9 Practical Ways Enterprise AI Can Identify High-Value Usage Cases

The Enterprise AI Productivity Gap

According to research from McKinsey & Company, generative artificial intelligence has the potential to generate between $2.6 trillion and $4.4 trillion annually in economic value across global enterprise use cases.

In the Fortune 2000, the engineers and innovation leaders have to contend with a depressing reality where hundreds of pilots are locked into sandboxes and development environments forever. 

Millions are pumped into enterprise budgets in the form of compute credits and talent for the creation of chatbots that simply summarize meetings and write company emails.

According to research carried out by organizations like Gartner and RAND Corporation, over 70% to 80% of enterprise AI initiatives do not move past the PoC phase.

The common denominator for this constant problem is not the inefficiency of the algorithms themselves. The frontier foundation models possess outstanding multimodal reasoning capabilities, code generation ability, and contextual synthesis skills.

What usually goes wrong is when enterprises launch initiatives way too early without being able to find the right high-value AI use cases for their unique operational problems.

If executives decide to see AI as a marketing opportunity and not a means for operational success, then the company's experiments will be inefficient and will fail the capital efficiency test.

To create sustainable competitive advantage, tech leaders have to substitute their brainstorming with a well-defined discovery process.

The purpose of this guide is to show nine tried-and-true methodologies of finding the right high-value AI use cases.

Why Most Enterprise AI Pilots Fail: The "Solution Looking for a Problem" Trap

In order to understand where business value lies, one must consider why there is such a high number of enterprise AI projects that end up stalled out in practice.

Without the proper identification of the most valuable applications of AI, it is virtually certain that technology-first experimentation will ensue in search of a problem to be solved.

A common example of this starts off in the executive offices. An executive sees a presentation at an industry event with a conversationally capable vendor solution.

They then instruct their technology team to roll out their own solution in the form of a conversational AI system that they hope will generate results in under three months' time.

This leads to a retrieval-augmentation generation (RAG) system being built off of unfiltered internal documents that start giving contradictory answers and triggering compliance violations in short order.

The technology stalls out in "pilot purgatory."

Pilots in purgatory arise from team confusion between technical novelty and operational value.

A model that produces a quarterly memo or poetic verse in rhyme is technically sophisticated, yet has little to no operational utility if the same memo would take only two minutes for a person to read.

A process which automates the reconciliation of international customs declarations and shipping manifests can save your company from demurrage fees and compliance risks and reduce order fulfillment cycles.

AI is not a business strategy; it is an enabling optimization technology.

Business strategies do not start by calculating parameter counts or the context window of new foundation models.

9 Practical Ways Enterprises Can Identify High-Value AI Use Cases

1. Deconstruct Value Streams to Isolate Asymmetric Bottlenecks

Every big organization works through connected value streams – the necessary processes involved in moving a product, service, or transaction from its initial demand until it reaches its final delivery.

Embedded in these value streams lie particular operational bottlenecks, where the transactions remain stagnant due to manual interventions.

One of the best operational methods to find out high-value AI use cases is by doing an end-to-end audit of your most utilized cost centers.

Look at “swivel chair” handoffs. These are operational handoffs that happen whenever an employee extracts the unstructured data from an inbound stream, such as an email discussion thread, a contract document scan, insurance claim inspection, or a regulatory document, and manually enters the extracted data in any enterprise system of record like SAP, Workday, or Salesforce.

Think about how freight logistics works around the world.

Large companies with hundreds of bills of lading to process per month find discrepancies on a regular basis between the manifest and the customs filing.

It is impossible to expect logistics specialists to compare line item discrepancies from different databases manually.

Consider three main factors when assessing your workflow maps:

Cycle Latency: Which processes produce the largest delay from customer triggering to completion?

Cognitive Overhead: Do your most highly paid technical and/or legal experts find themselves doing repetitive document classification on the clock?

Surge Susceptibility: Does your ability to process transactions break down at your quarterly closes or seasonal peaks because you are limited by human keying?

The act of requiring expert personnel to devote large amounts of time merely to parsing and re-entering unstructured text is a prime example of automation opportunity.

2. Calculate the Unit Economics: The "Labor Multiplier" vs. Direct Cost Model

Unconvincing references to "increased workforce productivity" or "faster digital transformation" are not going to cut it for executive finance committees.

The calculation of an ROI of AI is imperative when trying to find high ROI applications of AI that pass the test of the CFO.

The conventional enterprise software operates on a relatively consistent cost structure consisting of upfront licensing fees, setup costs, and maintenance plans.

Large language models and sophisticated machine learning models have a marginal cost structure, as each API call, contextualization query, and token generation takes compute power.

Any probabilistic system requires evaluation and maintenance.

To see if the deployment is going to have positive net economic returns, analyze projects using an economic framework:

NetBusinessValue = (TaskVelocity x LoadedLaborRate) - (InferenceCost + MaintenanceOverhead + Human-in-the-Loop Review)

where:

TaskVelocity is the number of documented hours that are stripped out of the total workflow cycle.

LoadedLaborRate is the complete financial cost per hour of professionals performing the task.

InferenceCost involves tracking the consumption of tokens, vector database hosting, and GPUs per transaction.

MaintenanceOverhead includes prompt engineering, artificial benchmarks, and re-indexing of pipelines along with data updates.

Human-in-the-Loop Review covers the cost of verification of the output by humans based on risk tolerance.

Use this equation before assigning engineering resources.

If the internal assistant is saving an administrative assistant ten minutes every day in the morning but demands cloud computing capabilities, custom middleware, and constant monitoring, which will cost tens of dollars per user per day, then your unit economics are in the red.

On the other hand, consider a workflow around analyzing commercial real estate leases.

If you have a smart review tool which allows a high-level corporate counsel making $180 per hour to review tenant covenants in 30 minutes instead of four hours, with negligible inference cost per lease, then your bottom line margin per professional will definitely turn out to be quite sizable.

Focus on applications which offer maximum economic gap between loaded professional hourly rate and computing cost.

3. Audit Your Data Foundation for "Proprietary Moats"

One common mistake when developing an enterprise AI roadmap is spending capital on applications which rely on commoditization of publicly available information.

In case the information necessary for development of your application is publicly available on the Internet, then it becomes easy for your competition to replicate the same using developer APIs.

A proper evaluation of your data sources will help you understand the scenarios in which AI can be applied effectively to have a competitive moat.

What will make your business application valuable is the exclusivity and quality of the proprietary data you have.

Assess your data foundation against the three key criteria:

Data Exclusiveness: Do you have years' worth of proprietary operational data that can be found only within your organization?

Some examples may include telemetry from equipment sensors for many years, unique underwriting results, proprietary engineering failures log, and extensive technical support databases about complex integration problems.

Data Integrity and Structure: Is there a possibility to ask and parse this data using automated pipelines without additional manual cleansing?

Untagged, duplicate and contradicting sources will hamper the efficiency of any retrieval-augmented generation model.

Data Governance and Availability: Can the applications integrate this data through up-to-date API with granular permissions, or do you keep it locked away inside outdated on-premise storages, impossible to export?

Using a universal conversational agent to answer policy questions for your employees doesn't provide any competitive advantage for you.

Implementing an intelligence layer over decades of your historical claims data, litigations and adjusters' opinions allows your insurance company to make underwriting recommendations that commercial off-the-shelf models cannot.

4. Separate Full Automation from Cognitive Augmentation

Many visionary enterprise projects grind to a halt due to the assumption of senior leadership that the AI solution will be a success only when it becomes totally autonomous and removes any human involvement in the process.

The difference between performing tasks autonomously and augmenting cognition helps technical executives to recognize valuable AI application areas without running into critical failures in some edge cases.

In order to find out what architecture needs to be applied, the classification of workflows needs to be performed based on:

Autonomous Execution: The architecture is appropriate for processes characterized by low operational ambiguity and minimal consequence of an error, or processes for which programmatic validation rules can validate the output immediately.

Some examples of such processes would be sorting support requests based on their products, identifying dates and amounts from vendor receipts, or searching system logs for common error strings.

Small errors are easy to detect and correct using existing validation rules.

Cognitive Augmentation (Human-in-the-Loop Approach): This architecture will be necessary in cases where there is the possibility of a hallucination or a computational mistake having serious implications in terms of money or compliance.

Examples include analysis of clinical trials, audits for regulatory compliance, portfolio risk modeling and analysis of contracts.

In such scenarios, it should not be the role of the model to be the ultimate decision-maker but rather to be an enabler in terms of retrieving regulatory requirements, providing a first draft and showing discrepancies to a human expert.

For assessing business opportunities, start with cognitive augmentation. The quest for absolute autonomy in critical processes entails edge case testing and defensive mechanisms that could lead to indefinite postponement of implementation.

By adopting the strategy of having your intelligent tools assist human experts, your firm eliminates edge cases of failure while maximizing efficiency.

5. Mine Customer-Facing Touchpoints for Friction and Churn Latency

Decreasing operational costs is one part of the solution; revenue velocity and churn prevention are the other part.

When analyzing the effort ratings of customers, CSAT scores, NPS responses, and support ticketing, frictional workflows that pose an immediate risk to pipeline conversions or retention are identified very rapidly.

Think about the complex sales cycle where it may be taking place in a B2B context, for example in industrial manufacturing or sales of IT equipment.

For most companies, preparing the right proposal may involve technical analysis by sales engineers, availability checking using ERP system, regional freight calculations, and customer discount calculations, and this is likely going to take days.

Published studies in Harvard Business Review on lead response management show that time to respond is a crucial variable in competitive sales; vendors who can provide quick and detailed information on pricing and specifications win deals easily.

An intelligent proposal engine which creates accurate proposals using product catalogs and enterprise pricing logic within minutes is critical in retaining sales momentum.

Other high-impact frontline workflows include:

Complex Case Triage: Using AI for automatic summarization of multi-year customer ticket history and system logs so that support engineers within the enterprise can identify the root cause without having the client explain the problem again.

Onboarding Validation: For accelerating know-your-customer (KYC) documentation and corporation validation for banks and financial services, thereby turning months-long backlog into a fast and efficient validation process.

Churn Prevention: Analysis of customer usage data of the products and tickets sentiments so as to identify enterprise clients who show early signs of disengagement.

An AI project which results in direct impact on sales cycle reduction or protection of high-revenue recurring business easily gains executive support.

6. Deploy a 2x2 "Feasibility vs. Strategic Impact" Scoring Matrix

Organizational politics can often influence the decision making process regarding technology investment decisions within corporations.

A loud voice from a specific division’s manager with a significant budget can push for specific generative capabilities even if the general benefit to the business will be minimal.

By using an objective evaluation process, it becomes easy to discover valuable applications of AI which will avoid politics and individual bias.

Create an evaluation team composed of enterprise architects, finance managers, security experts, and other executives.

Assess all the proposals on two major criteria:

Business Impact (1–10)

Revenue Acceleration: Does the project open up new revenue streams or make the sales process faster?

Operating Expenses Reduction: Does the project lower costs of processing or makes operating scale independent from linear growth of headcount?

Defensible Competitive Advantage: Does the project create a proprietary operational process which is not replicable by competitors through acquisition of commercial software packages?

Risk and Compliance Avoidance: Is this system capable of eliminating all risk due to human error in highly-compliant regulatory environments?

Technical Feasibility (Scale 1-10)

Data Readiness: Is all the necessary enterprise data ready to go, clean and compliant?

Maturity of Architecture: Does the requirement require an approach that is yet to be researched or does it fit into already proven foundation models and architectures?

Integration Overhead: How easy will it be to integrate the application with already existing ERP, CRM and Identity Management Systems?

Scope of Security & Regulation: Is there any element of personal or financial information that would fall under regulation?

Regulatory Concerns: Is there any PII or financial or critical infrastructure involved in the project?

The classification of these projects into the matrix provides for four clearly defined operational groups:

Strategic Bets (High Impact, High Feasibility): These are the high-priority initiatives that tackle important operational problems by means of available enterprise data. Invest senior management in technology and funding in these initiatives.

Tactical Wins (Moderate Impact, High Feasibility): Low complexity projects that can be rapidly deployed via standard APIs. These initiatives prove their value to the company’s management, train employees to integrate models and create momentum in the organization.

Research Initiatives (High Impact, Low Feasibility): Ambitious initiatives that have encountered various obstacles in terms of technical, data or regulatory problems. 

Such initiatives should be contained to R&D sandboxes, where their evaluation checkpoints should be clearly defined. Avoid linking the annual objectives of the operation to their completion.

Low Value Distractions (Low Impact, Low Feasibility): Speculative initiatives and experiments. Do not include such initiatives in your roadmap.

7. Uncover "Shadow AI" to Detect Organic Enterprise Demand

Your knowledge workers will likely not be awaiting the rollout of official AI strategies from corporate IT.

Legal, financial, marketing, engineering, and customer service people will be using personal commercial services to script, summarize, draft, and analyze.

Known colloquially as “Shadow AI,” this situation is usually considered by corporate information security departments only as a data governance issue.

It is true that using unmonitored commercial services to process corporate data can be an issue of compliance; it is also the most real demand signal you can have in your organization.

Watching how your employees are using the tools gives management a true idea of where high-value AI applications with validated internal demand can be found.

Your people use those tools because their current corporate workflows are manual and fragmented.

Instead of creating just punitive policies that will only make things worse, do a proper discovery exercise:

Surveys of Workflow Processes: Survey departments about the extent of their use of external cognitive technologies for addressing their administrative burdens.

Challenges to Internal Innovation: Organize workshops within the company where various operational teams share prompts, frameworks, and workflow processes they’ve created to help with their routine processes.

Analysis of Network Gateway Logs: Analyze network gateway logs within the enterprise for the business units that are consistently sending traffic to commercial model endpoints.

If analysts in the finance department are constantly using external models for reformatting data extracts and writing preliminary commentary for the board, then they've proven genuine operational needs exist.

This is when the duty of the enterprise leadership comes into play – creating and implementing a secure and auditable internal solution for protecting sensitive data while making sure the team benefits from an operational process that they've already proven to work.

8. Screen for Governance, Regulatory, and Reputational Risk

An AI project that could possibly save the company $2 million in processing fees but poses a risk to the company with regard to regulatory, intellectual property, and reputational risks cannot be said to be valuable.

Creating proper governance check points upfront makes sure that only those AI use cases with significant value for the business that pass the legal and security audits can be found.

With the introduction of the EU AI Act and regulations on industry level, such as the ones by the SEC and HHS, any use case for AI must undergo thorough risk evaluation before development starts:

Data Provenance & Confidentiality: Is the proposed integration sending any confidential business documents or code to an external endpoint? Make sure that all vendor agreements do not allow for the storage, logging, and training of models using your payload data in any way.

Algorithmic Explainability & Audibility: Is it possible for the system to track its input to a specific source document?

For regulated processes such as deciding whether a person is eligible for a loan, calculating the right insurance payout, or evaluating a personnel decision, the organization needs to have the ability to produce the specific document chain used in making this decision.

Systems acting as black boxes in a regulated area are compliance risks.

Consequences of Hallucination: What is the consequence of making a factual error?

A mistake that causes the cosmetic wording error would mean little operational risk.

A mistake that changes a legal liability clause, gives wrong medical advice, or calculates the structural engineering parameter incorrectly cannot be done without verification.

Fairness & Data Integrity: Has there been any legacy bias in the operational data?

If a particular idea does not live up to your requirements for risk management/governance, you should not go ahead with the implementation of the idea because you will think that you will be able to solve these issues at a later date.

9. Run Rapid "Minimum Viable Experiments" (MVEs) Before Capital Deployment

The old school approach of historical enterprise procurement does not apply to today's machine learning efforts.

It takes nine months to specify the requirements, six months to evaluate the enterprise software options, and one year to implement an on-premises solution, which virtually guarantees that the end result will be out of date by the time it is completed.

Conducting well-defined minimum viable experiments provides your team with the data to find valuable use cases for AI applications prior to making any big investments.

Minimum viable experiments are not undefined research experiments that run endlessly.

They are well-specified technical experiments that take place in two to four weeks.

Every MVE should be based on three operating principles:

Success Metrics: No ambiguous success metrics such as "model quality evaluation." Create explicit operational KPIs: "The system should extract twelve crucial commercial covenant fields from unstructured leases of real estate with F1 score higher than 0.93 in less than ten seconds per document."

Time-Bound Gates: Should the engineering team fail to reach the baseline accuracy threshold in four weeks using state-of-the-art foundation models and state-of-the-art retrieval pipelines, kill the project.

Such a result means either that the enterprise data foundation needs to be modernized or that the task complexity is out of reach for existing models.

No Complex Custom Engineering: For an experimental phase do not invest into complex custom engineering architecture.

Use managed vector services, state-of-the-art evaluation pipelines, and state-of-the-art models API to test your hypothesis.

Save the custom engineering efforts for the workflows that have proven their empirical viability.

Treat the enterprise AI adoption process as the portfolio of fast and cheap experiments to protect your balance sheet from costly capital errors.

Overcoming Structural Implementation Roadblocks

Even in cases where a systematic approach to discovery is taken by companies, enterprise structure obstacles can still affect execution.

Experienced technology executives know of these potential problems and design measures to address them into the plans of their projects.

1. Data Fragmentation and Technical Debt

Data fragmentation is the problem that large companies face rather than data scarcity.

Business context is often locked up in many different legacy ERP systems, disconnected relational databases in-house and document storage without indexing.

Without recent context, the reliability of the system will be seriously affected.

Mitigation Strategy: Don’t wait until the completion of a multi-year company-wide data modernization effort before deploying your AI projects.

On the contrary, create dedicated and accessible via APIs data layers specifically designed for your strategic priorities.

2. Organizational Inertia and Cultural Resistance

Digital transformation often fails among middle management and line-level workers.

Where employees see artificial intelligence as a threat to their job security or paychecks, there will be resistance to implementation of the system.

Employees can point out edge cases of the model and avoid the use of new technology, while continuing to rely on old methods.

Mitigation in Operations: Incentivize departments to use the augmented technologies directly.

The program should not be seen as a headcount cutback program, but as an expansion capacity program to get rid of repetitive processes.

Reward teams that leverage cognitive technologies for better outputs, and train paths for augmented staff to perform advisory roles.

3. Cross-Functional Silos

An enterprise AI initiative needs tight coordination between experts who normally don't act in sync: data engineers, cybersecurity experts, compliance experts, and business unit heads with operations responsibility.

Machine learning experts develop systems without operational input, and those systems fail to meet operational demands.

Operational Solution: Create multi-disciplinary deployment pods for each strategic initiative.

The deployment pod would have the following roles: an engineering head, a data governance head, a product manager, and an operations expert.

The Enterprise AI Discovery Checklist

Identifiable Bottleneck – Is there an identified workflow bottleneck where knowledge workers are spending substantial effort manually reading, verifying, and entering unstructured information?

Viable Unit Economics – Is the anticipated net value of the business proposition positive, taking into account compute costs for tokens, API fees, constant prompt evaluation, and professional review?

Proprietary Moat – Does the app make use of proprietary, properly governed internal data assets which competitors cannot replicate using open market models?

Augmentation Architecture – Is the design such that it keeps the human experts in charge of high-stakes ambiguous judgments, reducing liability and risk?

Empirical Validation – Has the core working hypothesis been empirically validated with a bounded, four-week minimum viable prototype that passed quantitative accuracy thresholds?

If a proposed project passes all five tests, then your company has left speculation behind and discovered an initiative that can deliver balance sheet results.

Accelerating Your Enterprise AI Roadmap with Gigmint.AI

From discovery to deployment necessitates the filling of the deployment pods discussed earlier with top engineering talent—a bottleneck for enterprise organizations that balance the day-to-day maintenance of architecture.

And that is why we have Gigmint.AI. We offer enterprise organizations with access to the technical talent, architecture, and engineering capabilities needed to quickly develop and prove ROI initiatives.

Gigmint.AI provides you with everything you need to make your digital transformation strategy actionable – the strategic framework, elite technical skills, and engineering execution.

Whether your organization needs to undergo enterprise-wide readiness assessment, architect retrieval-augmented generation infrastructure over proprietary enterprise-level databases, or implement fast-running autonomous agents, Gigmint.AI makes it possible to do so in an efficient manner.

Applying those strategic frameworks gives you a chance to keep on identifying AI-driven use cases which bring you long-term competitive advantages, guard your profit margins, and have an impact on your bottom line.

Contact Gigmint.AI to set up an executive discovery call now!