A Little Less Conversation, A Little More Action: How to Accelerate Generative AI Deployment in the Next 6 Months

Enough daydreaming, enough speculation, enough hype – this is a year of action. According to the McKinsey Global Institute, nearly 50% of typical business activities can now be automated by generative AI (GenAI), a type of artificial intelligence that can produce text, images, video, and synthetic data.

This automation drives tremendous value and solves critical business challenges across industries and functions, enhancing customer experiences, optimizing operations, and spurring innovation. But, for the most part, GenAI has not been pressure tested on a large scale, and the true ROI on these investments needs to be clarified.

While companies have begun to invest heavily in experimental and ad-hoc GenAI projects, scaling these efforts can be a complicated endeavor. Leaders are grappling with how to maximize GenAI benefits while observing and minimizing costs, ensuring auditability and access controls, improving performance, providing model abstractions and strengthening security. Those who have been hesitant to embrace GenAI up to this point for fear of the high overhead and data governance/security concerns should consider the following as they build GenAI into their workflows and larger business strategies.

Create a plan of measured transformation: 3 key actions to take right now

1. Upskill your workforce to harness GenAI’s full potential in a risk-mitigated manner.

It’s a brave new world in artificial intelligence, and there are varying levels of understanding about what’s possible. Companies just starting on this journey can benefit by running organizational programs to train both IT and business teams on the potential of GenAI, developing specific protocols around risk, transparency, and ethics.

Organizations can choose whether to bring in outside expertise or create a new role dedicated to AI ethics, but they should understand that the training is not for show. Dedicating days or weeks to programming that coaches all employees (not only those in technical roles) on how to use GenAI will see better organization-wide buy-in than those who don’t.

By educating business teams on identifying potential GenAI applications that can help them in their respective work (and separating fact from fiction around security concerns) organizations will be in a much better position to assess total value.

2. Fuse AI with GenAI: Ready your infrastructure for data-heavy changes

GenAI is quickly acquiring attention for its ability to drive productivity, pushing operation margins to previously unseen levels. However, it is important to remember that GenAI is no silver bullet. With the rise of GenAI, traditional data engineering practices and AI have become more important than ever.

Consider the following GenAI-powered solutions:

  1. Retail: Driving hyper-personalization in retail using autonomous agents to generate recommendations.
  2. Travel: Using a GenAI-infused workflow to create personalized travel itineraries based on individual preferences.
  3. Banking: Use of conversation agents to personalize banking from bill payments to spending trend analysis and recommendations.

GenAI alone isn’t sufficient to power the solutions mentioned above. It is critical to bind the natural language understanding and reasoning capability of GenAI with the proven accuracy and efficiency of traditional AI.

For example, hyper-personalization can be achieved with greater consistency if we use traditional machine learning algorithms to generate a bouquet of recommendations and use GenAI-powered agents to reason which of them will be most relevant to the user.

As such, it is critical to look at GenAI, traditional AI and data engineering practices in cohesion, with a single prism, rather than in isolation. This makes it exceedingly important for organizations to provide infrastructure to merge AI development with GenAI solutions.

3. Build your GenAI readiness: Scale, innovate, control

It’s smart to be proactive, but transformation doesn’t happen overnight. By identifying the imperative “must haves” for the organization, you can stagger your development timeline based on critical needs.

Then, designate a group of internal leaders to fast-track the awareness and adoption of a GenAI Operating System—a platform that provides auditability, cost controls and chargebacks, security, privacy, access control and model abstractions—to onboard GenAI applications and processes using this platform. This will help innovation at speed and scale by ensuring rapid iterations of GenAI use cases by focusing primarily on functionality, and thus increase buy-in across the organization.

In retail, according to a recent IBM study ahead of NRF 2024, modern customers expect a tailored shopping journey, complete with “the convenience of product choices, detailed information, diverse payment methods, and a seamless integration of in-store and online experiences” that cater to their individual preferences.

To meet these expectations, retailers need to organize and democratize access to their data so that business functions from R&D to sales to marketing are working from the same home base. Without a clear view of the data or a plan to implement it cross-functionally, organizations can overinvest in AI-powered solutions and see little ROI. Retailers unsure how to maximize their existing data should turn to a partner with deep industry experience to establish an AI-ready infrastructure. Only then, can they take advantage of GenAI to streamline customer service with less human intervention by providing conversation summaries, automating tasks, and ultimately driving conversion—a key priority for the industry.

Further, retailers are experimenting with the idea of dynamic product descriptions. Reliant on AI, e-commerce listings could change based on the viewer, tailored to the unique wants and needs of each customer. A strong team, underpinned by a level of GenAI readiness will be well-equipped to capitalize on these AI technologies ahead of competitors.

Identify transformative GenAI use cases & offer quantifiable business outcomes.

Oftentimes, in a rush to show progress, companies can start sprinting without a direction in mind. Rather than expend that energy going after everything at once, take note of specific use cases that can be completed within 3-6 months, 6-12 months, etc. Prioritize those short-term projects first to demonstrate the value of running GenAI at scale and then, for areas that have potential, focus on building platforms that can showcase the benefits of GenAI to other departments. Areas like model training, autonomous agents, and private LLMs hold huge potential for future innovation, and strategic investment in those areas now will give you a head start on your competition.

In banking, applying for a loan for medium and large enterprises requires an analysis of a lot of documents including the company’s bank statements, audit reports, tax returns, credit bureau reports and recent news. All of this must be processed manually to prepare an approval memo. Automating this process via, GenAI, not only saves quantifiable costs but the speed in reduction of overall TAT can be a competitive advantage and differentiator that can help generate new business.

With GenAI, the banking sector among others is poised to remove stress and provide additional visibility to customers with relatively low effort and up-time. While there are many more singular use cases of GenAI in play, making it to the next phase of a GenAI-powered business requires replicating and operationalizing the technology across the enterprise to infuse it into the overall business strategy.

Don’t procrastinate, it’s time to wake up to AI delivery

Overcoming implementation challenges and implementing GenAI at scale is no small feat. It takes total alignment from the board and C-suite and a commitment from business leaders across the organization. To move past the fear of missing out on AI and begin to create profit-driving, AI-powered tools, educate your teams on what’s to come, establish an infrastructure that can sustain rapid change, and focus on the short-term outcomes that matter to your clients and partners.

As you transform, it’s important to bring on expert hires or outside counsel you can trust to help ensure a smooth transition. Look for those that are action-oriented (i.e. builders, not just advisors) and bring leadership into the decision process early on to increase transparency and foster collaboration. GenAI capabilities are evolving rapidly, and by acting now you’ll be on your way to creating a future-ready organization poised for sustainable growth.

The post A Little Less Conversation, A Little More Action: How to Accelerate Generative AI Deployment in the Next 6 Months appeared first on Unite.AI.

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