Misalignment between LLM selection and business objectives
A lot of companies are going all in on large language models (LLMs) without fully understanding what they’re solving for. Technology on its own isn’t a solution. It’s a tool. If you pick an LLM just because it ranks high in benchmarks or looks promising in a demo, and you skip matching it to what your business actually needs, you’ll burn time, money, and internal credibility.
This kind of misalignment is more common than you think. According to Beatriz Sanz Saiz, EY’s Global AI Sector Leader, one of the top mistakes organizations make is failing to align LLM selection with business outcomes. That means getting caught up in marketing hype instead of grounding decisions in practical business cases. It’s like buying a jet when you just needed a car. Saiz also points out the hidden complexity, integrating these systems into existing infrastructure isn’t plug-and-play. That catches teams off guard and delays delivery.
Maitreya Natu, a Senior Scientist at Digitate, said it best: companies often choose a model first, then retrofit a use case to it. That’s backwards. Start with the problem. What bottleneck are you trying to eliminate? What decisions would be smarter, faster, or more scalable with an LLM? If the answers aren’t clear, maybe you’re not ready yet.
Naveen Kumar Ramakrishna from Dell Technologies highlights a similar trend, LLMs get slapped into projects because they’re popular, not because they’re needed. Sometimes simpler solutions, like a rule-based system or small machine learning model, would’ve done the job better. Teams go big for no real reason and end up in a pit of costs, delays, and questionable performance.
If you don’t match the tool to the task, you’ll run into usability issues, miss expectations, and in many cases never even get to production. Trust in your AI roadmap takes a hit, and getting future projects greenlit becomes a challenge. So, be strategic, not reactive.
The importance of a well-defined problem and measurable outcomes
Before you even think about model selection, clarify the problem. Sounds obvious, but it’s the step companies skip the most. This should be a working session with technical leads, business owners, and end-users in the room, define the friction, the current workflow, and what success looks like. No vague optimization goals. Real metrics tied to throughput, accuracy, response time, or cost.
Naveen Kumar Ramakrishna from Dell says it clearly: without a solid understanding of the problem, you’re building for the wrong targets. The result? Over-investing in huge models for small tasks or deploying generic models where specificity is needed. Either way, there’s waste.
Max Belov, CTO at Coherent Solutions, adds another important layer, identify what kind of task this actually is. Is it a chatbot? Document summarization? Code generation? Different LLMs excel at different things. Models like GPT-4 or Claude v3.5 Sonnet offer strong general performance. Others, like Meta’s LLaMA or Google’s Gemma, are open-source and suited for fine-tuning. Figure that out early so your model fits your actual load and domain.
Setting measurable outcomes keeps everything grounded. That’s how you avoid scope creep. And once you hit your first milestone, you’ve built the case for scale with real data, not theoretical ROI. This is what C-level teams want, evidence, not enthusiasm.
Once the use case is clear, validate your ideas in the real world. Run a few pilot tests. Evaluate usability and see how well the model adapts to edge cases and scaling conditions. You’ll uncover issues early, before you’re deep into contracts, integrations, or sunk infrastructure.
Executives are expected to manage risk and deliver impact. Getting your LLM investment right starts with the clarity to say exactly what problem is being solved and how performance will be measured. Everything else depends on that.
Evaluating total cost of ownership and scalability
Total cost of ownership (TCO) goes way beyond license fees or API access. Running large language models can scale fast on compute consumption, bandwidth usage, and infrastructure dependencies. If you don’t assess long-term implications early, what starts out as an exciting AI project can become an unsustainable drag on resources.
Naveen Kumar Ramakrishna from Dell Technologies stated that a year ago, anyone could access their internally hosted models freely. Now? Access is limited. Why? Rising traffic and mounting compute costs. It’s a clear warning signal. Most companies underestimate runtime expenses, particularly token costs, inference latency, and data load implications. They think the heavy lifting ends after fine-tuning, but that’s exactly when real costs begin to scale.
Ken Ringdahl, CTO at Emburse, put it bluntly: understanding your use case growth patterns is key. Test models under pressure. Don’t assume you’ll use the same model the same way six months from now. Every request made to an LLM costs compute. For larger models, that jumps significantly. If your usage spikes unexpectedly, because of internal adoption or user demand, costs will follow. Without rate controls or architectural planning, you lose control.
Also, performance problems don’t just affect users, they compound infrastructure costs. If results aren’t accurate, users re-query. That drives up transactions and increases system load without actually increasing output value. Token waste, latency, and compute inefficiency can become recurring liabilities.
Executives need to require cost modeling upfront. Understand idle vs. peak loads, redundancy scenarios, and compute utilization patterns. Choose the smallest model that satisfies the use case, and build in constraints early. LLMs deliver value when they’re right-sized, not just powerful.
The need for customization and fine-tuning of off-the-shelf models
Off-the-shelf LLMs look promising on the surface, but most business environments aren’t plug-and-play. The way your teams work, the terminology they use, the documents they process, none of that is generic. Which means that general-purpose models often underdeliver when placed inside real-world workflows without some level of adaptation.
Maitreya Natu at Digitate pointed out this core mistake: businesses use generic models for domain-specific problems and then need to manually fix what should have been automated in the first place. That negates any productivity gain and drives up operational costs. General models can support prototyping and ideation, but high-reliability use cases, especially in regulated or technical industries, require better alignment through fine-tuning or model refinement.
Max Belov, CTO at Coherent Solutions, breaks this down further. Some models are optimized for conversations, like virtual assistants or chatbots. Others perform better when used for summarizing technical documents, generating software code, or handling multimodal tasks. You need to map skill set to function. Without this, you may have a technically advanced model that performs poorly in context.
Customization also means integrating proprietary data, or prompts engineered to mirror your actual operation. That takes prep work. You need clear workflows, use-case scripts, and labeled training sets. Once deployed, these models must be monitored, tweaked, and continuously assessed for alignment.
From a leadership perspective, the takeaway is simple: don’t treat LLMs like commodity software. Evaluate where off-the-shelf ends and customization begins. Make tuning part of your implementation timeline, budget accordingly, and assign teams that can translate business logic into language data. This is where efficiency gains turn into competitive advantage.
Validation through pilot testing and stakeholder engagement
If you’re serious about deploying LLMs, validation through real-world testing isn’t optional, it’s operationally necessary. Teams need more than technical performance on paper. They need to know how the model behaves under stress, with real inputs, inside their specific environment.
Beatriz Sanz Saiz from EY emphasizes the value of running controlled pilot programs. Test a few selected models with targeted use cases. Measure output relevance, latency, adaptability, and failure handling. This process reveals early how the model responds to complexity, scale, and non-standard behavior. It also highlights if model performance aligns with your workload structure and business requirements.
Engaging stakeholders from across business units during this stage helps surface user expectations early. Sales, compliance, engineering, operations, each of these teams brings unique context. If the model’s responses lack domain depth or clarity, you’ll hear about it immediately. That feedback loop isn’t noise, it’s precision.
Ken Ringdahl at Emburse also makes this point tangible. He advises testing specific prompting techniques, like zero-shot, one-shot, or few-shot types, across multiple LLMs. The goal is to see which model consistently performs well under real use conditions. Individuals scattered across departments will interact with the model differently. You need to know which version delivers consistent accuracy at scale.
From a C-suite level, this boils down to risk containment. Pilot testing creates space for failure without massive cost. You gain performance data, friction insights, and credibility with internal stakeholders. If you launch into production without this phase, you invite expensive surprises. If you validate properly, you establish a defensible rollout strategy backed by evidence.
The advantage of Domain-Specific Language Models (DSLMs)
If your business operates in a specialized field, insurance, forensic accounting, healthcare, or legal, among others, you should be seriously evaluating DSLMs. Domain-Specific Language Models are built for precision in specialized environments. They understand the structure, terminology, and expected outcomes in ways that general-purpose models simply don’t.
Beatriz Sanz Saiz from EY notes that DSLMs are becoming more common in sectors like indirect tax and insurance underwriting. Why? Because when accuracy and efficiency are non-negotiable, DSLMs deliver operationally. A generalist model might understand context at a surface level, but a DSLM trained on industry-specific datasets delivers higher certainty, less ambiguity, and reduced rework.
Specialization doesn’t mean giving up flexibility. Many DSLMs are built to support modular training. That means integrating your proprietary data across process-specific tasks, risk scoring, claims classification, compliance interpretation, becomes more seamless. You control performance evolution by training on materials that matter to your vertical.
This kind of model also accelerates internal adoption. When results mirror expectations and language usage matches the operating environment, trust in the system scales faster. Project leads become advocates instead of skeptics. Efficiency gains become visible without constant human intervention.
Executives thinking about long-term ROI need to ask whether domain precision justifies upgrading from a general model. For most mid-to-large enterprises operating in complex industries, it does. The delta between 85% general accuracy and 96% contextual relevance can translate into significant business impact. The closer the model gets to how your business speaks and thinks, the more it will actually deliver.
Integration, data quality, and compatibility as critical factors
LLMs don’t deliver value in isolation, they work inside systems. Too many organizations underestimate what it takes to integrate a language model into production systems, workflows, and platforms. That’s where projects break down.
Naveen Kumar Ramakrishna from Dell Technologies has seen it first-hand. Integration challenges, platform dependencies, and unaccounted data issues don’t just slow projects down, they can send them into indefinite limbo. Many organizations don’t fully understand their own data pipelines or how their information flows through systems. When mismatches in data structure, quality, or scale are discovered late, it creates rework that’s expensive and time-consuming.
Teams also tend to assume they can deploy on any environment, on-prem, hybrid, public cloud, without constraints. But the reality is, LLMs interact with infrastructure differently. Incompatibilities affect performance, latency, and cost. You need to evaluate the hosting environment early with the same scrutiny you apply to the model itself.
Data quality is another blind spot. If your model is trained or fine-tuned on noisy or inconsistent internal data, accuracy will degrade quickly. Inconsistent labeling, gaps in historical records, or improper formatting will compromise outcome reliability. That means downstream teams will have to verify or manually adjust output, killing any productivity gains.
From a C-suite perspective, integration and data quality aren’t technical afterthoughts, they’re critical to adoption. Ensure your teams map the current data ecosystem and validate compatibility across the entire stack, including APIs, orchestration tools, and monitoring infrastructure. The goal isn’t just to get an LLM working, it’s to ensure it adds value without breaking your processes.
Ensuring security and data privacy in LLM deployments
Security and data privacy aren’t features, they’re requirements. If you’re deploying LLMs without understanding how your sensitive data is stored, processed, or trained on, you’re opening up operational and reputational risk.
Max Belov, CTO at Coherent Solutions, emphasizes that on-premises or private cloud deployments offer the highest levels of control. For organizations handling regulated data or operating under strict compliance frameworks, this isn’t optional. It’s the baseline. Cloud-based models can be highly performant and scalable, but depending on the vendor, data residency, retention, and access policies may be unclear or difficult to enforce.
Naveen Kumar Ramakrishna from Dell makes the strategic case: data privacy must drive the architecture. This decision impacts everything from vendor selection to internal policy alignment. If you don’t want sensitive data leaving your environment, structure your implementation accordingly.
Ken Ringdahl at Emburse advises that any external model should be reviewed with extreme scrutiny. Token usage, customer inputs, logs, even prompts, can contain private information. Before data is sent off-platform, sensitive details need to be sanitized or redacted, especially in industries like financial services, law, or healthcare. Don’t assume the platform provider is keeping you compliant, validate everything.
What’s often missed at the executive level is that privacy missteps don’t just result in penalties. They erode confidence and slow adoption. The model might function, but if stakeholders don’t trust its data handling, they’ll avoid it. Build privacy into your design, through environment strategy, vendor review, internal protocols, and training.
Security isn’t checked at the end. Start with privacy goals, structure around them, and deploy models that reinforce trust as they scale.
Embracing open-source and scalable, cost-efficient solutions
You don’t need the largest or most hyped model on the market to create value. What you need is a language model that fits your business, adapts over time, keeps your costs in check, and gives you control. That’s exactly where open-source and smaller-scale LLMs come in.
Naveen Kumar Ramakrishna from Dell Technologies recommends starting small, deploying the simplest model that handles the task effectively, and scaling intelligently based on what delivers measurable outcomes. A smaller model, if properly trained or tuned with relevant data, can outperform a larger model that isn’t optimized for your domain or task type. It will also cost significantly less to operate and integrate.
Open-source LLMs, like Mistral, Meta’s LLaMA series, or Google’s Gemma, give you full control over deployment. You avoid vendor lock-in, which reduces risk when commercial pricing or terms change. You also have the flexibility to tune and host models on your infrastructure, in the cloud, on-prem, or hybrid. That’s vital if your data strategy depends on control and compliance.
Max Belov, CTO at Coherent Solutions, notes that some of the most business-ready platforms now offer models that are highly customizable, come with advanced compliance features, and integrate easily into private cloud setups. These open frameworks allow teams to fine-tune performance, scale usage with demand, and embed LLMs inside enterprise applications with minimal disruption. That means reduced latency, greater responsiveness, and higher confidence from internal stakeholders.
This route also encourages a more iterative mindset. You can validate performance in real workloads, prove business value, and then allocate more compute or expand use cases as needed. You don’t make an oversized commitment based on market perception, you build deployment based on system realities.
For executive teams, especially those managing costs and ROI expectations, open-source and right-sized models are strategic options. They align better with long-term budget cycles, IT governance, and operational resilience. The decision here is about control, not compromise. Choose the model that fits your infrastructure, data, and growth goals, not just the one trending online.
Final thoughts
LLMs aren’t magic, they’re systems. If you want them to deliver, you need to treat them like any other strategic asset. That means aligning them with actual business goals, building around your own infrastructure and data realities, and making decisions based on outcomes, not hype.
The real cost of poor planning isn’t just wasted compute. It’s failed adoption, stalled innovation, and loss of internal trust. Executives don’t have to be experts in transformers or training loops. But they do need to own the decision-making process, define the problems, validate the use cases, and demand clarity from their teams.
Start small. Stay focused. Look for traction early, then scale with discipline. The companies that get this right aren’t the ones with the biggest models. They’re the ones making the smartest decisions with what they have.
