Google has steadily moved towards zero click searches over the past few years, with features such as instant answers and knowledge panels giving users the info they are looking for directly on the search engine results page (SERP) with no clickthrough required. The introduction of AI Overviews is an evolution of that trend, further reducing the need for users to leave the SERP.

AI Overviews – generative AI-produced summaries that appear at the top of search results – mean users can often find the answers they need without clicking through to a website. While this may offer a more frictionless experience for the user, it presents new challenges for advertisers trying to make the most of their digital ad budgets. This trend is reshaping user behaviour and requires us to rethink how we measure visibility and performance across organic and paid search.

Traditional metrics are no longer enough

The metrics we traditionally used to measure search success now tell only small parts of the story. With clickthrough rates declining, we need new metrics to better understand visibility and conversion impact in a world of AI overviews and zero-click searches. It is also becoming more challenging to model attribution, when a user may first encounter a brand through an AI-generated answer, and then later converts through another channel.

Marketers need to adjust their approach to maintain presence and visibility within a more complex search experience. As a starting point, optimising for conversions (leads or sales) is more important than ever. In addition, keyword strategies should evolve to accommodate the reality that there are fewer clicks to go around. Marketers should focus on transactional, high-intent keywords found in the queries where users are closer to converting and AI Overviews are less likely to satisfy fully.

It is also necessary to fine-tune our strategies to balance cost-efficiency with visibility in increasingly competitive spaces. That does not mean going back to fighting for impression share through expensive bidding wars. Instead, it is about making smarter use of SERP real estate by taking advantage of assets such as sitelinks, callouts and structured snippets.

The changes demand that we re-evaluate many pillars of our digital advertising and marketing strategies. Even more now than before, we need to support paid search with brand-building campaigns across social, programmatic and other channels to lift user awareness and brand searches. First-party data and diversified channel strategies can really help brands to preserve visibility and performance in this landscape.

Unlocking the opportunities of AI Max

Finally, marketers should also explore the opportunities that Google has opened with AI Max for Search, a new feature that enhances keyword reach, ad asset generation and landing page selection using AI. It offers expanded reach into new intent and query types, using broad-match and “keywordless” technologies to serve ads for queries you did not explicitly target.

Meaning that when AI Overviews answer informational queries, advertisers can participate in the query-set to capture conversion-intention traffic. AI Max also optimises assets by generating or adapting ad text, headlines and descriptions according to landing pages, keywords and ad assets. This helps ad messaging to rise above the noise. Enhanced controls (such as locations of interest and brand controls) and expanded reporting of search terms and assets, meanwhile, help to optimise ads in response to changing user behaviours such as declining clicks.

AI Overviews do not solve all the challenges we face. Google is testing ads within AI Overviews via standard search and shopping campaigns, but there is no guarantee that an ad will appear within the AI Overview box after AI Max is activated. What’s more, the core challenge of zero-click remains in play. While AI Max can help a brand capture new or deeper-intent queries, it may well still be necessary to rethink search metrics and strategy. If a brand’s budget and bidding only target generic top-of-funnel keywords, it may lose visibility or efficiency. As such, it is important to also bid on conversion-intent and transactional keywords.

Here is a quick-start guide for using AI Max in the context of AI Overviews

• Enable AI Max for your Search campaigns and monitor new queries closely.
• Shift your keyword strategy to place more emphasis on conversion-intent and high-transaction keywords. Also use AI Max to reach less obvious queries.
• Focus on metrics beyond clicks. Measure impression‐share, ad visibility in emerging placements, conversions (not just CTRs) and monitor how your ads perform when AI Overviews are present.
• Use the enhanced reporting features available with AI Max. Review search term reports, asset performance and query expansion logs to understand how AI Max is performing.
• Ensure you have a solid data capture strategy (first-party, CRM, offline conversions) to make the most of AI Max and shifting user behaviour.
• Experiment and optimise. Because search is changing fast with AI Overviews and AI Mode, you will want to keep testing ad creative, landing pages, bidding strategy, query coverage and attribution methods to see what works best for your brand.

A bridge between search campaigns and evolving user intent

AI Overviews represent one of the most fundamental shifts we have seen in search for many years. It is difficult to predict right now how this space will evolve over the next couple of years, but it seems likely that many of our old approaches and metrics will fall by the wayside. As the world of search evolves, using AI Max enables us to progress keyword strategies from focusing on keywords and clicks to optimising for intent, engagement and conversion across multiple query types. Brands that start adjusting their strategies today will be best placed to capture high-intent engagement as search continues to evolve.

AI is not a future concept; it’s a powerful tool shaping how businesses operate today. The key to unlocking its potential lies in a thoughtful, strategic approach that moves beyond hype and focuses on tangible outcomes. By applying AI to their most impactful processes, organisations can drive meaningful improvements across the enterprise.

This is particularly relevant in South Africa, where new research from Dell Technologies shows that South African businesses are increasingly viewing AI as a strategic priority. The global study, which surveyed 2,850 business and IT decision-makers, of which 50 were from South Africa, found that 92% of South African companies now view it as a ‘key part’ of their business strategy. Additionally, 32% of South African organisations report seeing tangible productivity and financial returns from initial AI investments.

At Dell Technologies, we see AI as a vehicle for human progress. Our journey has provided a clear blueprint for turning AI vision into reality. This involves asking the right questions, establishing a solid data foundation, and implementing solutions that deliver real business value. Let’s explore how you can chart your own course for successful AI transformation.

Charting your course: A top-down and bottom-up approach

A successful AI strategy starts with introspection. Before diving into technology, you must understand what makes your organisation unique.

• What makes us special? Identify your core differentiators.
• What problem are we trying to solve? Define the specific challenges you want to address.
• What specific process are we changing with AI? Pinpoint the exact operational areas for improvement.

This top-down approach ensures your AI initiatives are directly tied to your business goals. It’s about being actionable and strategic from the very beginning.

Simultaneously, a bottom-up approach is necessary to identify common patterns across your organisation. Not every use case requires a unique tool; by identifying these patterns, South African companies can develop versatile capabilities that serve multiple functions, creating efficiency and preventing the need to create a bespoke snowflake for every new project.

Prioritising AI projects for maximum impact

With countless potential AI applications, prioritisation is critical. We recommend evaluating use cases against two core criteria: business value and feasibility.

Business value measures how a project will contribute to your bottom line. Will it make your team, products or processes:

• More effective?
• Better in quality?
• Faster to execute?
• Cheaper to operate?

Feasibility assesses the practical and technical viability of a project. This involves evaluating your readiness across key areas:

• Data: Is the necessary data available, accessible, and of high quality?
• AI model: Do you have the right model, or can you build or acquire it?
• Process: Can the existing process be adapted for AI integration?
• People: Do your teams have the skills to implement and manage the solution?
• Platform: Is your underlying technology infrastructure ready to support AI?

By plotting potential projects on a matrix of high/low business value and high/low feasibility, you can quickly identify the initiatives that promise the greatest return on investment and are most likely to succeed. High-value, high-feasibility projects are your clear starting point.

The foundation of AI: Modern data management

Data is the lifeblood of any AI system. Without a solid data foundation, even the most advanced models will falter. Establishing this foundation is a multi-step process.

• Data discovery: Begin by uncovering all relevant data sources within your organisation. The goal is to identify datasets that can be paired together to provide rich context for your AI models. Think about broad reuse, treating your data as a product that can serve many applications.
• Data preparation: This crucial phase involves cleaning and sanitising data to ensure its quality. It also includes managing data access, classification, and security to maintain governance and compliance, no matter where the data resides.
• Implementation: With clean, well-organised data, you can move to implementation. This can range from using pre-trained models for inference to more complex tasks like model augmentation with Retrieval Augmented Generation (RAG), fine-tuning, or training a new model from scratch.

Dell’s AI journey: From process to platform

Our own AI transformation followed this same path. We started by identifying what makes Dell special; in our case, that’s: our go-to-market engine, our world-class services, our market-leading products, and our supply chain.

From there, we determined which processes would benefit most from AI as well as what capabilities were needed.

• Go to market: To improve sales meeting preparation, we developed a RAG-based chatbot to provide product and solution content, and advanced search/research.
• Services: To deliver more efficient and effective support, we built a hybrid AI and RAG-based chatbot that supports our service professionals with next-best action recommendations.
• Product development: To accelerate innovation, we implemented coding assistance tools to enable our software developers to go faster.
• Supply chain: To create more predictive systems, we leveraged a hybrid AI and agentic system for enhanced supply chain intelligence.

This structured approach, moving from differentiators to processes and finally to specific projects, ensures that every AI initiative is purposeful and aligned with our core mission.

Lessons learned on the path to AI transformation

Our journey has been one of continuous learning and adaptation. We’ve discovered several key lessons that can help guide any organisation embarking on AI transformation.

1. Evaluate projects holistically: An AI project’s success depends on more than just the technology. Evaluate it against your broader business strategy and its technical viability, not just its fit within an isolated AI strategy.
2. Focus on core capabilities: Projects will evolve, but the underlying patterns and required capabilities often remain the same. Understand the core functions you need, and you will probably be able to support many outcomes with a few AI platforms.
3. Embrace ecosystem partners: Enterprise AI projects can easily become complex, unique ‘snowflakes’ that are difficult to scale. Lean on ecosystem partners which bring expertise and help you standardise and scale your solutions.
4. Accept platform evolution: Technology platforms have short shelf lives. Embrace a ‘two-year rule,’ knowing that you will need to re-evaluate and adapt your platforms to keep pace with innovation.

By turning vision into a structured, actionable plan, you can harness the power of AI to not only solve problems but also to drive your organisation forward. The AI era is here, and with the right strategy, it offers endless opportunities for progress.

About the Innovation Catalyst Research
This research was conducted in June 2025 by independent research firm Vanson Bourne on behalf of Dell Technologies. A total of 2,850 business and IT decision-makers were surveyed worldwide, including 100 in South Africa. For the complete findings of this year’s Dell Technologies Innovation Catalyst Research, visit this Link.

In the tech sector, 2025 was all about AI awareness and experimentation, the end of the hype cycle and a shift toward cautious, pragmatic decision making. 2026 will be the year of AI execution, efficiency and tangible return on investment.

This is according to ICT industry leaders from Jem HR, Euphoria Telecom, Accelera Digital Group, Go Rentals, Qwerti, Dispose-IT and CommsCloud.

2025: The year reality hit

For many, the past year served as a wake-up call on the speed of technological change. Daniel Fabre, marketing lead at Jem HR, says the big story of 2025 was the realisation of just how real the AI wave is. While large enterprises struggled to turn awareness into operational change, agile organisations treated AI like a muscle to be built.

“The companies that won in 2025 were the ones that took on the challenge of adoption and built the capability to learn, unlearn and relearn faster than everyone else,” says Fabre.

This sentiment is echoed by Cliff de Wit, chief innovation officer at Accelera Digital Group (ADG), who believes the market has entered a phase of discernment. Companies stopped throwing capital at broad experimentation and started demanding that technology solve real problems this year, he says

“What really stood out in 2025 was the definitive end of the hype cycle,” adds De Wit. “It wasn’t enough to just have an AI strategy anymore. Businesses began demanding that these technologies solve actual problems rather than just serving as novelty additions to the tech stack.”

The outsourcing shift

For mid-tier businesses specifically, the sheer volume of change in 2025 became overwhelming. Craig Freer, director at Qwerti, says technology began moving faster than internal teams could realistically manage.

“2025 was the year where it simply became too much for the mid-tier business,” says Freer. “Companies were trying to manage hardware lifecycles, software sprawl, security layers and compliance, all while the cost structures and features of these systems kept shifting.”

He predicts that this pressure will drive a massive shift toward outsourcing among mid-size businesses in 2026. As cyber threats become smarter and AI-driven, businesses will look for trusted partners who can offer resilience and security maturity without the need to hire more staff.

“Cybersecurity has become central to business continuity,” Freer adds. “We expect more organisations to adopt outsourced models because they offer scalable IT costs rather than fixed overheads. For many, it comes down to staying agile, secure and confident that their technology is being managed in their best interests.”

Financial caution and hardware cycles

This period was also marked by financial caution. Evan Berger, director at Go Rentals, says tight credit and a volatile exchange rate pushed many businesses to delay capital investments.

“Across our customer base, the strongest theme was agility,” says Berger. “New ventures, unpredictable pipelines and AI-driven shifts made companies far more reluctant to lock themselves into long-term commitments.”

Berger believes that refresh cycles will continue to shorten as collaboration tools and AI workloads increase their resource demands, making older hardware obsolete faster.

The great clean-out

This rapid obsolescence has created a secondary challenge: what to do with the old equipment. Clayton Heldsinger, director at Dispose-IT, says 2025 was the year many organisations finally confronted the mountains of old hardware sitting in their storerooms.

“With Windows 10 support ending and heavier AI workloads arriving, upgrades became unavoidable,” Heldsinger says. “2026 is shaping up to be a major clean-out year. Companies have pushed refresh cycles as far as they could, and the performance gap between old devices and modern workloads is now too large to ignore.”

Heldsinger adds that mid-tier businesses are increasingly focused on the data security risks and value recovery linked to their disposals, while larger organisations have begun shifting slightly from recycling toward circularity as part of broader ESG efforts.

2026 trend: The efficiency trap

Looking ahead, a major concern is that the rush for efficiency might damage customer relationships. Nic Laschinger, technology director at Euphoria Telecom, warns that 2026 might be remembered as the year customer experience metrics drop.

“I expect that in the rush to do ‘what we can’ in terms of implementing AI and other communication channels, many businesses will neglect the key elements of how this affects customers’ experiences,” says Laschinger.

He argues that while the trend of multichannel engagement will continue, most businesses have a reporting blind spot that prevents them from managing multiple channels effectively.

2026 trend: Specificity over generic AI

To avoid these pitfalls, businesses will move away from generic tools. De Wit predicts a significant shift toward specific, intelligent agents trained on an organisation’s in-house content.

“Organisations are realising that standard vendor tooling, which relies on generic data scraped from the wider internet, has a ceiling,” explains De Wit. “To reach the next level of capability, businesses will start building and training agents on their own organisational content.”

2026 trend: The human element

Technology will also need to serve the workforce more effectively, particularly deskless workers.

“AI implementation will shift from a buzzword to a key performance indicator,” says Fabre. “Mobile assistants, predictive scheduling and dynamic workflow tools that actually help workers do their jobs better, not just faster, will begin to gain traction.”

2026 trend: Convergence of AI and IoT

Finally, the operational backbone of business is set to transform. Peter Walsh, managing director at CommsCloud, sees the convergence of AI and the Internet of Things (IoT) reshaping entire industries like logistics and manufacturing.

“2026 will be the year organisations expect their technology stacks to self-optimise, from autonomous failover networks to AI-assisted decision making built into operational workflows,” says Walsh.

He emphasises that cross-border interoperability will become a strategic priority as African supply chains become more interconnected.

“2026 will be defined by the convergence of AI and IoT as the backbone of operational efficiency,” concludes Walsh. “Businesses across Africa will prioritise real-time visibility, automation and cross-border continuity as they navigate an increasingly complex environment.”