the less glamorous work first. They sorted out their data, fit the AI into how people already work, and they still check whether it’s helping.
Gen AI solutions are used to create and work with content at scale. Common enterprise uses include knowledge assistants that answer questions from internal documents, call and document analysis, code generation, customer service support, report drafting, and agentic automation of multi-step processes such as order handling or claims. The most valuable uses are usually tied to a specific workflow with a measurable outcome.
Generative AI solutions can reduce time spent searching for information, speed up repetitive document and communication work, and automate multi-step back-office processes. They also make data accessible to people who don’t write code. The business impact depends on choosing a well-defined workflow, preparing the underlying data, and integrating the solution into the systems employees already use.
Industries with large volumes of documents, customer interactions, or complex processes tend to benefit most. These include financial services (compliance, fraud, customer service), healthcare (clinical documentation, claims), retail and CPG (product content, customer support, supply chain), manufacturing (maintenance assistants, design, order processing), and energy (forecasting and reporting). Nearly every industry has viable back-office use cases.
Start with one high-volume workflow and measure its current performance. Prepare and govern the data the solution will use, then run a pilot with real users and production-grade security. Set evaluation and escalation rules before launch, give business owners responsibility for adoption, and reuse components such as retrieval pipelines and monitoring to scale to additional use cases.
Statistical generative models have existed for decades. Deep learning brought variational autoencoders in 2013 and generative adversarial networks soon after, enabling realistic images and speech. The transformer architecture, introduced in 2017, made large language models practical. Advances in cloud computing then made generative AI commercially viable at scale from around 2022 onward.
Traditional AI and machine learning models are mostly discriminative: they classify data, predict outcomes, or detect patterns, such as flagging a fraudulent transaction. Generative AI creates new content, such as text, code, images, or synthetic data, by learning how features in its training data relate. Many enterprise solutions combine both, using predictive models alongside generative ones.
Less than many expect. Foundation models are already pretrained, so most enterprise solutions don’t require training a model from scratch. What matters more is the quality and accessibility of your own content. Retrieval-augmented generation lets a model work from a well-organized, governed set of documents, which keeps data preparation costs manageable compared with full model training.
Retrieval-augmented generation (RAG) is a technique where a gen AI system searches approved company content for relevant information, then passes it to the model along with the user’s question. The model answers from that material instead of relying only on general training data. RAG improves accuracy, keeps answers current, allows source citations, and respects access controls.
Generative AI solutions can be highly secure when designed with security from the start. Key controls include deployment within the company’s own cloud environment, encryption, role-based access to retrieved data, PII masking, protection against prompt injection, and full logging of prompts and outputs. Continuous monitoring and regular evaluation help catch new risks after launch.
Costs vary widely based on scope, data readiness, integration complexity, and model choice. A focused pilot on a single workflow costs far less than an enterprise-wide agentic platform. Ongoing costs such as model inference, infrastructure, and monitoring also need to be budgeted. Most organizations start with a scoped pilot to validate value before committing to larger investments.
Measure performance before deployment, then track changes in efficiency (cycle time, hours saved), quality (error rates, accuracy), financial outcomes (cost per transaction, avoided spend), and adoption. Include full costs such as inference, infrastructure, and maintenance. Adoption and time savings usually appear first, with financial results following once the solution is embedded in daily work.
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