Overcoming the Challenges of Integrating Collaborative Intelligence into IT Workflows
Integrating Collaborative Intelligence into IT workflows promises increased productivity, efficiency, and quality, more rapid innovation, and a means of strengthening competitive position. However, realizing these prospective benefits has its challenges.
One of the biggest challenges is maintaining data privacy and security when building, deploying, and using these technologies. Meeting this challenge is crucial, but it has been written about extensively. This article focuses on three others:
Developing Talent and Strengthening Organizational Commitment
Scaling Solutions to Support Wide-Spread Adoption
Ensuring Continuous Improvement and Innovation
Developing Talent and Strengthening Organizational Commitment
The successful integration of CI into IT workflows requires that IT leaders:
Assess needs and current capabilities, then rapidly eliminate skills gaps, and
Reduce anxiety about and resistance to CI, driven by staff fears that these technologies will eventually replace them.
Talent Management
Successful development, deployment, and management of CI requires specific skills that may be lacking among internal resources. Bridging these skills gaps requires multiple tactics.
IT leaders should develop comprehensive and individualized training programs to upskill existing staff. The training should accommodate various levels of technical proficiency, ensuring IT staff are equipped with the necessary skills and mindsets to leverage CI effectively.
In instances where upskilling may not suffice, or immediate expertise is needed, hiring new staff (which is its own challenge) and collaborating with external experts can provide the needed infusion of new skills and experience. Additionally, fostering a culture of continuous learning encourages IT staff to actively engage with these emerging technologies, ensuring the organization’s IT capabilities evolve in step with technological advancements.
Strengthening Organizational Commitment
As I have written previously:
It’s called Collaborative Intelligence for a reason. The focus of all CI efforts, not just the initial few, should be on implementing workflows wherein intelligent machines support humans or work alongside them to achieve important business goals.
Certainly, some job types and skill sets will be less valuable or rendered obsolete because of CI, but the overall theme should always be that CI-centric workflows:
enhance human performance,
improve work life by offloading routine and uninteresting tasks to machines, or
make previously impossible tasks possible.
When just getting started, before there is any evidence within your company that this statement is true, you must be especially sensitive to this issue.
As with any change management effort, strong, visible, and sustained support from top management and the identification and empowerment of effective change champions go a long way to ensuring these changes are successful and sustainable.
Honest and straightforward communication is essential for allaying fears and overcoming resistance. IT leaders should be clear about the expected outcomes and engage staff in conversations about how CI can augment their work, emphasizing the positive impact on efficiency and job satisfaction, as well as the potential for new career opportunities.
Scaling Solutions to Support Wide-Spread Adoption
While the initial integration of CI into IT workflows can show promising results, scaling these solutions across the entire IT organization poses several challenges. These include designing scalable architectures, effectively managing resource allocation and ensuring consistent performance, and easily integrating with existing systems and other technologies.
Sidebar Scaling AI-Based Business and Customer Solutions
AI in general, and generative AI specifically, is a very hot topic among executive teams. In their efforts to provide answers to questions such as “What is our AI strategy?” or “What are we doing with AI?” IT organizations risk getting off on the wrong foot and developing business and customer applications that are not scalable or whose costs will grow rapidly when scaled.
We have observed companies whose initial forays into AI:
were not cloud-native, which hinders their ability to scale the applications and creates other problems,
were not architected for hybrid cloud, which risks rapid public cloud cost growth,
moved large datasets across their networks as they trained models rather than through data APIs, which strains network and data storage capacity and increases cloud costs, and
did other things that created technical or economic barriers that hindered their ability to scale their AI applications successfully.
This article focuses on IT workflows, but the challenges identified apply to AI systems in general, regardless of end-user category.
As you might guess, a scalable and flexible architecture for CI applications creates a foundation for success. Without one, the IT organization will struggle with technical and economic issues as these applications are scaled. Such architectures include hybrid cloud architectures for dynamic scalability and future flexibility, microservices architectures (including data APIs) to ensure agility, and architectures that enable AI models to be updated dynamically (there are several approaches).
CI applications may require significant computational resources and data storage capacity as they scale. IT leaders should assess current capacity and develop plans for increasing capacity, incorporating edge computing (where applicable), utilizing advanced caching techniques, and optimizing the AI models that are deployed to support the applications. They should also implement resource monitoring and management tools to optimize usage and costs, ensuring the infrastructure can support larger-scale AI operations without compromising performance.
Many CI applications will be compound systems that combine various technologies to automate entire workflows. As such, these systems may require interaction or integration with legacy systems, third-party services, other CI solutions, and other technologies. IT leaders should create development roadmaps and integration plans that aim to minimize disruption and maximize economic and strategic value. Such plans may require the establishment of data exchange protocols, interoperability standards, and other technical standards, as well as an articulation of the economic and strategic rationale for the sequencing of the rollouts.
Ensuring Continuous Improvement and Innovation
After the initial deployments, CI applications risk becoming stagnant and failing to keep pace with changes to third-party models and APIs, other advancements in the field, or shifting organizational needs.
Keeping up with this rapidly evolving field is a full-time job. IT leaders should create a “CI Innovation Team” tasked with monitoring the latest developments in collaborative technologies, assessing their applicability to the organization, exploring new ways to apply CI within the organization, and regularly reviewing and updating models and workflows.
This team should also establish formal or informal relationships with academic institutions and companies that are researching or developing technologies of interest to the organization. These collaborations can provide early access to new research, tools, and methodologies, ensuring the organization fully leverages the potential of collaborative intelligence.