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The truth is that relatively few organizations have yet to establish enterprise-wide AI implementations. In fact, almost half of companies surveyed by CompTIA as recently as October of last year, said they were still in the exploratory phase with AI, and a third are implementing AI in only a limited fashion.
However, according to a recent Harvard Business Review survey, those organizations that are focused on building their AI and digital capabilities are significantly outperforming their competitors in profitability, market share, and customer satisfaction. In fact, in another recent study, Accenture found that while only 12% of companies could claim to have achieved AI maturity, those “AI Achievers” credit AI for nearly 30% of their total revenue.
One thing that most of these leading organizations have in common is that they’re not treating AI like it is the solution. It’s a tool in an organization’s approach to generating solutions. And Generative AI specifically is a highly valuable tool that, when strategically applied, can help organizations solve their most pressing business challenges.
From Promise to Practical
While Generative AI continues to become increasingly powerful and more useful with every new update and release, right now at G2O, we see the most practical uses for AI falling into squarely one of three categories: optimizing costs, accelerating human innovation, and informing decisions.
OPTIMIZING COSTS
From automating repeatable tasks to generating code that enables the migration of data, AI is proving valuable in improving efficiencies and fueling increases in productivity.
For example, while working with a national homebuilder, our team here at G2O leveraged the power of AI to write automated tests and conduct QA on code for the launch of a new buyer portal. Our application of generative AI reduced coding time by nearly 40%. Plus, in addition to saving time, these AI-generated solutions helped our team rapidly identify altogether new test cases and scenarios which led to an improvement in overall code and application quality.
ACCELERATING HUMAN INNOVATION
Yes, you can prompt generative AI tools to create some preliminary lists of ideas that get the ball rolling or help a team get “unstuck,” but our team has found that the most effective way AI can increase creativity and innovation is by freeing up more time for the team to be just that – creative and innovative.
By using AI as a production tool, our team members can move their focus from pixels to purpose. But AI can also help speed the process of defining that purpose by leveraging generative AI before, during, or after a brainstorming session.
Our Service Design team recently used this approach when helping a banking client with an ongoing CX challenge.
- First, the team used AI to collect and compile market trends, customer signals, and industry-wide challenges to augment their existing qualitative research findings – all to help the team better prepare for a project brainstorm.
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After the brainstorm, the team used AI to organize the ideas that were generated during the session, quickly combine concepts in novel ways, and identify patterns the team could build upon to come up with the proposed solution.
One member of the team described the benefits of the process this way: “The AI tools we leveraged produced multiple novel perspectives, which provided more inspiration for more ideas. And quantity of ideas whittled down to quality of ideas is what leads to innovation.”
In addition, generative AI helps our team develop proofs of concept with greater speed, enabling them to either fail fast or realize they’re really onto something in far less time than previously possible.
INFORMING DECISIONS
Even though generative AI tools are not yet capable of truly finding insights that haven’t been found before, AI can mine through piles of data and discover patterns to help humans uncover insights far more efficiently than what could be done manually.
In fact, leading brands and organizations around the globe are using the power of AI to help themselves and their customers more quickly make better informed decisions.
- Wells Fargo AI assistant app, Fargo has handled 20+ million interactions since launching in March of 2024 and is on pace to handle 100 million interactions per year. Additionally, the bank is using its AI-powered Livesync app to provide financial wellness advice to its customers and in its first month it logged a million active users.
- Zillow is using AI to estimate the value of properties, provide personalized recommendations, and more to inform potential homebuyers as they make the high-stress decision of buying a home.
- Morgan Stanley’s AI assistant supports financial advisors quickly accessing and gleaning insights from more than 100,000 research reports and other documents.
However, no matter how helpful generative AI is and continues to become, for the foreseeable future, we cannot trust AI to make subjective decisions. A human still needs to be in the loop – making, or at least approving – any critically important decisions.
AI Challenges and Temptations
The market is saturated with Large Language Models and companies launching new generative AI platforms. In fact, one of the most common delays in companies embracing AI is the paradox of choice – there are so many options that they can’t decide where to start.
And the temptation is to chase the newest, most-hyped tool and use it simply so you can say your solution is AI-powered. However, at G2O, we recommend starting with the business challenge, and then investigating if, where, and how AI could and should be part of the solution. Our team helps our clients understand the risks and compliance areas to consider, get smart about what A.I. tools are available and which are most appropriate for their business and use cases, and assess where your organization is in the A.I. lifecycle.
Immediate Applications of Generative AI
While AI is a powerful tool with almost unlimited potential, it can’t do everything… yet.
But what can AI do for you right now?
- Driving Development Efficiency. Using AI, teams can create standardized code structures that free up developers to concentrate on the truly important programmatic issues. Similarly, AI can empower core level programmers to solve issues on their own that would typically require the involvement of senior-level team members. Functioning like an AI tutor or instructor, this is done through plain English speaking prompts used to describe solutions to programming issues and document existing code.
- Converting Legacy Code. AI can enable development teams to more quickly convert identified legacy code to align with new requirements, platforms, technologies, or efficiency expectations. And by converting this code, using best-in-class tools such as Copilot and Cursor, the team is eliminating time-consuming and expensive tech debt.
- Migrating Data. While migrating data is often mentioned as a valuable use for AI, AI doesn’t actually migrate data. It simply accelerates many of the steps involved in migrating data. For example, teams can use AI to classify and catalog data sources, tables, columns, and other data assets based on their descriptions, metadata, and content – facilitating data discovery and lineage tracking, and also ensuring proper handling of sensitive data. Additionally, developers can feed all data quality rules into AI, enabling the programs to detect anomalies and help with data-cleansing tasks that improve the overall data quality of the migrated data sets.
- Speeding Internal Processes. Whether converting meeting transcripts to actionable notes or creating résumé summaries, there’s an almost endless number of business functions that could be sped up with the use of AI. These tasks still require a person to review and revise, but the time savings is enough to significantly increase the productivity and efficiency of individuals and teams.
We Are Not an AI Company
After reading this article, you might assume we’re trying to convince you that at G2O, we’re an AI company. In actuality, we’re not. We’re not an AI company working to create a unique platform and trying to sell it to you or develop our own attempt at an LLM powered, proprietary tool.
Instead, we see ourselves as an AI enablement partner.
Our team is focused on helping our clients optimize the potential of existing generative AI tools as part of a comprehensive solution. We’re helping our client capitalize on the power of AI to develop custom applications, address unique business challenges, and ensure they’re achieving their desired outcomes. As we said in the beginning, AI is a tool – an extremely powerful one – and our goal is to help our clients get the most out of this tool that they possibly can.
To discuss how our cross-functional team can customize a generative AI solution specific to your unique business challenges, please reach out. We’d love to talk.