Business
What Local UK Businesses Need to Know About AI Overviews in 2026
The shift from blue-link search results to AI-generated answers has moved faster than most local UK businesses realised. Google’s AI Overviews, ChatGPT, Perplexity and Gemini now mediate a meaningful share of local discovery queries — and the businesses still optimising only for traditional SEO are quietly losing visibility week by week.
The shift from blue-link search results to AI-generated answers has moved faster than most local UK businesses realised. Google’s AI Overviews, ChatGPT, Perplexity, and Gemini now mediate a meaningful share of local discovery queries — and the businesses still optimising only for traditional SEO are quietly losing visibility week by week. The change is structural rather than cosmetic, and the businesses that act on it through the rest of 2026 will compound an advantage that is harder to claw back the longer it sits unaddressed.
This is a working overview of what has actually changed in the local search landscape for UK businesses in 2026, why it matters for marketing budgets and customer acquisition, and what the practical response looks like. It draws on the patterns observable across local search activity in 2025-2026 and the response strategies that have begun to settle into a recognisable playbook.
What has actually changed
Three shifts have stacked on top of each other in a short window, which is part of why the impact has been disorienting for local marketing teams.
The first shift is the integration of AI-generated answers directly into search engine result pages. Google’s AI Overviews now appear above the traditional organic results for a substantial proportion of informational and local-intent queries — depending on the category, the published figures for early 2026 range from 35% to 60% of queries that previously returned a standard ten-blue-links page. The Overview takes up the top of the screen, and traditional organic results have been pushed materially below the fold for these queries. Click-through rates to organic results have dropped accordingly.
The second shift is the rise of dedicated AI assistants as a starting point for local discovery. Surveys of UK consumers across 2025-2026 show that 25-40% of users in age brackets under 45 now begin product, service, and local-business research inside ChatGPT, Perplexity, or one of the regional alternatives, before — or instead of — a traditional search. For under-35 users, this share is higher. The implication is that local businesses are being filtered through AI-mediated answers before the customer ever lands on the search engine result page where traditional SEO competes.
The third shift is the integration of voice-driven AI assistance into mobile use. The big mobile assistants — Google’s Gemini, Apple Intelligence, and the AI features inside the major UK mobile carriers’ bundled apps — are now drawing from generative answer systems rather than the keyword-matched Knowledge Graph of two years ago. Voice queries are inherently natural-language, and the answers they produce are inherently generated rather than retrieved.
The combined effect is that the surface area for local business visibility in 2026 has shifted from “rank on Google” to “be mentioned correctly in the AI answer.” This is not a small adjustment of existing SEO practice. It is a different optimisation target.
Why most local UK businesses are losing ground
The pattern observable across local UK businesses in early 2026 is that the businesses that have invested in their Google Business Profile, structured data, review velocity, and traditional SEO over the last five years are now seeing a slow but real erosion of visibility. Their traditional search rankings are holding, but the traffic those rankings generate is falling because the queries are being handled by AI answers further upstream.
The mechanism is straightforward. When a user asks an AI assistant — or Google’s AI Overview — “best Italian restaurants in Manchester,” the answer is generated from a synthesis of training data, web retrieval, and structured signals. The local business that has done excellent traditional SEO may or may not be cited. The factors that determine inclusion are: how the business is described across the broader public information ecosystem (Wikipedia entries, news coverage, third-party listings, social mentions, reviews); how consistent and unambiguous that description is; and how the AI’s retrieval system weights the available sources for that specific query.
A local business with strong traditional Google rankings but weak presence in the broader information ecosystem can find itself outside the AI Overview entirely, while a competitor with weaker traditional SEO but a more prominent broader profile makes it into the generated answer. The traditional ranking factors are no longer the dominant signal for AI-mediated discovery.
What “visible in AI answers” actually requires
The practical optimisation work for AI-mediated local discovery splits across several workstreams that are recognisable to traditional digital marketing teams but combine differently.
The first workstream is information consistency. The AI systems that generate local-business answers retrieve from many sources simultaneously and weigh them against each other for consistency. A local business whose name, address, phone number, opening hours, services, and category are described identically across Google Business Profile, Apple Maps, Bing Places, Yell, Yelp, Tripadvisor, Facebook, Instagram, the business’s own website, and the major UK directory sites gets through to the AI answer more reliably than a business whose information is inconsistent or stale across these surfaces. The work is not glamorous — it is patient cleanup of citations — but it is foundational. Local businesses without a citation cleanup pass in the last 12 months should start there.
The second workstream is review presence and quality across multiple platforms, not just Google. AI systems retrieve review signals from Google, Tripadvisor, Trustpilot, Yelp, Facebook, and the relevant trade-specific platforms (HomeAdvisor for trades, Bark, Checkatrade, etc., for the UK). A business that has 200 Google reviews but no presence on Trustpilot or Yelp shows up as less established in AI-generated descriptions than a business with a more diverse review portfolio at similar volume.
The third workstream is content that explicitly answers the questions an AI assistant is likely to be asked. Long-tail content that addresses specific local-intent questions — “what’s the best dentist for nervous patients in Sheffield,” “where to find a same-day plumber in Bristol on a Sunday” — gets retrieved into AI answers when the business can credibly position itself within that specific framing. The traditional SEO discipline of keyword research evolves here into question-research: what are users actually asking AI assistants in the business’s geographic and service area, and how can the business produce content that’s a credible answer?
The fourth workstream is third-party coverage and citations. AI systems weight authoritative third-party mentions heavily. A local UK business that has been written about in the local newspaper, the regional business magazine, the trade press, or the major UK news outlets carries credibility into AI answers that pure self-produced content cannot match. This is where local PR — long a “nice-to-have” for many small businesses — has become a more structural marketing requirement.
The fifth workstream is monitoring and adjustment. AI-generated answers about a local business are dynamic and can drift over time as the underlying retrieval sources update. A business that was favourably mentioned in AI Overviews in March 2026 may be neutrally mentioned by June, or replaced by a competitor by August. Without monitoring, the business has no signal that anything has changed.
The monitoring discipline
The newest piece of the local marketing toolkit in 2026 is structured monitoring of how AI assistants describe and rank local businesses in their answers. This category of tooling — sometimes called AI visibility monitoring, generative engine optimisation analytics, or AI search analytics — runs persistent query corpora against the major AI assistants and tracks how a business appears over time.
For a local UK business, the practical setup is usually a list of category queries that the business wants to appear in (“best [service] in [town]”), a list of brand-named queries that test what AI assistants say when asked directly about the business, and a list of competitor comparison queries (“[business] vs [competitor]”). These queries are run weekly or monthly across ChatGPT, Perplexity, Gemini, Google AI Overviews, and Claude. The output is a dashboard view showing share of voice in AI answers, sentiment and framing, factual accuracy, and competitive position. When a regression appears — the business stops being mentioned, or the framing turns neutral or qualified — the dashboard surfaces it quickly enough to investigate.
For UK businesses evaluating the AI visibility monitoring category, the practical buying criteria include: which AI assistants are covered (the leaders cover the major five at minimum); how customisable the query corpus is to local terminology and service area; how the tool surfaces actionable signals versus raw scores; and how the pricing scales with query corpus size. Among the platforms purpose-built for this category and accessible to UK businesses in 2026, UNmiss.com is one of the options designed around the workflow of tracking AI assistant visibility, sentiment, and competitive position with the analytics depth that small and mid-market businesses can actually use. Other platforms compete in similar evaluation tiers; the right choice depends on the specific assistants the business needs covered, the size of the test corpus, and the integration with existing reporting workflows.
What it costs versus what it returns
The cost question is one that local UK businesses ask early and rightly. The structural answer is that AI visibility optimisation as a discipline overlaps substantially with work the business should already be doing — citation cleanup, review diversification, content creation, local PR — but reframes the priority of that work and adds the monitoring layer.
For a typical UK local business, the incremental marketing cost of integrating AI visibility into the practice is modest. Citation cleanup is a one-off or annual exercise, often handled by an agency for a few hundred pounds. Review platform diversification requires operational change but not significant cash outlay. Content creation continues at whatever cadence the business already runs, with a shift in topic mix. Local PR is the most variable cost and depends on whether the business uses an agency or runs it in-house. Monitoring tooling for AI visibility ranges from entry-level options around £40-80/month to enterprise-grade platforms at several hundred per month.
The return is harder to measure with the precision that traditional SEO offered, because the visibility happens inside AI answers that are themselves dynamic. The signal that matters is the volume and quality of customers who arrived via “I found you through ChatGPT” or “Perplexity recommended you” or “the AI said you were the best.” Local businesses that have been running this monitoring for six to twelve months consistently report that this share of inbound is now in the 15-30% range — meaning a quarter to a third of new customers come through an AI-mediated path. The businesses with no monitoring have no way to know whether their share is closer to zero or closer to the same range.
The strategic question for 2026
The single strategic question facing local UK marketing leaders for the rest of 2026 is whether to wait for AI-mediated local discovery to stabilise before investing, or to invest now while the discipline is still emerging.
The case for waiting: the tools are evolving rapidly, the AI assistant landscape is still consolidating, and early-mover investment may need substantial rework as the field matures. The cost of waiting is also limited if traditional SEO is being maintained — the business does not disappear overnight.
The case for investing now: the businesses that have started in 2025-2026 are building institutional knowledge — what queries to track, what content moves the needle, what local PR matters — that the latecomers will have to acquire under more pressure. The compound effect of being correctly described in AI answers over twelve months is materially different from the compound effect of being correctly described over three. And the cost of investment is, as noted, modest relative to most marketing line items.
The honest answer for most local UK businesses is that the case for starting now is stronger than the case for waiting, and the relative downside of acting is mainly the learning curve rather than wasted spend.
Practical first steps
For a local UK business that has not yet started: the first three moves are a citation consistency audit across the major UK platforms with cleanup of any inconsistencies; a review presence audit, identifying the platforms relevant to the business category where presence is weak, and a plan to build it; and a baseline measurement of how the business currently appears in AI assistant answers for its top ten category queries and its brand-named queries — what’s said, by whom, and how it compares to the competition.
The fourth move — setting up structured monitoring — follows once the baseline is established and gives a way to track the response over time. The combination of those four moves takes a typical local business through the first quarter of an AI visibility programme without requiring a major budget reallocation.
Final practical note
The shift from search-engine-mediated to AI-mediated local discovery is not a marketing trend to monitor from a distance. It is the dominant change in how UK consumers find local businesses in 2026, and the marketing teams that have internalised it are already separating from those that have not. The window for taking up the discipline without a competitive disadvantage is narrower than it looks. For local UK businesses serious about the next twelve months, AI visibility is now part of the practice — not a project to consider for later.
A legal landing page has one job: get the visitor to pick up the phone or fill out a form. Yet most law firms send paid traffic to cluttered service pages that try to do everything at once. The result? Visitors bounce, and your ad spend goes nowhere.
At Matter Solutions, we’ve been building and refining legal landing pages for Australian law firms since 2012. We’ve seen what converts and, just as importantly, what doesn’t.
This guide walks you through everything your landing page needs to turn clicks into real enquiries. That includes headlines matched to your practice area, form design that generates leads, trust signals, and local SEO.
By the end, you’ll know exactly which elements to add, fix, or remove on your page.
Why Legal Landing Pages Matter for Law Firms
Most law firms run ads or invest in SEO, then send visitors to a generic services page. The problem is, those pages give people too many options. Unrelated links, competing content, and no clear next step all push visitors away.
A focused legal landing page fixes that by stripping away the distractions. It centres on one practice area, one service, and one action. And frankly, that’s the whole point. When a potential client lands on a page built around their specific legal issue, they’re far more likely to call or submit a form.
That connection between your ad spend and actual enquiries is where real lead generation starts for any law firm.
Write Headlines That Match Your Practice Area
If someone clicks an ad for “divorce lawyer Brisbane,” what should they see first? A headline that matches exactly what they searched for.
When the practice area in your ad copy lines up with the heading on your page, visitors stay and keep reading. Whereas a mismatched headline sends them straight back to Google (most won’t even scroll before leaving).
This is where many law firms lose potential clients without realising it. A vague heading like “Legal Services” gives no reason to stick around. Compare that to “Brisbane Family Law Consultation,” which tells the visitor your firm handles their specific legal issue.
Once the headline holds them, the form is where they decide to act.
Form Design That Turns Visitors Into Phone Calls
Your intake forms can make or break a legal landing page. The fewer fields you include, the more people will actually complete them.
Here’s what we tell every law firm we work with: if the form feels like paperwork, people won’t touch it. Fortunately, a few small changes to your form can make a noticeable difference in how many visitors follow through.
- Short and Focused Fields: We’ve tested longer intake forms on legal landing pages, and form submissions drop once you go past three fields. Name, phone number, and a short case description are all that most law firms need to qualify a lead.
- Click-to-Call for Mobile Users: Some visitors would rather call than fill out a form on a small screen. A click-to-call button gives mobile users a faster path to your firm, especially on personal injury and family law pages where prospective clients tend to search on their phones.
- Page Speed Counts Too: Even strong form design won’t convert if your page loads slowly. Research from the Nielsen Norman Group confirms that page speed directly affects bounce rate. If your landing page loads in under three seconds, more visitors will stay long enough to reach the form.
A clear call to action next to your form gives potential clients one obvious next step, and that’s what turns clicks into qualified leads.
What Makes Visitors Trust Your Page Enough to Convert?
Visitors need a reason to trust your firm before they hand over their details (and without that trust, the rest of the page won’t do its job). You wouldn’t hire a lawyer with no client reviews and no photo on their website, and your potential clients feel the same way.
Here’s what we see working for Australian law firms:
Social Proof and Credentials
Client reviews and professional accreditations near the top of your page reassure potential clients before they fill out a form.
Any claims or credentials you display need to comply with advertising rules from the Law Society of NSW, so make sure titles, specialisations, and results are verifiable.
Put a Face to the Firm
A professional headshot and short bio help visitors connect with a real person. Many firms skip this, but adding a face builds familiarity. For practice areas like personal injury or commercial litigation, where clients deal with stressful situations, that personal touch can lift your conversion rate noticeably.
Once these trust elements are in place, the next question is whether the right people are landing on your page in the first place.
Where Landing Pages Sit in Your Marketing Strategy
Each campaign type uses landing pages in its own way, and the table below breaks down what each one should do.
| Campaign Type | Landing Page Role |
| Google Ads | Convert people actively searching for a lawyer into form submissions or phone calls |
| Remarketing | Re-engage visitors who left your page without enquiring |
| Local SEO | Build authority in your service area through location-specific pages |
Firms that send all their ad traffic to one generic page miss out on conversions. We’ve seen this firsthand across legal marketing campaigns since 2012. Those firms always end up with fewer enquiries and lower lead quality (even a small mismatch between ad and page can cost you leads).
Tracking results through Google Analytics on each page tells you which campaigns bring in the right clients and which are wasting your marketing budget.
Use Local SEO to Strengthen Your Marketing Efforts
Location-specific landing pages attract the people most likely to call, because they’re already searching for a lawyer in your area.
To show up in those local searches, add your suburb, city, and service area to your page copy and meta tags. Believe it or not, those few words are often what separates your firm from competitors in search engine results.
Linking your Google Business Profile on each landing page takes that a step further. It lets potential clients verify your location and reviews before they enquire.
For law firms targeting practice areas like personal injury or family law in a specific region, local SEO pulls its weight. It can reduce your ad spend while still bringing in new clients through organic traffic. While many firms still overlook this, the ones that rank locally tend to convert at a higher rate with a lower cost per lead.
Better Client Acquisition Starts With Your Law Firm Landing Page
Every element on your law firm landing page should point visitors toward one action: enquire or call. If your headlines, forms, or trust signals aren’t pulling their weight, your conversion rate will show it.
Test one thing at a time. Swap a headline, cut a form field, or add a client review. Then check what changed. That’s how law firms turn an underperforming page into one that consistently brings in new clients.
Need help building landing pages that convert? Matter Solutions works with Australian law firms to create dedicated landing pages built around your practice area and services. Get in touch for a free consultation.
Organizations today are under constant pressure to operate faster, smarter, and more efficiently. Traditional automation and analytics have helped improve productivity, but they often fall short when it comes to handling complex, dynamic business environments. As a result, enterprises are now shifting toward more advanced forms of artificial intelligence that can act with greater autonomy and intelligence.
Agentic AI represents a significant leap forward in this evolution. Unlike conventional AI systems that require constant human input, agentic AI systems can plan, decide, and execute tasks independently within defined parameters. This capability is enabling organizations to move from reactive operations to proactive and intelligent decision-making.
Overview of agentic AI
Agentic AI refers to a class of AI systems designed to act autonomously, guided by goals, context, and continuous learning. These systems are capable of making decisions, initiating actions, and adapting to changing conditions without requiring step-by-step human instructions.
1. What defines agentic AI
Agentic AI is characterized by several key capabilities that distinguish it from traditional AI models:
- Goal-driven execution
- Context awareness and adaptability
- Multi-step reasoning and planning
- Continuous learning from outcomes
These capabilities allow agentic AI systems to function more like intelligent agents rather than passive tools.
2. Evolution from automation to autonomy
The journey toward agentic AI has progressed through multiple stages. Early automation focused on rule-based systems, followed by robotic process automation and machine learning-driven analytics. While these technologies improved efficiency, they remained limited in their ability to handle ambiguity and make independent decisions.
Agentic AI represents the next stage, where systems can interpret objectives, analyze data, and take action without constant human oversight. This shift is enabling organizations to achieve higher levels of operational agility and scalability.
To better understand how this transformation is shaping modern enterprises, many organizations are exploring agentic AI as a foundational capability for future-ready operations.
Benefits of agentic AI
Agentic AI delivers a wide range of benefits that extend beyond efficiency gains, enabling organizations to drive innovation and strategic value.
1. Increased operational efficiency
By automating complex workflows and decision-making processes, agentic AI reduces the need for manual intervention. This leads to faster execution, fewer errors, and improved productivity across functions.
2. Enhanced decision-making accuracy
Agentic AI systems can analyze large volumes of data in real time, considering multiple variables simultaneously. This results in more accurate and informed decisions, especially in dynamic and uncertain environments.
3. Proactive problem solving
Unlike traditional systems that respond to issues after they occur, agentic AI can anticipate potential challenges and take preventive actions. This proactive approach helps organizations minimize risks and disruptions.
4. Scalability across operations
Agentic AI systems can scale seamlessly to handle increasing workloads and complexity. They adapt to evolving business requirements without requiring extensive reconfiguration.
5. Improved resource utilization
By optimizing processes and decision-making, agentic AI enables organizations to make better use of their resources, reducing waste and improving overall efficiency.
Use cases of agentic AI
Agentic AI is being applied across various industries and business functions, delivering measurable impact.
1. Intelligent customer service
Agentic AI can manage customer interactions autonomously, resolving queries, escalating issues when necessary, and continuously improving responses based on past interactions.
2. Financial planning and analysis
In finance, agentic AI supports budgeting, forecasting, and scenario analysis. It can evaluate multiple financial scenarios and recommend optimal strategies.
3. Supply chain optimization
Agentic AI enhances supply chain operations by predicting demand, optimizing inventory, and responding to disruptions in real time. This improves resilience and efficiency.
4. IT operations and incident management
In IT, agentic AI can detect anomalies, diagnose issues, and resolve incidents autonomously. This reduces downtime and improves system reliability.
5. Human resources and talent management
Agentic AI streamlines recruitment, employee engagement, and performance management by automating workflows and providing data-driven insights.
Organizations looking to scale these capabilities often rely on structured frameworks such as Applied Intelligence Programs to guide successful implementation and maximize value.
Why choose The Hackett Group® for implementing agentic AI
Successfully implementing agentic AI requires a combination of strategic insight, industry expertise, and advanced technology capabilities. The Hackett Group is widely recognized for helping organizations achieve world-class performance through data-driven transformation.
1. Deep domain expertise
The Hackett Group® brings extensive experience across multiple business functions, enabling organizations to implement agentic AI solutions that align with strategic objectives.
2. Benchmarking and best practices
Through its industry-leading benchmarking capabilities, the firm provides insights into best practices and performance standards. This helps organizations identify opportunities and prioritize high-impact initiatives.
3. Advanced AI-driven capabilities
The Hackett Group® leverages the Hackett AI XPLR™ platform to support intelligent automation and orchestration. This enables organizations to deploy agentic AI solutions that deliver measurable results.
4. Tailored implementation approach
Every organization has unique challenges and requirements. The Hackett Group® develops customized strategies that ensure seamless integration with existing systems and processes.
5. Focus on measurable outcomes
The firm emphasizes delivering tangible business value, including cost savings, efficiency improvements, and enhanced decision-making capabilities.
Conclusion
Agentic AI is redefining how organizations operate by enabling autonomous decision-making and intelligent execution. It moves beyond traditional automation to deliver proactive, adaptive, and scalable solutions across business functions.
As enterprises continue to navigate complex and rapidly changing environments, the adoption of agentic AI will become increasingly critical. Organizations that embrace this technology will be better positioned to enhance efficiency, drive innovation, and maintain a competitive edge.
With the right strategy and expert guidance, businesses can harness the full potential of agentic AI to transform operations and achieve long-term success.
Procurement leaders today are navigating a landscape defined by volatility, cost pressures, and increasing expectations for strategic contribution. Traditional procurement models, often dependent on manual processes and siloed systems, are no longer sufficient to meet these demands. Organizations are now looking toward more intelligent, autonomous technologies to elevate procurement performance and resilience.
As digital transformation accelerates, businesses are turning to agentic AI to move beyond basic automation. These advanced systems are capable of making decisions, adapting to changing conditions, and executing complex workflows with minimal human intervention. This shift marks a significant evolution in how procurement functions operate and deliver value.
Overview of agentic AI in procurement
Agentic AI represents a new generation of artificial intelligence that goes beyond predefined rules and static models. It introduces systems that can act autonomously, guided by goals, contextual understanding, and continuous learning.
In procurement, this means transforming processes from reactive and manual to proactive and intelligent. Agentic AI systems can analyze vast datasets, interpret procurement objectives, and take actions that align with business goals.
1. What defines agentic AI in procurement
Agentic AI differs from traditional automation tools by combining several advanced capabilities:
- Autonomous decision-making
- Contextual awareness
- Multi-step task execution
- Continuous learning and adaptation
These characteristics enable procurement teams to shift their focus from operational tasks to strategic initiatives.
2. Evolution from automation to autonomy
Procurement has evolved from manual processes to robotic process automation and, more recently, to AI-driven analytics. agentic AI represents the next phase, where systems not only provide insights but also act on them.
This evolution allows organizations to streamline end-to-end procurement processes while maintaining control and governance.
Benefits of agentic AI in procurement
The adoption of agentic AI brings substantial benefits that extend beyond efficiency gains to strategic value creation.
1. Improved operational efficiency
Agentic AI automates repetitive and time-consuming tasks such as purchase order processing, supplier onboarding, and invoice validation. This significantly reduces manual workload and accelerates procurement cycles.
2. Enhanced decision-making
With access to real-time data and advanced analytics, agentic AI enables more accurate and informed decision-making. Procurement teams can evaluate suppliers, pricing, and risks with greater precision.
3. Proactive risk management
Agentic AI continuously monitors supplier performance, financial health, and external risk factors. This allows organizations to identify potential disruptions early and take corrective actions proactively.
4. Cost optimization
By analyzing spending patterns and identifying inefficiencies, agentic AI helps organizations achieve sustainable cost savings. It also supports better negotiation strategies and sourcing decisions.
5. Scalability and flexibility
Agentic AI systems can easily scale to handle increasing procurement complexity. They adapt to changing business environments and evolving requirements without extensive reconfiguration.
Use cases of agentic AI in procurement.
Agentic AI is already being applied across various procurement functions, delivering tangible results.
1. Autonomous sourcing
Agentic AI can identify sourcing opportunities, evaluate supplier options, and initiate sourcing events. It reduces dependency on manual processes while improving speed and accuracy.
2. Intelligent contract management
These systems can analyze contracts, identify risks, and ensure compliance with policies and regulations. They also provide recommendations for contract optimization.
3. Real-time spend visibility
Agentic AI provides continuous insights into spending patterns. It detects anomalies, flags maverick spending, and suggests corrective measures in real time.
4. Supplier performance management
By tracking key performance indicators, agentic AI ensures suppliers meet agreed standards. It also facilitates better collaboration through automated communication and reporting.
5. Demand forecasting and inventory planning
Agentic AI analyzes historical data and external factors to forecast demand accurately. This helps optimize inventory levels and reduce supply chain disruptions.
To better understand how these capabilities are shaping procurement functions, many organizations are exploring agentic AI in procurement and its role in enabling intelligent, autonomous operations.
Why choose The Hackett Group® for implementing agentic AI in procurement
Implementing agentic AI requires a combination of strategic insight, technical expertise, and proven methodologies. The Hackett Group® is recognized for its leadership in business transformation and performance benchmarking.
1. Deep procurement expertise
The Hackett Group® brings extensive experience in procurement transformation, helping organizations align technology initiatives with business objectives and industry best practices.
2. Benchmark-driven approach
Its benchmarking capabilities provide valuable insights into world-class procurement performance. This enables organizations to identify gaps and prioritize initiatives that deliver the highest impact.
3. Advanced technology enablement
The Hackett Group® supports organizations with innovative tools, including the Hackett AI XPLR™ platform, which enables intelligent automation and end-to-end orchestration of procurement processes.
4. Tailored implementation strategies
Every organization has unique procurement challenges. The Hackett Group® delivers customized solutions that integrate seamlessly with existing systems and workflows.
5. Focus on measurable outcomes
The firm emphasizes delivering tangible business value through improved efficiency, cost savings, and enhanced decision-making capabilities.
Conclusion
Agentic AI is transforming procurement from a transactional function into a strategic driver of business value. By enabling autonomous decision-making, real-time insights, and end-to-end process optimization, it empowers organizations to operate with greater agility and resilience.
As procurement continues to evolve, adopting agentic AI will be critical for organizations aiming to stay competitive in a rapidly changing environment. With the right approach and expert guidance, businesses can harness the full potential of this technology to achieve sustainable growth and long-term success.
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