Presented by Veritone
A recent trend of AI task forces has been spurred on by the growing interest in the potential of generative AI. Contrary to the looming fear of AI, McKinsey aptly describes generative AI as an empowerment tool for the global workforce rather than a replacement. In fact, their recent report predicts that by 2030, gen AI will infuse trillions into the global economy by facilitating the automation of nearly 70% of business activities across many occupations.
Organizations appear to agree with McKinsey’s assessment: Diverse groups ranging from Disney to the U.S. government are announcing the formation of generative AI task forces. Why? Organizations know it’s important to their current and future operations and success, but they’re also trying to understand how they can best harness the capabilities of this technology.
Simply put, many business leaders don’t know where to start with this groundbreaking technology and are trying to gather their smartest people to figure it out – in other words, forming generative AI task forces.
The rise of generative AI task forces
A generative AI task force is a cross-disciplined team brought together to focus on assessing how AI can help drive innovation, affect product quality and increase competitiveness amidst a rapidly changing landscape. They are meant to help organizations reap the promise of the technology while mitigating associated risks and challenges of software development and security. That includes establishing an ethical practice for the use of AI internally and externally, augmenting and redirecting the workforce to execute greater business impact.
Embedded in the AI industry for the last decade, Veritone has learned a thing or two about best practices when assessing what AI means for your organization. Here’s a look at the top things you should know as you form your task force.
Where to start with any AI task force
It starts with assembling a team that includes deep expertise in the type of AI you’re trying to implement, in order to ensure you’re making accurate decisions for your product or business case.
To identify a good starting point, juxtapose current processes against the potential adoption of AI. This is critical in discerning the extent of the technology’s capabilities, cost of implementation and trade-offs.
Next, every task force needs a foundation to anchor your approach. To build this out, start by asking the following questions of your current technology infrastructure:
What is/are the organization's current problem(s)?
What are the desired outcomes?
What would bring value, and what type of value is a priority?
Using the answers to these three questions as the foundation of your approach helps you you set clear short- and long-term objectives that align with the business's overarching goals and ensure stakeholder buy-in. With these goals as your guiding light, you can select a pilot project. Finding the most feasible project to provide an easy win rather than trying to do too much in the short term is crucial for long-term success.
For instance, one Veritone customer determined that they'd start with integrating gen AI into customer support processes. They automated responses for customer support sessions that they could choose from and modify as needed. This pilot project proved the efficacy of a concept without taking on too much.
After validation, companies can then scale their gen AI initiatives by identifying other business areas that could benefit from the technology. An iterative approach where you test, learn and refine as you progress will also help yield better results and ensure that your new framework will be formidable.
Best practices for integrating generative AI
To make sure your team is putting their best foot forward as they explore generative AI, here are some real-world-tested best practices to follow:
Continuous training and learning: AI continues to evolve rapidly. Invest in resources and courses to keep your team up-to-date with the latest skills and knowledge.
Collaboration and a feedback loop: Create regular check-ins with all stakeholders and a mechanism for feedback to create a system for continuous refinement.
Scalability and maintenance: Craft strategies for scalability, consistent maintenance and updates so that you build a robust AI framework.
Performance metrics: Measuring success becomes essential with AI. It's crucial to define KPIs and benchmarks specific to every project.
Ethical considerations: Ensuring transparency and data privacy, as well as addressing inherent biases in AI models is a non-negotiable prerequisite. In addition, establish ethical standards for the safe use of AI internally and externally, promoting workforce augmentation or refocus rather than replacement.
Feasibility study: Performing a thorough AI assessment, evaluating the technological infrastructure, and gauging the financial implications are all imperative.
Overall, remember that buzz often leads to unrealistic expectations. With generative AI and any AI implementation, ensure the team and stakeholders understand its capabilities clearly. Overestimating what it can do will inevitably extend the development lifecycle and potentially lead to negative end-user satisfaction.
Discovering what AI means for your business
Every organization will use AI differently. Understanding what AI means for your own business requires gathering the right people, establishing systems that yield tangible results and fostering accountability to ensure maximum ROI. Experts who have been working with AI for many years can also provide that accelerant you need to play catch up or leap ahead of your competition.
Veritone has been supporting various types of clients across multiple market sectors for the better part of the last decade, most recently helping the federal government implement new AI solutions. The sooner we can help an organization understand what AI means for their business, the better positioned they’ll be to win.
Want to learn more? Download this free AI task force guide.
Ryan Steelberg is CEO & President at Veritone.
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