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How Channel Partners Can Help Enterprise Customers Get Better ROI from Gen AI DeploymentsHow Channel Partners Can Help Enterprise Customers Get Better ROI from Gen AI Deployments
The generative AI era continues to develop and advance rapidly, promising to create better and more efficient business processes.
4 Min Read
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The generative AI hype has cooled tremendously since it burst on the scene two years ago, with Gartner proclaiming it has slipped into the “trough of disillusionment." This may only be temporary as gen AI still holds great potential for enterprises, even as they “strive to scale,” according to a recent report from Deloitte. The firm’s survey found that two out of three organizations said they are increasing their investments in gen AI because they’ve seen strong early value. That said, many are still challenged to successfully scale that value, with nearly 70% of respondents reporting their organization has moved 30% or fewer of their gen AI experiments into production.
What does this mean for the channel companies seeking ways to leverage the new AI economy by broadening their portfolios and taking advantage of the opportunity AI and generative AI might present? While deployments may be occurring slowly – which is not unusual for large technology deployments by any means – channel companies should prepare for the gen AI era as it continues to develop and advance rapidly, promising to create better and more efficient business processes. Deloitte reports that 58% of survey respondents said they realized a more diverse range of most important benefits, such as increased innovation, improved products and services, or enhanced customer relationships.
Despite the lag on gen AI deployments, channel partners with expertise in AI and data life-cycle management can help their enterprise customers with pre-deployment processes, which will in turn help their own customers increase return on investment for their gen AI projects. Preparing company data is an important first step because it lays a strong foundation for generative AI deployments. Investing in ensuring data is accurate, relevant and up to date improves the effectiveness of generative AI models, which will result in higher ROI. Some of the gen AI data preparation services partners can provide include:
Data Inventory: Conducting a thorough inventory of existing data sources to identify where data is stored, its formats and its relevance to the intended AI applications.
Data Classification: AI algorithms can be used to automatically categorize data into predefined classes. This is particularly useful for large volumes of unstructured data, such as emails, documents and social media posts. It can also be used to classify data according to compliance requirements and security policies, such as the GDPR and HIPAA.
Data Quality Assessment: Evaluating the quality of data to check for accuracy, completeness, consistency and relevance, then cleansing it to remove duplicates, correct errors and fill gaps.
Data Life Cycle Management: Once companies start adopting the latest AI-enabled PCs, such as Microsoft’s Copilot + PCs, they’ll be collecting and storing more data than ever at the client level. Service providers can assist with data life-cycle management by using AI’s machine-learning algorithms to classify data into categories such as redundant, obsolete or trivial (ROT), then performing data erasure to permanently eradicate it.
Data Structuring: Organize unstructured data (like text, images or videos) into structured formats, if possible. This might involve tagging, categorizing or converting data into a standardized format.
Improving Gen AI Outcomes and ROI
In addition to preparing data properly pre-deployment, boosting gen AI return on investment (ROI) is dependent on a number of other factors, including alignment with business goals. Channel partners can play an important advisory role, helping to guide their customers on strategies to make generative AI deployments more seamless and trouble-free; for example, choosing the right applications in which AI can directly address specific business challenges, such as employing it to make customer service more efficient by automating responses.
Focusing on high-impact use cases that drive revenue growth or cost reduction is another way to increase gen AI deployment ROI. Use cases ranging from content generation, product design, predictive analytics or streamlining workflows are some that can make the biggest impact. Gen AI can significantly streamline processes like content creation, document review, customer support and other repetitive tasks. This can reduce labor costs and improve operational efficiency.
Channel partners can also collaborate with their customers to create clear gen AI ethical guidelines, guardrails and governance frameworks, as well as employee training programs to enable the effective use of AI tools. Regular employee training can accelerate adoption and improve the overall value of the technology.
Looking Ahead to the Gen AI Era Services Portfolio
Generative AI has the potential to be a game-changer for enterprises, and channel partners with expertise can guide their customers through the preparation, implementation and adoption phases to ensure AI applications deliver tangible benefits that meet business goals. By aligning AI with business objectives, continuously optimizing processes and fostering a culture of innovation and collaboration, companies can maximize the value of their gen AI investments with support from their trusted partners.
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