Last year, communications companies began exploring generative AI (GenAI) by focusing on a limited set of use cases. This targeted approach allowed experimentation with specific technologies to test if the marketed benefits could be realized. It also allowed organizations to prepare necessary controls to safely use GenAI and related technology advancements more broadly.
Industry leaders are now making AI a strategic advantage across their organizations. A technology shift of this magnitude will require leadership alignment at the highest levels to support large-scale transformation. These changes will heavily impact how business is done, how teams are formed, and how customers interact with a company.
Lessons from recent history, including experiences in cloud adoption, offer a roadmap for this shift and key actions that will lead to the most significant impact in the shortest timeline. This technology shift can be successfully navigated by focusing on the following areas and applying lessons learned to drive meaningful progress.
Data: Multi-year and aligned data strategy
Data has shifted in importance to an organization. It is now the currency that enables new technologies such as GenAI and monetization opportunities. Most communications companies have significant first-party data, creating a strategic advantage.
During the shift to the cloud, simply moving an application from on-premises did not result in realizable benefits without further design. Similarly, a well-designed data strategy and corresponding architecture are needed to allow an organization to realize the benefits behind this AI technology shift. Merger and acquisition activities, silos within an organization, and a legacy application stack complicate most current-state data architectures. Modernization efforts are required to enable a long-term strategic advantage.
When designing this strategy, be intentional about incremental value aligned with company-wide priorities while paying down technical debt and aligning to a structure that enables future success.
People: Upskilling and future organizational design
Attracting, retaining, and training people with AI skill sets will create a large strategic advantage. It is also a significant challenge for communications companies competing with start-ups and the largest technology companies. Talent strategies will shift as AI reduces the number of people needed in some areas of the business while changing the skills needed in other areas. Rapidly changing technologies require business leaders with a greater technical aptitude and technology leaders who understand the business roadmap.
Managing the impact of change on the organization will be an important part of maintaining productivity. Prioritize a strategy of recruiting and retaining key leaders in the AI space while leveraging a partner strategy that focuses on building employee skill sets. Consulting partners can deliver near-term value and build the employee teams for the future. HR teams should be key resources in this approach with intentional team design and change management strategies.
Governance: Proceed at pace while protecting the business
As with the early days of cloud transformations, significant security, legal, and data privacy risks are challenges for AI transformation. No leader wants to see their company in the headlines for the wrong reasons, and, as we’ve seen recently, that is a real possibility. To move at pace, security, risk, and legal teams must partner in an aligned governance team. Key senior leaders involved must be committed to realizing AI's value at the fastest, safely executed pace.
Several strategies can support this approach. Prioritize use cases that provide significant value and have built-in human protections. A good example is agent-assist AI tools, which can lead to greater customer satisfaction and retention in addition to upsell opportunities. These tools provide enhanced customer service while addressing common AI concerns: comprehensive data security, data access strategies, and testing of technologies before driving direct customer interactions.
Prioritization: Rapid delivery of the most impactful use cases
With potential AI impact across dozens of areas and several hundred use cases, prioritizing focus will be critically important. For use cases with the largest and most timely impact, key senior leaders involved in the governance approach should communicate these as a company priority. Consider having a centralized top-level team supporting these efforts backed by meaningful partnerships to allow for scale.
As AI technologies are enabled across the organization, hold accountability and ensure the impact is measured by driving improvement in key metrics. This will strengthen prioritized use cases that measurably move the most important metrics.
Cost containment: Avoid unexpected large bills
Unforeseen costs were a challenge in early cloud adoption before companies realized how to optimize the technology. As AI is rolled out to an organization, there is the potential for similar challenges. I recently spoke with an executive whose team had incurred a bill for tens of thousands of dollars in AI compute costs for a question that could have been answered with a simple search.
Cost to value will be managed through architecture, design, and prioritization. Cost visibility to end users ahead of spend will be an important part of driving adoption. Include spending rules and authorization limits in the system design and create a model that allocates costs to the organizations that incur them. Differentiate between use cases that require an answer now and those where information could be provided on a longer timeline. In a recent example we ran at Slalom, a 97% cost savings was achieved from moving workloads to compute over time when an immediate answer was not required.
Benefit realization: Measure and capture savings for intentional investment
Annualized and siloed budgeting processes create the risk that realized savings are merged into ongoing budgets. This could lead to a sub-optimal allocation of investment into the broader business. If a prioritized use case results in significant savings from call center operations, for example, there needs to be an aligned process to decide if those funds are best reinvested into customer care or other priority areas of the business. For the largest impact, create a process to hold accountability to the original business case and a prioritized approach to reinvesting savings as they are realized.
Technology selection: Right technologies in a rapidly shifting market
As with any early-stage technology, there are leaders and new entrants into the field. The chosen partners within different areas of the business can make a material difference in results. Continuously assess available technologies and the skill sets required to see the targeted impact. Leverage internal experts, a broad set of technology-agnostic partners, and specialists to make selections for where to invest. Investment decisions should be paired with a talent strategy, resulting in employees owning key knowledge and skill sets over time.
Partner strategy: True partners to accelerate value realization
With the current and anticipated pace of change in the GenAI space, consistently building and updating teams' skill sets is crucial. Moving at pace will require a strong network of partners who can help with skill-building while supporting delivery in areas that provide near-term competitive advantages. Choose partners who have relevant experience in the chosen technologies and within the target industry. These partners should be committed to supporting employee teams while delivering immediate outcomes.
We’re experiencing one of the most exciting times in history, with GenAI and supporting technologies affecting change in nearly every area of business. Decisions made now by leaders in the communications industry will determine who leads and who struggles to take advantage.
