The pressure is real for CEOs everywhere to put Generative AI at work in whatever way is possible in their business workflows.

Generative AI risks for businesses are a big concern. Yet, investors, stakeholders, peers, and partners want to use Generative AI to drive operational resilience and business continuity in a variety of ways.

82% of CEOs surveyed in a report by IBM in collaboration with Oxford Economics believe that the rate at which GenAI delivers business benefits could easily outperform its rate of potential risks.

Productivity gains and workforce agility can be two significant factors businesses want to advocate for this nascent technology and drive actual business results.

Despite demonstrating some outstanding abilities, for those who still believe GenAI is nothing but hype, OpenAI’s first developers’ conference, called DevDay could be a lever for them to realize the positive possibilities that Gen AI could create.

The developers’ conference brings a lot of promise to the front of enterprise businesses.

With the unveiling of GPT-4 Turbo, businesses can build new technologies more intuitively and efficiently for their existing processes much faster and gain profitable results. For example, companies can use GPT APIs to create custom AI agents in less than 4 minutes. This is a giant leap in how businesses can interact with their customers or employees and drive business growth.

With that, new product releases and version upgrades are part of continuous research and development to help discover more new opportunities with GenAI tools and technologies.

Stopping to play around with emergent technologies just because they can pose some risks can be equal to “give in before the game starts.”

Generative AI or GPT technologies are new in terms of their existence as a mainstream commodity.

This is where CEOs must act in a sensible manner. Instead of striking it off the priority list of AI projects, it is imperative to try it and explore mediums to use GenAI that can create opportunities for business growth.

Here is our guide to help you start your Generative AI journey.

As a CEO, you must first build a strategy when prioritizing how GenAI can be significant for your business use cases. To do this, you bind together people, processes, and technologies for ideas and the success of your initiative.

Explore Generative AI as a growth driver for competitiveness.

Driving business growth can directly be attributed to achieving faster time to market and cost-efficiency.

Let’s take an example of developing a share market app for a client. For a mobile development company, it is months or years of research and development that goes into MVP development, iterations of a fully featured framework, test and trial through agile development, and finally, celebrating the go-live event.

Generative AI's code generation capability can automatically reduce the time it takes for a complete featured app. Generative AI can help with

• Writing codes with prompts

• Testing codes

• Fixing bugs

• Accelerating the launch time

For your business, it has multimodal benefits.

You can cut development time and resource costs by reducing the time from years to just a few months, yet you can easily compete with rival companies.

Think about what is hard to do with Generative AI for competitiveness.

All integrated content generation tasks, including content translation and summarization, extend to a variety of departments to help them save time in writing something new correctly for more meaningful client communications and service delivery.

Your team escapes the unwavering pressure of creating impromptu business communications materials without looking too anxious and panicked.

Know opportunities for your existing business model.

Generative AI is an expensive iteration for AI projects. For CEOs, it is a huge responsibility to use a bottom-line budget in GenAI projects in a cost-effective way.

Also, it is imperative to balance between investment and ROI. However, the initial stages of experiments can hardly demonstrate any good returns on your investment.

The fruition of the project may be subject to time and how your people effectively leverage it for various business functions at your organization.

As you aim to cut costs at the primary stage of the decision-making, one or two options can look relatively cost-effective for you to implement Generative AI and gain a competitive edge.

• Find out if your existing processes or models are fit for adapting to Generative AI properties to automate workflows. For example, ERP, CRM, or any other enterprise workflow can be transformed for your people.

• Run a stakeholder meeting to decide if your existing business functions, such as supply chain operations, sales and marketing, and finance, need improvements in business process for speed and service delivery.

Identify areas for optimization and innovation.

Where are those PO orders? Which clients are following to be greeted for the upcoming festivity season? What is the progress on the user guide for client onboarding?

There are many tasks for a solution provider or product-based company to handle.

These are mundane yet essential to keep operations resilient and business running through robust client relationship management.

Missing out on any opportunities shifts the client’s focus on your competitors.

Take a closer look at various business functions you might think need a change.

Your team may need speed, and you can level up their skills with Generative AI solutions that can help them automate mundane work daily.

For say, they can easily automate the generation of various documents, speed up supply chain delivery, connect with customers, flag any anomalies, etc.

Generative AI Business Use Cases

Not every business is the same. What seems significant use cases for a global BPO company may be different for a company working in the manufacturing sector.

A product-based company may need a unique solution to resolve its problems.

Say, as a product-based company, it is a regular challenge for your team to find information for a development status; Generative AI can conveniently solve this.

Integrating with existing employee management systems or project management tools allows you to streamline workflows and let your people know what happens when and who’s taking care of which part of the project.

While you efficiently augment the pace of project management and client relationships, you can seamlessly automate customer support, employee support, and other areas of business operations.

The possibilities are endless with Generative AI.

The only significant consideration is to find opportunities across your business operations and implement Generative AI solutions for use cases.

When you spearhead a mission-critical project for organizational growth, it is critical that you set your eyes on every crucial aspect of operations that drives project success.

Work on your vision and strategy.

Generative AI is nascent, but it is evolving at a faster pace. Your outlook on implementing AI must be in line with how new additions of Generative AI can impact your operations and what way you can utilize them.

Note that the implementation process can have some changes during development, but the solution must have the same problem-solving objectives.

Be ready for unexpected changes in the iteration process because some process additions can raise maintenance costs and time.

Ensure data readiness and security.

To be able to roll out a custom Generative AI solution that works in sync with your business persona, you must ensure you have enough domain-specific data.

The off-the-shelf models can be a treasure trove of data for your use cases. Note that they are ubiquitous and can be used for general purposes only.

The best thing you can do is ask your data team to scrape data from disparate systems, store them for a business use case, and train your model.

In addition to collecting data, it is essential that you have a policy to educate your team about the sensible use of data.

A few instances demonstrated that teams got involved in passing confidential company data or personally identifiable information while experimenting with the model.

A strict policy can help them be aware of the possible threat to your company and customer relationship. As a result, they can demonstrate responsible behavior in using data and follow a secure way of handling data.

Build a cross-functional team.

It is not an individual’s sole game.

A GenAI implementation project needs contribution from the entire team.

As a CEO, you must bring key stakeholders from across every corner of your organization.

Reports suggest that customer support, sales, and marketing are critical business operations to benefit from Generative AI.

HR teams are pretty close. They are highly likely to see a transformation in their daily jobs.

Build a cross-functional team because they can suggest better ways for Generative AI to be implemented across their existing workflows and maximize them for ultimate benefits.

With that, you can also define specific responsibilities for each department for the project to come live and start functioning in full swing.

For example, an HR team can take responsibility to ensure that iteration may be delayed but is optimized appropriately.

On the other hand, your sales department can take care of the project's progress and communicate with stakeholders.

These teams can also become an essential resource for your AI project to gather data and help bring out a solution that helps your people work at scale.

Generative AI – which model looks good for you?

In its post, BCG has talked about a clear roadmap about how you can be strategic about investment in Generative AI.

Two efficient ways to consider your investment in large language models or Generative AI frameworks are –

• Open-source models to fine-tune

• Custom solutions with new models

There are some significant considerations as you aim to build on your use cases and desired goals.

• Implications with open-sourced models

Open-sourced models need a deep learning curve from highly skilled AI data specialists.

If you want to fine-tune off-the-shelf models, your AI teams must retrain themselves with new AI technologies, such as developers' tools and libraries, to work with a new model.

This option is less expensive than having to train a model from scratch. However, fine-tuning can work more with the proprietary data of the service provider combined with some of your company-owned data.

Workflow automation for employee or customer support remains as generic as other business functions in different industries, while data-sharing with third-party providers can pose a risk.

• Implications with custom models

Building a new model from scratch provides a high level of domain specificity and the flexibility of custom workflows for your specific business operations.

Say you are an electronic goods manufacturer. You have an operation to manage supply chains for distributors. To build robust supply chain visibility, your workflow automation differs from others.

In this case, you can build with data specific to your business, customer databases, and history of interactions.

It entirely lies on you to build your architecture, including LLM models, cloud infrastructure for computing, a great talent pool, time, and money.

This is something only a few can do with a massive amount of budget.

One more option is an API-led solution. It comes built with LLM architecture. The only requirement is to develop your data and train the model to deliver problem-solving solutions for your business-related issues.

Workativ Knowledge AI platform is an efficient model that allows you to harness the power of Generative AI and a large language model for employee and HR support.

Being a no-code platform, it is relatively inexpensive and easy to build custom solutions for a variety of business functions across HR and IT support.

After deciding what model option you want for your business use cases, the mandatory responsibility is reviewing how your AI initiative works.

KPI for Generative AI

To determine the success of your Generative AI model, know how your Generative AI model is working.

There are two essential metrics to gauge the efficacy of your model.

The first is to assess the model's performance based on its response time, accuracy, and problem-solving capacity.

Next is to determine if the model is delivering the value it is projected for.

• How fast does it solve a user’s problem?

• How much time does it take to respond to a user?

• How often does a user return to the help desk for a similar problem?

The evaluation is necessary since it helps find loopholes in the system and encourages you to find a way to employ improvements.

Impact on revenue and customer satisfaction

KPIs deliver more comprehensive details into the user satisfaction rate and revenue growth.

These two components are closely knitted to determine your project's success and your business's growth.

If employers become efficient and fast at what they do to solve business problems, leaders can streamline operations and accelerate service delivery.

With improved employee productivity and engagement, businesses can save time and drive cost efficiency for tasks that are rather more time-consuming.

Continuous monitoring and improvement

Most projects take off successfully and fail silently as well. Leaders need to take this into consideration. Continuous monitoring across the market to know what is coming up and what is essential to implement for employee flexibility is critical to ensure workflows are practical and efficient in solving employee problems.

As a CEO, ensure you have a robust monitoring mechanism to observe the market trends and a highly efficient team to implement these changes to your model.

Be vigilant and help your team to adapt and embrace change.

Generative AI is nothing less than a smartphone revolution right at this moment. Optimizing your business, considering it aptly aligns with the persona of Generative AI, provides massive potential to generate business revenues through enhanced employee engagement, productivity, and customer satisfaction improvement.

As a CEO, the road ahead to implementing Generative AI is challenging but possible. With a careful approach and strategy, your Generative AI project can become a success.

Our guide is a helpful lever to build a can-do mindset in you and help you see the positive side of this emergent tool to lever maximum benefits from it rather than just play with a ‘will do’ approach.

If you want to learn more about your feasible option to make use of Generative AI for enterprise workflows, Workativ can help.

Schedule a demo today.