Executives are feeling top-down pressure to show real ROI from AI. We have interviewed dozens of companies and through these conversations we group what they are looking for into three buckets: IT efficiencies, business efficiencies, and business growth. Unfortunately, it’s not simply pointing your enterprise data at the latest model. You risk lack of context understanding, confidently wrong answers, and in turn lack of trust with these systems. For successful implementations, what matters is how you handle for reliability, what you do to ensure iterative improvement, how you manage change, and whether you show empathy as part of the adoption process. Success comes from a bottoms-up delivery approach that starts with understanding current workflows, capturing business rules and logic, and encoding those definitions into the system. This takes time sitting with users, but it is what keeps people, context, and governance at the center. When done at scale with proper instrumentation and telemetry, companies can build specialized models that solve high impact use cases in a self-serve way. Break problems into smaller chunks and leverage AI to do much of the “preparing to do the work” (data harmonizing, validating, creating views) and you drive value through more visibility, accelerated decision making, democratized access to information, pattern spotting, and proactive insights.
From a recent MIT study, we hear 95% of enterprise AI pilots do not stick. The pattern is consistent across industries. Teams start from technology rather than a high impact business case that moves a north star metric. They chase the newest models, point them at enterprise data, hope for an end to end workflow, and watch it fall short. The fix is not better models. It is a forward deployed approach that respects how change actually happens in organizations and treats model refinement and user adoption as continuous, not one-time events.
What forward deployed means and why it matters
Forward deployed means we sit with the customer and learn how work actually happens today. It is not a handoff. It is a partnership where we take a bottoms-up approach to understand your current workflows, capture the business rules and logic that govern decisions, and encode your institutional knowledge into the system so it speaks your language from day one. This takes time. We spend hours with the people who do the work to extract the definitions, thresholds, and edge cases that make answers trustworthy. This is the unlock that separates the 5% of successful pilots from the 95% that stall.
We start from seven principles that compound over time:
- Treat generative AI as a journey with visible checkpoints and learning loops.
- Start small and choose one high impact use case owned by a clear role.
- Build the data universe bottoms up around that use case rather than trying to model everything at once.
- Invest heavily in the semantic layer and contextual engineering because fidelity comes from captured business rules, not bigger models.
- Define KPIs and OKRs during scoping and capture a baseline so ROI is measurable, not theoretical.
- Bake change management into day zero because adoption is mostly about people, not technology.
- Be patient and persistent because the teams that follow these steps join the small set that realize needle moving returns.
The forward deployed framework in practice
We map the process end to end for the chosen use case. What data gets pulled, how is it cleaned and validated, and how are decisions made now. We sit with users to understand the current business workflows in detail. What triggers an analysis. Who gets involved. What thresholds matter. What constitutes an exception. This bottoms up discovery captures the business rules and logic that actually govern how your team operates.
We extract the unwritten metadata, the tribal knowledge that lives in people’s heads. How does your operations leader think about weeks cover. How does your finance team reconcile variances. Which thresholds trigger escalation. What does “on time” actually mean in your business versus the retailer’s definition. We encode all of this into prompts, rules, and the semantic layer. This work takes time, but it is how we ensure the system adheres to your definitions and produces answers you can trust.
We capture the KPIs and OKRs that matter to the specific team and tie model behavior to those outcomes. When users log in for the first time they see content that is familiar, actionable, and context aware. That builds trust. We do not hand over a blank canvas and expect teams to figure it out. We pre seed examples that reflect real business questions, offer guided suggestions that match how your team actually speaks, and model good questions. We train users in short sessions and keep a visible feedback loop.
We track telemetry to see who is getting value, who is stuck, and where questions drift outside the agreed scope. That signal tells us when to coach and when new data should be brought in. We stay scope disciplined yet curious. If question patterns point to adjacent data needs, we use a bring your own data workflow and refine the semantic layer. One successful use case becomes a portfolio of use cases because confidence and vocabulary compound.
The three ROI buckets that matter
When we interview leaders about AI ROI, the conversation always lands in the same three places. Each bucket delivers measurable returns that compound over time.
IT efficiencies mean consolidating tool sprawl and avoiding new headcount in analytics teams. Replace multiple point solutions with one end-to-end platform. Transform existing teams into power users instead of hiring specialized data analysts. Skip the lengthy implementation timelines and fees that come with traditional BI.
Business efficiencies mean freeing working capital, avoiding compliance penalties, and accelerating cash conversion. Better OTIF performance protects purchase orders and shelf space at major retailers. Unified visibility across inventory, orders, and shipments reduces days sales outstanding and excess stock. Better fill rates improve retail scorecards and prevent the cascading costs of stock outs.
Business growth means protecting key accounts, monetizing data with partners, and preventing disasters that come from operating blind. Provide secure access to performance metrics for suppliers and distributors. Better visibility prevents major retailer delisting, production line shutdowns, and inventory write-offs that can run into seven figures per event.
Traditional business intelligence projects promise all three but deliver slowly. By the time reports are ready, the business context has shifted. Generative BI with a forward deployed approach changes this math. Conservative first year returns range from $216,000-$350,000 in verified savings against an annual investment of $72,000-$96,000. That is a 3-5x return in year one with payback in Q1.
Why the default approach fails
Point a general purpose model at your data warehouse and ask it to answer supply chain questions and it will give you something. That something will lack the context of how your business actually speaks about inventory, how your team defines on time in full, and which edge cases break trust when the answer is wrong. There is missing metadata. There are naming inconsistencies. Your team needs guidance to learn what questions work and what the system can handle.
The core issue is not the models. Integration and adoption are the missing muscles. Several write ups of recent MIT research underline that workflow integration and change management sink most efforts, not model capability. Companies that purchase AI tools from specialized vendors and build real partnerships succeed about 67% of the time. Internal builds succeed only 33% as often. The difference is not technical depth. It is delivery approach.
The forward deployed framework
Forward deployed means we sit with the customer and learn how work actually happens today. It is not a handoff. It is a partnership where we encode your institutional knowledge into the system so it speaks your language from day one.
We start from seven principles that compound over time. First, treat generative AI as a journey with visible checkpoints and learning loops. Second, start small and choose one high impact use case owned by a clear role. Third, build the data universe bottom up around that use case rather than trying to model everything at once. Fourth, invest heavily in the semantic layer and contextual engineering because fidelity comes from captured business rules, not bigger models. Fifth, define KPIs and OKRs during scoping and capture a baseline so ROI is measurable, not theoretical. Sixth, bake change management into day zero because adoption is mostly about people, not technology. Seventh, be patient and persistent because the teams that follow these steps join the small set that realize needle moving returns.
What forward deployed means in practice
We map the process end to end for the chosen use case. What data gets pulled, how is it cleaned and validated, and how are decisions made now. We capture the KPIs and OKRs that matter to the specific team and tie model behavior to those outcomes. We extract the unwritten metadata, the tribal knowledge that lives in people’s heads. We encode it into prompts, rules, and the semantic layer so the model knows how your operations leader thinks about weeks cover, how your finance team reconciles variances, and which thresholds trigger escalation.
When users log in for the first time they see content that is familiar, actionable, and context aware. That builds trust. We do not hand over a blank canvas and expect teams to figure it out. We pre seed examples, offer guided suggestions, and model good questions. We train users in short sessions and keep a visible feedback loop.
We track telemetry to see who is getting value, who is stuck, and where questions drift outside the agreed scope. That signal tells us when to coach and when new data should be brought in. We stay scope disciplined yet curious. If question patterns point to adjacent data needs, we use a bring your own data workflow and refine the semantic layer. One successful use case becomes a portfolio of use cases because confidence and vocabulary compound.
A concrete CPG supply chain example
We worked with operations leaders in consumer packaged goods who live with stock out risk and compliance penalties across retail partners. When an event hits they need to know what happened, where, why, and what the knock on effects are at the warehouse and on shelf. They look at orders, shipments, store and DC inventory, lead times, planograms, promotions, and seasonal shifts. They ask which stores, which items, which routes, which vendors, and what bottlenecks caused the issue.
That work used to take too long. Data was scattered across the ERP, the WMS, retailer portals, and spreadsheets. People had to compile, join, validate, and then start analysis while the event was still unfolding. With our forward deployed approach, we built a workspace around this exact workflow. We connected the data sources, harmonized the naming, and taught the system the business logic for calculating fill rate, OTIF compliance, and days on hand using their exact definitions.
Specialized agents that understand operations use root cause frameworks like the five whys and the Pareto principle inside their skill library. The agent compiles disparate data, harmonizes it, builds the views that matter, validates the joins, and presents answers as analytic content that is easy to review. It proposes three or four risk scenarios with trade offs and asks follow up questions when context is missing. The team can weigh options while the event still matters. Better decisions, less waste, faster recovery. That is the promise when conversation is the interface and context is engineered for the specific use case.
What to ask vendors before you start
You can use AI even with messy or siloed data. There are clear paths to bring it together and harmonize it. If things are named inconsistently or data is scattered you can organize and make sense of it. Do not let messy data block you from getting started. The right approach handles this during implementation, not as a prerequisite.
Address the failure rate head on. Pick one high impact use case, set measurable success criteria, and publish time to value. Adoption is mostly change management. You need an internal champion who has patience and urgency.
Ask vendors about reliability. How do they test their models before handing it over. Do they invest in internal evaluations. Do they improve over time with additional context and metadata. Do they make this self serve and easy for customers to update themselves. Are they using the latest models or are they embracing a mixture of experts approach where depending on the problem they rank the best fit model on latency, performance, and cost. Are they honest that capturing business rules and unwritten metadata is the way fidelity improves, not just throwing more tokens at the problem.
How we approach it at Beye
We serve operations, supply chain, finance, and purchasing teams in mid market retail, manufacturing, and distribution. Our thesis is that an AI native approach to business intelligence, which we call Generative BI, can change how teams engage with data. Conversation becomes the interface. Business users get faster access to insight which supports more timely decisions and reduces the constant queue on central data teams.
We can set up a workspace in less than two weeks. Our proof of value period is one month total where we spend the bulk of the time fine tuning, educating, and guiding users on best practices for adoption. We go deep with the people who own the work. We sit with operations managers, planners, buyers, controllers, and supply chain leaders. When an event happens we map the follow on questions and the root cause analysis tree. We identify the data that is needed, who holds it, and the constraints and boundary conditions that shape the decision. We pay attention to the last mile problems and the edge cases that break trust.
Education and empathy are part of the product. In our proof of value we co design a fine tuned workspace for one clear use case. We bring the relevant data into one place and keep the history in one place. Our mixture of experts has a data model and metadata that match the use case. It knows the critical KPIs and exact definitions. We pre seed content to show what is possible with the data that exists. We offer suggestions in the flow so momentum starts on day one.
Beye also watches question patterns. When it detects a gap it asks its own question and posts it to the working channel for approve or deny. We keep the end user in the loop so the workflow stays meaningful and purposeful. The goal is simple. Shorten the distance between a question and a trustworthy answer inside the work.
Where Beye is going next: proactive behavior
Proactive insights have been a promise in business intelligence for years. The promise has consistently fallen short. The technology and our approach now make it possible to deliver for customers.
This only works if we do our job well in partnership with you. By building the foundation on instrumentation and context aware models tied to specific business cases and success criteria, agents can display proactive and prescriptive behavior.
Our approach focuses on high impact use cases. With this foundation we have the context of the data we have connected, the goals and OKRs that matter, the north star metrics the team is tracking, and the questions and answers history from business teams. With this information and context we can inform our AI on continuous improvement and enable goal directed proactive behavior. As new data is brought into a customer workspace, the system shares opportunities that are relevant and contextualized to what will move the needle. It uncovers patterns, anomalies, and thresholds being breached and feeds you this intelligence without having to prompt.
How to get started
This correction in the market filters out fantastical claims and rewards teams that deliver reliability on specific workflows. It pushes us to keep language simple, keep outcomes clear, and measure progress with real numbers. That is how we speak about Beye and how we build.
We provide consultative guidance to help you identify high impact use cases and approach them the right way. We work with you to understand which parts of your workflow can be leveraged with AI and how to do it reliably. We can scope and deliver a use case end to end in just a few weeks. We help you frame the IT efficiencies, the business efficiencies, and the business growth potential with Generative BI so your executive team sees the path to measurable ROI across all three buckets.
Why wait? The gap between the teams that move fast and those that stall is widening. The forward deployed approach works. We have proven it with operations, supply chain, finance, and purchasing teams across mid market retail, manufacturing, and distribution.
Book time with us for a 30-minute discovery session where we will walk through your high impact use case and show you exactly how the forward deployed framework applies to your business.
Book your discovery call here
Or reach out directly: sales@beye.ai
