TLDR: Enterprise AI innovation can fuel your company’s growth and efficiency, but companies face challenges evaluating the range of evolving AI technologies and their opportunities. Here’s how a digital solutions partner can help you identify the highest-impact use cases and manage risk.

Leaders are already seeing how generative AI can dramatically increase organizations’ productivity and growth potential. Engineers are completing tasks in less than half the expected time with the help of tools like GitHub’s Copilot; with ChatGPT, data teams can get code for dataset visualizations in seconds. The use cases for enterprise AI innovation are nearly endless, as teams are beginning to discover.

But that doesn’t mean that the adoption of generative AI technology will be easy or its outcomes guaranteed. To see the transformations possible with this groundbreaking technology, companies should carefully develop an adoption strategy in line with their business requirements, security needs, and opportunities for innovation in their market.

Making the leap with next-generation AI technologies

First, let’s make an important distinction: AI isn’t new. Machine learning is an advanced practice, and many companies have been leveraging this capability for years.

Generative AI and the large language model (LLM) platform launched by OpenAI, however, brings wider accessibility and signals the potential of next-generation enterprise AI technology.

Like predictive AI, generative AI systems are trained on large datasets to provide insights or produce results. But generative AI goes beyond predictive capabilities, creating content based on data in its ecosystem and user prompts. LLM is similarly powerful, using language-based content from its massive training datasets to predict and generate responses, interacting with users via text.

That potential still comes with risks, especially as the technology continues to evolve for commercialization. From employees who query ChatGPT with propriety company information to prompts that end in obviously false information, enterprises may be eyeing generative AI with a mix of enthusiasm and caution.

Some leaders may be waiting to see the technology mature to the point where a few key pillars are in place — but they’ll need to balance the waiting game with a proactive approach, continually assessing the opportunities and risks of adoption. And eventually, they’ll need to strategically make the leap or risk falling behind innovative competitors actively leveraging enterprise AI’s transformative capabilities.

Pillars of enterprise AI adoption

Enterprises will need to have a high degree of confidence in generative AI — and they’ll need the technical know-how to ensure that AI systems are effectively designed and optimized in a couple of key areas.

Leveraging innovation partnerships to power ahead with enterprise AI 

As leaders look to the future of generative AI and LLM for their organizations, they’re coming up against challenges. Whether it’s experimenting with brand new technologies and sorting use cases, the task of transforming with AI is proving a tall order — and one that leaders must respond to as quickly as possible to capitalize on the value potential. 

With the constant pace of new development, it may be essential to support your own AI experimentation with an innovation partner, complementing the power of your teams with AI expertise. From rapid proof of concept development to exploring technologies, learn more about how an innovation partner can guide you in implementing next-generation AI.

Evaluating AI technologies & third-party partners

From ChatGPT to the growing range of generative AI-powered productivity tools coming to the market, companies have to sort through all the brand-new AI technology, assessing data privacy features and risk potential as it rapidly evolves and comes online.

While OpenAI and the GPT models are in the headlines now, the AI space is quickly changing and expanding. Other platforms and providers may now or soon be offering solutions better suited to unique business needs.

Along with a supporting innovation team, companies will want to evaluate AI technology features like:

  • Data privacy and control. As mentioned, data privacy is a crucial risk factor as companies deploy AI-powered software. Consider that different AI technologies and providers have different data privacy and data control structures. For instance, users add input and data to OpenAI’s LLM — and for now, that means company data could become part of the OpenAI data environment. But other large language model providers allow companies to add the model to their data, granting greater control over data privacy.
  • Third-party data sources. Companies may need to leverage third-party data sources as part of their enterprise AI systems, which can add to the complexity involved in finding and vetting technologies.

Identifying the right AI technology solutions for your business requires careful analysis and planning. And it may be an equal risk to delay the process, waiting until new generative AI technology is packaged with enterprise software and comes to market down the line.

Expert digital partners can guide you across all stages of enterprise AI exploration, starting with the technical analysis — and help you start innovating sooner rather than later.

Identifying highest-impact use cases 

Waiting too long to explore the potential of generative AI will leave companies at risk of falling behind disruptive competition. But that doesn’t mean that enterprise AI will be a solve for just any business case, especially at the outset.

Strategically exploring enterprise AI early on for targeted, high-performing, and high-impact use cases will have transformative potential for organizations. While dozens of use cases are on the table, your innovation partner can help you determine the low-hanging fruit and the highest-impact opportunities for your business.

Here are a few common use cases companies and their enterprise AI innovation partners are likely to look at first.

Financial services use cases

enterprise AI innovation in  financial services

ChatGPT is only the first major signal of what’s possible for generative AI technologies. While GPT is trained on general data from across the web, companies will begin to leverage enterprise AI tech trained on industry-specific datasets, powering even greater potential for use cases and innovation within verticals.

From banking to capital markets to payments, financial services organizations have the potential to build on the growing capabilities of AI by leveraging the next generation of AI advancements.

1. Payments automation & optimization

Companies no longer need to rely on manual analysis for many of their costliest payments challenges. With AI-powered analytics evaluating transaction data or automating operations, card companies can optimize payments processing — from transaction decisioning to chargeback review and prevention.

3. Risk management

Similarly, financial institutions have been leveraging machine learning and AI to make better, data-driven decisions and manage risk faster. Combined with AI and LLM technologies, your teams can automate risk monitoring data along with other data sources from across lines of business.

Use cases include merger and acquisition and due diligence, where AI-powered risk monitoring can make recommendations for process optimization and document review.

4. Personalized marketing & customer retention

Customer service is an important factor for brand loyalty among 96% of consumers — and customer service can be transformed and optimized with LLM solutions.1

From cardholder microtargeting to advanced customer retention analytics to highly engaging chatbot experiences, companies have significant potential to innovate the customer journey with the latest AI. Of course, companies will also need to work through the challenges where AI systems interface with customers, like developing bias prevention in product recommendations.

5. Portfolio management & deal sourcing

Managing portfolios and evaluating acquisition potential involves analyzing vast amounts of data, and that’s where companies can leverage automation. Identify trends in performance data and synthesize analytics with event data or other third-party data sources to make data-driven decisions.

6. Compliance and ESG automation

From PCI DSS to KYC standards, finserv organizations have to ensure compliance with a range of ever-evolving security standards. On top of it all, organizations must meet their own and industry ethics standards.

From auditing to monitoring regulatory changes, AI can ensure you’re in compliance and make suggestions for the best actions.

2. Fraud prevention

Fraud is expensive, with every dollar of fraudulent activity resulting in over $4 in recovery and related expenses on average — and those costs are only rising.2 Companies are already automating fraud monitoring with the combined power of big data and artificial intelligence, identifying anomalies and trends in transaction data to stop fraud in real-time.

With the latest AI technologies, like adaptive AI, your AI systems can continuously learn from and improve on their fraud and risk monitoring capabilities.

Life sciences use cases 

Across life sciences, next-generation enterprise AI will transform everything from clinical trial data and design to the personalization of medicine.

1. Drug discovery

Over 30% of drug discovery will be driven by generative AI technologies by 2025.3 Research shows that the cost of bringing a drug to market ranges from $314 million to $2.8 billion.

2. Synthetic data

As in finserv, patient data privacy is also a significant concern in research and development settings, where exposing medical records can pose risks. Generative can help to produce synthetic data for healthcare and life sciences applications, ensuring minimal concerns around data privacy.

3. Precision medicine

With the power of next-generation AI to learn from large patient datasets, identify trends, and make personalized predictions or recommendations, precision medicine can see significant potential with the latest AI technologies.

Implementing effective design and guardrails 

Generative AI can drive game-changing innovation across your organization. But companies will also need help with developing and implementing the tech effectively, including in the following areas.

Rapidly building & launching prototypes

Ideating and imagining the possibilities of AI is only the beginning. Your in-house innovation team may need a lab of developers who can realize the vision — fast.

AI experts can design, develop, test, and implement custom AI solutions. Whether you need to explore solutions, experiment with design concepts, or rapidly prototype software, a digital partner can help you to quickly scale and deliver innovative enterprise AI technologies.

Build your enterprise AI roadmap with Blankfactor

Exploring and building AI-powered solutions should be done strategically with a technical team that understands your business, security needs, and how to leverage generative AI technologies to produce the results you want.

At Blankfactor, we can help companies lead the way with enterprise AI. Our digital innovation experts have the depth of experience you need to ensure that your data is secure, compliant, and your AI systems drive impact. From financial services to life sciences, our team is ready to engineer the next-generation enterprise AI innovation you need to pull ahead.

Ready to brainstorm your enterprise AI roadmap? Our industry experts and experienced technical consultants can strategize custom solutions tailored to your business in just an hour. Contact our team today for your 60-minute solution session.